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The myGrid User Guide
Document Class:User documentationRevision:1.2For myGrid version:0.6Date: SAVEDATE \@ "yyyy-MM-dd" \* MERGEFORMAT 2004-11-23Pages: NUMPAGES \* MERGEFORMAT 85Abstract:A practical guide to performing e-science experiments in myGrid Document Information
Table of Contents
TOC \o "1-3" \h \z \u HYPERLINK \l "_Toc88640393" 0 Document Information PAGEREF _Toc88640393 \h 2
HYPERLINK \l "_Toc88640394" 0.1 Table of Contents PAGEREF _Toc88640394 \h 2
HYPERLINK \l "_Toc88640395" 0.2 Document History PAGEREF _Toc88640395 \h 3
HYPERLINK \l "_Toc88640396" 0.3 Forecast Changes PAGEREF _Toc88640396 \h 3
HYPERLINK \l "_Toc88640397" 0.4 Contacting myGrid PAGEREF _Toc88640397 \h 3
HYPERLINK \l "_Toc88640398" 1 Introduction PAGEREF _Toc88640398 \h 4
HYPERLINK \l "_Toc88640399" 1.1 Future developments PAGEREF _Toc88640399 \h 4
HYPERLINK \l "_Toc88640400" 1.2 Assumptions PAGEREF _Toc88640400 \h 4
HYPERLINK \l "_Toc88640401" 1.3 Acknowledgements PAGEREF _Toc88640401 \h 4
HYPERLINK \l "_Toc88640402" 2 An overview of myGrid PAGEREF _Toc88640402 \h 6
HYPERLINK \l "_Toc88640403" 2.1 Introduction PAGEREF _Toc88640403 \h 6
HYPERLINK \l "_Toc88640404" 2.1.1 The Taverna e-science workbench PAGEREF _Toc88640404 \h 7
HYPERLINK \l "_Toc88640405" 2.1.2 The myGrid Information Repository (MIR) PAGEREF _Toc88640405 \h 7
HYPERLINK \l "_Toc88640406" 2.1.3 Workflow Enactment PAGEREF _Toc88640406 \h 8
HYPERLINK \l "_Toc88640407" 2.1.4 Returning Data PAGEREF _Toc88640407 \h 9
HYPERLINK \l "_Toc88640408" 2.1.5 Life Science Identifiers PAGEREF _Toc88640408 \h 11
HYPERLINK \l "_Toc88640409" 2.1.6 Publication of myGrid-Generated Results PAGEREF _Toc88640409 \h 13
HYPERLINK \l "_Toc88640410" 2.2 Advantages of Workflow Technologies PAGEREF _Toc88640410 \h 13
HYPERLINK \l "_Toc88640411" 3 The Taverna e-science workbench PAGEREF _Toc88640411 \h 14
HYPERLINK \l "_Toc88640412" Workbench Overview PAGEREF _Toc88640412 \h 14
HYPERLINK \l "_Toc88640413" 3.1 Getting started with Taverna PAGEREF _Toc88640413 \h 14
HYPERLINK \l "_Toc88640414" 3.1.1 Installation PAGEREF _Toc88640414 \h 14
HYPERLINK \l "_Toc88640415" 3.1.2 Configuration PAGEREF _Toc88640415 \h 15
HYPERLINK \l "_Toc88640416" 3.1.3 Running Taverna PAGEREF _Toc88640416 \h 17
HYPERLINK \l "_Toc88640417" 3.1.4 Enacting a predefined workflow PAGEREF _Toc88640417 \h 17
HYPERLINK \l "_Toc88640418" 3.1.5 Creating a (very) simple workflow PAGEREF _Toc88640418 \h 18
HYPERLINK \l "_Toc88640419" 3.2 Workbench Windows in Detail PAGEREF _Toc88640419 \h 20
HYPERLINK \l "_Toc88640420" 3.2.1 Advanced Model Explorer PAGEREF _Toc88640420 \h 20
HYPERLINK \l "_Toc88640421" 3.2.2 Workflow Diagram PAGEREF _Toc88640421 \h 37
HYPERLINK \l "_Toc88640422" 3.2.3 Available Services PAGEREF _Toc88640422 \h 43
HYPERLINK \l "_Toc88640423" 3.2.4 Enactor Launch Panel PAGEREF _Toc88640423 \h 51
HYPERLINK \l "_Toc88640424" 3.2.5 Enactment Status Panel PAGEREF _Toc88640424 \h 53
HYPERLINK \l "_Toc88640425" 3.3 Browsing the myGrid Information Repository PAGEREF _Toc88640425 \h 66
HYPERLINK \l "_Toc88640426" 4 The myGrid information model PAGEREF _Toc88640426 \h 71
HYPERLINK \l "_Toc88640427" 4.1 People and Organisations PAGEREF _Toc88640427 \h 71
HYPERLINK \l "_Toc88640428" 4.2 The e-Science Experiments PAGEREF _Toc88640428 \h 72
HYPERLINK \l "_Toc88640429" 4.3 Experiment and Data Provenance PAGEREF _Toc88640429 \h 73
HYPERLINK \l "_Toc88640430" 4.4 Operations PAGEREF _Toc88640430 \h 73
HYPERLINK \l "_Toc88640431" 5 Life Science Identifiers PAGEREF _Toc88640431 \h 75
HYPERLINK \l "_Toc88640432" 5.1 What is an LSID? PAGEREF _Toc88640432 \h 75
HYPERLINK \l "_Toc88640433" 5.2 Use of LSIDs in myGrid PAGEREF _Toc88640433 \h 75
HYPERLINK \l "_Toc88640434" 5.2.1 The Practicalities PAGEREF _Toc88640434 \h 76
HYPERLINK \l "_Toc88640435" 5.2.2 myGrid LSID components PAGEREF _Toc88640435 \h 78
HYPERLINK \l "_Toc88640436" 6 Gathering knowledge from workflows PAGEREF _Toc88640436 \h 80
HYPERLINK \l "_Toc88640437" 6.1 Installing the plug-ins PAGEREF _Toc88640437 \h 80
HYPERLINK \l "_Toc88640438" 6.2 Adding semantic information PAGEREF _Toc88640438 \h 81
HYPERLINK \l "_Toc88640439" 6.2.1 Browsing Provenance PAGEREF _Toc88640439 \h 83
HYPERLINK \l "_Toc88640440" 7 Useful Definitions PAGEREF _Toc88640440 \h 85
Document History
RevisionDescription of change1.1Initial issue of document skeleton1.2First full text versionForecast Changes
None forecast.
Contacting myGrid
Developments, news and recent publications in myGrid can be found on the myGrid project pages at HYPERLINK "http://www.mygrid.org.uk" http://www.mygrid.org.uk
Downloads of all myGrid components can be found at HYPERLINK "http://www.mygrid.org.uk/downloads" http://www.mygrid.org.uk/downloads
Authors: Nick Sharman, Katy Wolstencroft, Tom Oinn, Kevin Glover, Nedim Alpdemir, Chris Wroe.
Introduction
This document describes the myGrid e-science toolkit for bioinformaticians. myGrid supports the scientific process of experimental investigation, evidence accumulation and result assimilation; supports the scientists use of the communitys information; and directly supports scientific collaboration at the core of research, allowing dynamic groupings to tackle emergent research problems. myGrid presents the necessary infrastructure within a Web Service environment as an e-Science Workbench that actively supports the scientific lifecycle. In particular,
myGrid supports the definition and enactment of e-science experiments as workflows orchestrating e-science services and databases. Experiments can be re-enacted with new data and modified as the e-scientists understanding of the problem space evolves
myGrid manages experimental data and results in an information repository that also contains provenance metadata, tying data and results to the workflows and services used to deliver them
myGrid manages semantic metadata that captures the scientists understanding of the experiments meaning
The myGrid e-science workbench, known as Taverna, supports:
The construction, modification and enactment of workflows, accessing a large and extensible set of services and databases
During workflow enactment, it automatically populates
The myGrid information repository with results and provenance metadata
The semantic metadata store, KAVE, with application-specific knowledge
Direct exploration and update of the myGrid information repository
Future developments
Future releases of the myGrid e-science toolkit will support the following capabilities:
Discovery of workflows, services and databases from a registry using semantic annotations
Construction of application-specific portals that provide a simpler interface for biologists (rather than bioinformaticians) to enact workflows and manage their data, results and semantic metadata
High-level orchestration of the e-science process
Assumptions
This document assumes that its readers are familiar with starting and interacting with windows-based applications. Example workflows will involve bioinformatics services and databases, so some bioinformatics experience will be useful in understanding them.
Copyright & Acknowledgements
The myGrid project is funded by the UK Engineering and Physical Sciences Research Council and The myGrid project is funded by the UK Engineering and Physical Sciences Research Council and its partners are:
The European Bioinformatics Institute of the European Molecular Biology Laboratory ( HYPERLINK "http://www.ebi.ac.uk/" http://www.ebi.ac.uk/)
The University of Manchester, School of Computer Science ( HYPERLINK "http://www.cs.man.ac.uk/" http://www.cs.man.ac.uk/)
The University of Newcastle-upon-Tyne, School of Computing Science ( HYPERLINK "http://www.cs.ncl.ac.uk/" http://www.cs.ncl.ac.uk/)
The University of Nottingham, School of Computer Science and Information Technology ( HYPERLINK "http://www.cs.nott.ac.uk/" http://www.cs.nott.ac.uk/)
The University of Sheffield, Department of Computer Science ( HYPERLINK "http://www.dcs.shef.ac.uk/" http://www.dcs.shef.ac.uk/)
The University of Southampton, School of Electronics and Computer Science ( HYPERLINK "http://www.ecs.soton.ac.uk/" http://www.ecs.soton.ac.uk/) and IT Innovation Centre ( HYPERLINK "http://www.it-innovation.soton.ac.uk/" http://www.it-innovation.soton.ac.uk/)
With support from:
IBM UK ( HYPERLINK "http://www.ibm.com/uk" http://www.ibm.com/uk)
Sun Microsystems ( HYPERLINK "http://www.sun.com" http://www.sun.com)
AstraZeneca ( HYPERLINK "http://www.astrazeneca.com/" http://www.astrazeneca.com/)
GlaxoSmithKline ( HYPERLINK "http://www.gsk.com/index-flash.htm" http://www.gsk.com/index-flash.htm)
Merck KGaA ( HYPERLINK "http://www.merck.com" http://www.merck.com)
Epistemics ( HYPERLINK "http://www.epistemics.com" http://www.epistemics.com)
GeneticXchange ( HYPERLINK "http://www.geneticxchange.com" http://www.geneticxchange.com)
LION BioScience (HYPERLINK http://www.lionbioscience.com http://www.lionbioscience.com)
Network Inference ( HYPERLINK "http://www.networkinference.com/" http://www.networkinference.com/)
An overview of myGrid
Introduction
myGrid is a collection of services enabling the high level integration of biological data and applications. Each component contributes to a system that allows e-scientists to perform complex in silico experiments across distributed bioinformatics resources.
The architecture and interaction of myGrid components serves two distinct purposes. The first is to provide an environment where e-science experimental design and execution can be structured using workflows and distributed query processing. The second is to provide an environment where e-science best practice can be adhered to by recording data provenance alongside biological data and defining mechanisms for personalisation.
Figure 2.1 shows the myGrid components and their integration into the overall myGrid architecture. The following pages describe these components in greater detail and give an overview of how users interact with them when using myGrid. Some components require user input, whist others work behind the scenes.
Figure 2.1 The Integration and interaction of myGrid components
The Taverna e-science workbench
The main user interface to myGrid is the HYPERLINK \l "_The_Taverna_e-science_workbench" Taverna e-science workbench. Taverna provides a language and software tools to enable the design and implementation of workflows. In a bioinformatics context, a workflow is the entire process of collecting relevant data, performing any number of analyses over the data and extracting biological results. Often, in bioinformatics the result of one experiment can form the input values of the next. Designing workflows in Taverna allows services (or bioinformatics analyses) to be scheduled to run in series. If results are not dependent upon one another, services can be designed to run concurrently, allowing for faster, more efficient processing.
Figure 2.2 Service selections from remote and local machines
Workflows are designed in Taverna by selecting services and inserting them into a workflow diagram. Services are any bioinformatics applications that are available as web services. More and more applications are being provided as web services, but legacy applications, either command-line tools, or simple web interfaces, can also be transformed into web services using wrapping tools, such as HYPERLINK "http://industry.ebi.ac.uk/soaplab" Soaplab and HYPERLINK "http://industry.ebi.ac.uk/soaplab/Gowlab.html" Gowlab respectively. This functionality means that any bioinformatics experiment that can be conducted elsewhere, regardless of the number of different processes or data sources required, can be run as a workflow in Taverna.
The myGrid Information Repository (MIR)
In myGrid, any data produced can be recorded. This can either be biological data from previous experiments or details of experimental designs and the enactments of those experiments. Therefore, users may wish to search this data for workflows that have already been designed and implemented instead of designing new workflows. Additionally, users may wish to retrieve the results of previous experiments for comparison with new results.
The MIR can be searched using the HYPERLINK \l "_Browsing_the_myGrid_Information Rep" MIR Browser, which is a plug-in to the Taverna e-science workbench. The plug-in provides a user interface for the repository, but it also enables new results to be passed back to the MIR as and when experiments are conducted.
The MIR structure is dependent on the underlying HYPERLINK \l "_The_myGrid_information_model" information model (IM). The IM is the central organisational structure in myGrid. It defines the relationships and representations of the different aspects of an e-science process.
Figure 2.3 Browsing stored data and previous results
Workflow Enactment
Workflows are implemented using the HYPERLINK "http://freefluo.sourceforge.net/" Freefluo enactor engine, which is downloaded as an integral component of the Taverna workbench, but has wider uses in other e-science applications.
Freefluo accepts workflow definitions and input data from Taverna and coordinates the scheduling of workflow services.
Figure 2.4 Freefluo, the workflow enactment engine
As a service is invoked, a new event is generated in Freefluo to signify this change. Similarly, as services produce intermediate or final output data, events are also generated.
Figure 2.5 Service invocation by Freefluo, the workflow enactment engine
Returning Data
While workflows are running, progress can be monitored with the enactor invocation viewer in Taverna. Services are shown as scheduled, invoking, or complete. If a service is unavailable and the process fails, this is also shown.
Reporting the progress of workflow invocations provides greater control for myGrid users. Some workflows may take several hours, or several days to run. If a critical service is unavailable, an alternative service could be substituted. This can be specified in the workflow design, or implemented by the user if a failure occurs. Services can also be marked as critical. If a critical service fails, the workflow is aborted automatically.
Figure 2.6 Reporting progress during workflow enactment
The progress of workflows can be monitored because of the event generation in the workflow enactor engine. Other myGrid components can also respond to the changes in events in Freefluo. Data and metadata can be captured in this manner by the MIR and KAVE (Knowledge Assertions for Verifying Experiments) via plug-ins to the enactor.
Figure 2.7 Storing data and metadata at intermediate and final stages of workflow invocations
The myGrid Data Stores
In silico experiments, as with laboratory experiments, must follow the scientific method in order to produce meaningful results. If an experiment cannot be repeated and verified by others, or the methodology cannot be justified, any results obtained cannot be considered conclusive.
In laboratory science, recording detailed accounts of methods and results is common practice, but in e-science, the necessity for such activities has been largely overlooked. In myGrid this problem is addressed with the storage of metadata. Non-biological data is collected alongside biological results.
There are two data storage facilities in myGrid, the MIR and KAVE (Knowledge Assertions for Verifying Experiments). The MIR, as previously discussed, stores any biological data produced in the context of the experiment it was created in. Therefore, the MIR additionally stores data on the person who performed the experiment, the workflows used with their specific input parameters and the date the experiment was conducted. KAVE stores semantic provenance information, which can be highly specific for a particular subject area and can be linked to domain-specific ontologies. In the myGrid context, one subject area is bioinformatics sequence analysis. KAVE can store the semantic data types of data objects, for example, nucleotide_sequence_data or protein_sequence_data. KAVE can also store semantic links between data types and processes, for example, if a protein_sequence_data type is the input for a workflow involving the bioinformatics_application blastp, a specific type of bioinformatics_report is always produced.
Life Science Identifiers
Designing and implementing projects in myGrid can involve obtaining data from a large number of different sources for analysis. These sources all have different systems for assigning unique identifiers to their data and retrieving data using these unique IDs. When retrieving data from a variety of sources at any one time, this can present problems. Life Science identifiers (LSIDs) were developed to overcome these problems.
LSIDs are a mechanism for unifying the process of data retrieval by unifying the identifiers, regardless of their origin. An LSID has the following structure:
urn:lsid:AuthorityID:NamespaceID:ObjectID:RevisionID
urn:lsid - signifies that the identifier is an LSID.
AuthorityID the internet domain of the organisation that assigned the LSID
NameSpaceID the name of the resource that contains the data
ObjectID the unique name of a data object in the namespace resource
RevisionID an optional parameter to identify different versions of the same data object.
myGrid has adopted the LSID convention for all data in the system. Biological data, from external databases, or from newly generated results, are identified with LSIDs. Non-biological data, such as, workflows and experimental designs, are also identified with LSIDs.
Adopting the LSID convention means that all data references can be passed between myGrid components in the same way, allowing easy access to and from the MIR and the metadata store.
Figure 2.8 Retrieving stored data using LSIDs for publications and information sharing
2.1.6 Publication of myGrid-Generated Results
The automated storage of contextual experimental data and semantic metadata in myGrid facilitates the simple transfers of data into methods and results sections suitable for biological publications. It also promotes the easy sharing of data between collaborators at the same or distributed organisations. The use of LSIDs affords an efficient and reliable method for retrieving relevant data and tracking versions of data objects.
Advantages of Workflow Technologies
myGrid provides distinct advantages over traditional bioinformatics analyses in a number of different ways
Experimental Design Bioinformatics applications from distributed locations can be linked together to design complex e-science experiments without the need to install local implementations of the tools and datasets, or to write code to integrate applications.
Experiment Invocation The ability to schedule services to run in series allows outputs from one analysis to be automatically fed into the input of the next, reducing the need for manual transfer or manual formatting of data. This serves two purposes; the first is providing more freedom to the user. If each stage of the analysis is invoked automatically, the user does not have to physically watch the experiment running, leaving them free to do other experiments (workflow experiments or others). The second is reducing data formatting errors. The larger the data-set produced by an experiment, the more difficult it is to format manually.
Component Management Using distributed resources, from the source organisations that supply them, means that users do not need to invest time in keeping resources up to date. If the same analyses were run using local implementations of applications and data-sets, these would have to be updated as often as updates occur in the source organisation.
Invocation Performance Some bioinformatics applications involve complex algorithms requiring intensive computational resources. Whilst the computing facilities available to the average user have greatly improved over recent years, many do not have access to local supercomputer facilities. Invoking these computationally-intensive applications through myGrid means that users have access to the supercomputer facilities at the source organisations, allowing for much quicker and more efficient experiments.
The purpose of this user guide is to provide an introduction to the myGrid architecture and to the way in which different myGrid components interact. Some components are not visible to the user directly, whilst others have their own user interfaces, or are plug-ins to the Taverna e-science workbench.
For further information on any of the components, please see the HYPERLINK "http://www.mygrid.org.uk/" myGrid website
The Taverna e-science workbench
Workbench Overview
The Taverna Workbench allows users to construct complex analysis workflows from components located on both remote and local machines, run these workflows on their own data and visualise the results. To support this core functionality it also allows various operations on the components themselves such as discovery and description and the selection of personalised libraries of components previously discovered to be useful to a particular application.
Getting started with Taverna
This section will walk through the process of installing and configuring Taverna then creating and enacting a trivial sequence fetcher workflow. While this is obviously a task which could (and arguably should) be performed in a simpler fashion it should serve as a reasonable introduction to the basic features of Taverna. This section assumes that you have a modern version of the Windows operating system (Windows 2000, Windows XP), there may be some minor differences with other systems such as linux and Solaris although the basics should be identical.
Installation
Both Windows and Linux installations require a modern (version 1.4) Java installation to run. Windows machines will not have this by default; you will need to obtain it from HYPERLINK "http://java.sun.com/" http://java.sun.com. Linux users are more likely to already have this but should check - the procedure for Java installation on Linux is out of the bounds of this document.
Windows
On Windows machines installation should be extremely simple - open the downloaded archive (you probably already did this to read this file!) and unpack it to somewhere on your file system. The precise location doesn't matter but Taverna will not run from within a compressed folder - if you are using Windows XP and have not installed any software such as WinZip then Windows will appear to open the zip file just as if it were a normal folder on the file system, however, this is not the case and the differences will cause Taverna to fail.
Linux
Linux installation is very slightly more complex as you will have to install one extra piece of software first. Taverna makes use of the 'dot' application from the GraphViz package from AT&T research. This package (which is well worth installing for other reasons) is available as an RPM from the AT&T site here: HYPERLINK "http://www.research.att.com/sw/tools/graphviz/download.html" http://www.research.att.com/sw/tools/graphviz/download.html. You may already have it, a simple check is to go to a command line and try typing 'dot', if the terminal just sits there then you probably have GraphViz already, if it shows an error indicating that the application is not found then you'll need to install it. Installation of the RPM will almost certainly require root access so you may need to ask your systems administrator to install it for you.
Once you have GraphViz installed then you can simply unzip the Taverna archive to somewhere sensible, you might also want to sym-link to the runme.sh file from e.g. /usr/bin/taverna for ease of access.
Configuration
The basic configuration for the workbench is held in the file 'mygrid.properties' inside the 'conf' folder of your unzipped installation. For the majority of users the default settings will work just fine, but there are a few exceptions - check whether these apply to your installation and adapt the properties appropriately. For Windows users you'll want to edit this file in WordPad rather than NotePad - it uses UNIX style line endings so NotePad will get all the lines mixed up, WordPad handles this just fine. Linux users can use Emacs or your favorite text editor. Sections in blue boxes below are excerpts from the configuration file.
Proxy configuration
If you are behind an HTTP proxy, and there's a reasonable chance that you are, you'll need to locate the section in mygrid.properties relating to proxy settings. There are instructions above these parameters - if you are unsure of what values to use here then you'll have to ask your systems administrator. The values here will be the same as those used in your normal web browser; Taverna currently doesn't support configuration using a proxy.pac style mechanism but you should be able to find the individual parameters required without too much difficulty.
# PROXY CONFIGURATION (user editable)
#--------------------------------------------------------------------
# Use the properties below if your machine accesses the internet
# via a proxy server. Uncomment them by removing the leading '#'
# and then modify to suit your installation.
#
# Some configurations appear to need to edit the http.* versions.
#--------------------------------------------------------------------
# Configuration for Nottingham CS
# proxySet = true
# proxyHost = 128.243.220.41
# proxyPort = 3128
#
# http.proxyHost = 128.243.220.41
# http.proxyPort = 3128
#--------------------------------------------------------------------
# The http.nonProxyHosts property defines a set of host name or IP
# address patterns for which the proxy should not be used. For example,
# it would make sense for the local host to be included in this,
# either by name (localhost) or IP (127.0.0.1). Host patterns in this
# list are seperated with the '|' character as shown below.
#--------------------------------------------------------------------
# http.nonProxyHosts = localhost|127.0.0.1|*.mydomain.com
#--------------------------------------------------------------------
Default services
Taverna can be configured to automatically load libraries of components into the service selection panel when it starts up. This is worth looking into if you are, for example, running a course which involves Taverna and you want your students to see your sample services when the workbench starts up. For now I suggest leaving the values as specified, although as they contact the network to find additional information on startup commenting out these sections might speed up the load times.
# DEFAULT SERVICES (user editable)
#--------------------------------------------------------------------
# The service selection panel component can be populated with extra
# web services and soaplab installations using this configuration
# section. In order to enable this, uncomment and edit the two
# properties to suit.
# Note that the default services load in a new thread, so may not
# appear immediately within the service panel. If any errors occur
# during the load, the stack trace is displayed in the system
# console. This is not ideal, we'll have better reporting in the
# next release.
# Multiple instances of each (soaplab or wsdl) may be specified by
# including all endpoints, seperated with the ',' character as shown
# below.
# Trailing whitespace is not allowed in these URLs! If you're having
# problems getting this to work then check there isn't a space at the
# end of the line.
#--------------------------------------------------------------------
taverna.defaultsoaplab = \
http://industry.ebi.ac.uk/soap/soaplab,\
http://www.ebi.ac.uk/collab/mygrid/service4/soap/services
taverna.defaultwsdl = \
http://www.ebi.ac.uk/collab/mygrid/service1/goviz/GoViz.jws?wsdl,\
http://www.ebi.ac.uk/xembl/XEMBL.wsdl
taverna.defaultbiomoby = \
http://mobycentral.cbr.nrc.ca/cgi-bin/MOBY05/mobycentral.pl
taverna.defaultweb = \
http://www.ebi.ac.uk/~tmo/workflows/
#--------------------------------------------------------------------
Image types and the mysterious 'Error 4'
There are reports of some linux installations showing a mysterious error '4'. This is, as far as we can tell a problem with java handling PNG format images - we use this for the workflow diagram. If you are having this problem you might like to try un-commenting one of the 'taverna.scufldiagram.imagetype' lines to use GIF or JPEG coding instead. This seems to be a problem unique to Linux but this is worth trying on any installation showing this error.
# WORKFLOW DIAGRAM IMAGE TYPE (user editable)
#--------------------------------------------------------------------
# By default the workflow diagram uses an internal image in PNG
# format. Some platforms (win2000 and linux) have exhibited problems
# with this which seem to be rectified by changing the format to one
# of 'gif' or 'jpg'
# If you're seeing messages with the extremely helpful text of '4' or
# similar and no diagram appearing in the workbench you might like to
# try uncommenting one of the options below
#--------------------------------------------------------------------
# taverna.scufldiagram.imagetype = gif
# taverna.scufldiagram.imagetype = jpg
#--------------------------------------------------------------------
Running Taverna
You should be able to run an installed Taverna system by invoking the 'runme.bat' or 'runme.sh' files, the former being for Windows and the latter for Linux. If this fails to work you should re-check the installation, Java version and GraphViz version (linux only), and carefully read through the section on configuration. If you have done this and the system still doesn't work please let us know via the mailing list (link to mailto#####).
Enacting a predefined workflow
As a first quick demonstration of Taverna you can load one of the example workflows. Find the Advanced Model Explorer and click on the 'Load' icon in its toolbar. You should be presented with a file chooser dialog box, the exact appearance of which will be determined by your operating system.
Loading a workflow
Workflow examples are stored in the 'examples' subdirectory of your Taverna installation, so use the file browser to navigate to this directory and select the 'ShowGeneOntologyContext.xml' workflow. This should result in the Advanced Model Explorer and Workflow Diagram panels being populated with the workflow defined in this file. There may be a short delay while Taverna contacts the network to get more information about the resources the workflow uses - if the delay is a very long one followed by an error you probably need to check the configuration of your proxy settings, see the configuration section for more details.
Enacting the current workflow
Aside from creating and editing, the most useful thing to be able to do with a workflow is to run it. Go to the 'Tools and Workflow Invocation' menu in the top level toolbar, and select the 'Run Workflow' option at the end of the list. You should be presented with a new window - this is the place to enter the starting data, if any, on which the workflow is to run. In this case the workflow has a single input called 'termID', click on it to see some more information on what the workflow expects.
In this case, the workflow description has helpfully included an example of a sensible input value, the string 'GO:0007601' (this term corresponds to 'vision' in the Gene Ontology - http://www.geneontology.org). As you have just selected the 'termID' input you should notice that the 'New Input' and 'New List' options are now available at the top of the window, click on the 'New Input' to create a single data input to the workflow. In panel that has just appeared you should replace the 'Some input data goes here' with the input term, so try entering 'GO:0007601' as the description suggests (but without the quote marks). Once you have entered this input data you can press the 'Run Workflow' button at the bottom of this window to actually start the workflow engine off on the supplied input.
A new window should appear with the title 'Enactor invocation'. This window shows you the progress of the workflow and also the results on completion. Additionally, if anything goes wrong with the enactment you can interrogate other parts of this display to find out exactly what has happened (but we won't cover this here). You should see a table appear initially containing a list of the component operations along with their status, this will update until the workflow has finished and you can inspect the inputs and outputs of any given part of the workflow by selecting the process you're interested in and viewing the intermediate values from the bottom half of the window. Once the workflow completes you'll be jumped into the results view.
Browsing results
This particular workflow has a single output called 'graphical', which contains a single piece of data. Select the only available item in the list on the left hand side of the window and you should see a diagram of a subgraph of the Gene Ontology appear in the right hand panel - this is your workflow result (the diagram can be quite large, you might have to scroll around to see it). Congratulations, if this worked then you have installed and configured Taverna correctly and run your first workflow.
If you were to want to save the workflow (there's not much point as we haven't changed anything) you would select the 'Save' option from the Advanced Model Explorer and save the document in the usual fashion for your operating system. You probably don't want to do this for now as there are no changes.
To finish, and prepare for the next part of the introduction, you'll want to reset the workflow. Click on the red 'Reset' icon and confirm that you do really want to reset the workflow. This just resets the definition, your results from the workflow invocation are still there; you can release these by simply closing the Enactor invocation window.
Creating a (very) simple workflow
Workflow inputs and outputs
First things first - the workflow needs to have an input, in this case the id of a sequence to fetch. You can create a new workflow input by right clicking on the 'Workflow inputs' node in the Advanced Model Explorer and selecting 'Create new input'. This will then ask you for a name for the input, you can change this later but for now use 'sequenceID'. Click on 'OK' and you should see your new input appear in the Advanced Model Explorer and Workflow Diagram windows.
Similarly, the workflow will need an output. Follow an equivalent process but this time clicking on the 'Workflow outputs' node and using the name 'sequence' to create an output. Again, you should see the output appear in the two windows.
A single sequence fetch component
Now that the workflow has an input and output it needs something to fit between to actually do the work. If you're familiar with the EMBOSS tool suite you might be aware that there's a program called 'seqret' which retrieves a sequence from an ID. Fortunately, the default services available in Taverna include a system called Soaplab, from the EBI, which contains all EMBOSS tools including seqret. Go to the 'Available Services' window and either scroll down until you find it or, more sensibly, enter 'seqret' into the search box at the top and hit return. You should see the tree narrow to show you two matching operations, 'seqretsplit' and 'seqret'.
To add the operation to the workflow you can drag the 'seqret' service from the 'Available Services' window into the 'Advanced Model Explorer' window, dropping the service into the empty space on the right of this window. You should hopefully see a new entry under the 'Processors' node called 'seqret' with an array of other child items hanging off it - these are the available inputs and outputs to and from the newly created process.
Connecting everything together
You should see the workflow input, processor and workflow output in the 'Workflow Diagram' window, but they're not linked together yet. To link them, you need to right click on the 'sequenceID' node in the 'Advanced Model Explorer' and select the 'seqret' child menu (under the 'Link sequenceID to...' item). This child menu will show you all the inputs in the seqret tool to which you can link your workflow input. The exact details of all these inputs will depend on the processor, in this case you want to link the sequenceID workflow input to the 'sequence_usa' processor input. Select the 'sequence_usa' from the child menu and you should see the link appear in the workflow diagram.
The remaining link in the workflow is from the output of the processor to the workflow output. Conceptually we always link from the source of the information to its destination, so you'll need to expand the 'seqret' node in the Advanced Model Explorer (if it isn't already expanded) and scroll down to see the available outputs, denoted by purple circles with outgoing arrows. There are two outputs from this processor, 'report' and 'outseq', the one we want to use is the 'outseq'. Similarly to the first link, right click on the 'outseq' node and follow the 'Workflow outputs' child menu. There is only one workflow output so select it and you should observe the now completed workflow in the 'Workflow Diagram' window.
Describing the input
You can add descriptive information to the workflow inputs - this information is useful to guide potential users of your new workflow. Select the 'sequenceID' node by left clicking on it in the 'Advanced Model Explorer'. You should observe a tab at the top of the window with a purple icon and the text 'Metadata for 'sequenceID''. Select this tab by clicking on it.
This is the metadata editor. For now we're only going to look at the free text description, the ontology and mime type tabs are too complex for this introduction. Select the 'Description' tab, enter some description of the input (i.e. a quick paragraph explaining that this input should hold the sequence ID to fetch and perhaps an example value - 'swallid:ops2_*' is a reasonable one), click the 'Update' button when you're done and jump back to the 'Workflow' tab to get back to the default view of the workflow.
Enacting the workflow
You should now be able to run the workflow just as in the previous section, try entering your example 'swallid:ops2_*' as the input (without the quotes) and you should see the status display (briefly) followed by the results which, all being well, should contain the fetched sequences in fasta format. If this worked then great, you've created a simple workflow from scratch and enacted it.
Workbench Windows in Detail
Advanced Model Explorer
The Advanced Model Explorer (AME) is the primary editing component within Taverna. Through it you can load, save and edit any property of a workflow. As a result of this it is quite complex, this section will explain in detail exactly how to use each of its functions.
Toolbar
The toolbar for the AME consists of six items:
Load
Load from web
Save
New subworkflow
Offline checkbox
Reset
The 'Load' and 'Load from web' buttons are reasonably self explanatory. When selected they present the user with either a file dialogue (in the first case) or a text input where the user can enter a URL. The file or URL specified should be a workflow definition in XScufl format. Workflows are loaded alongside any existing content, if a clean load is desired the reset function should be activated prior to any load operation.
'Save' allows the user to write the current workflow to disk in XML XScufl format - this definition can then be loaded at a later date using the 'Load' function described above. A standard save dialogue is shown, the appearance of which is platform dependent.
'New subworkflow' creates a blank child workflow within the primary workflow for this instance of the AME. The child workflow appears in the processor list as normal but has neither inputs nor outputs.
'Offline' - this option allows the workflow to be set to offline mode. In offline mode Taverna attempts a 'best efforts' at all operations without requiring network connectivity to the workflow's resources. This can be useful for dealing with workflows where some processors do not load due to the underlying service no longer existing as well as the more obvious case of working on the train, at conferences etc where some display functionality is required without the network being connected. For obvious reasons it is generally impossible to actually enact a workflow in offline mode, and processors will only appear to have inputs and outputs which are explicitly referenced (i.e. have a data connection to another output or input) as the normal behaviour of fetching port information from the network is disabled. If a workflow is loaded in offline mode and the checkbox is cleared the entire workflow is reloaded in online mode - if this fails for some reason offline mode is reasserted and the workflow reloaded in offline mode again.
'Reset' clears the entire workflow definition, requiring an additional confirmation from the user before doing so. All entities in the workflow including descriptions and metadata are reset to their initial blank states. This does not affect the service panel, only the state of the current workflow.
Entity Table
The main section of the AME shows a tabular view of the entities within the workflow. This includes all processors, workflow inputs and outputs, data connections and coordination links. Some items may be expanded to show properties of child items such as alternate processors and input and output ports on individual processors. In general the right click menu on an entity will expose at least 'delete from workflow' functionality and may provide more.
Workflow Metadata
By selecting the 'Workflow model' node in the AME a new tab 'Workflow metadata' becomes available:
Selecting this tab shows the workflow metadata display:
This panel allows the user to enter an author name, descriptive title and longer textual description. The Life Science Identifier (LSID) field is not manually editable, instead the 'New' button will connect to whatever LSID authority is configured in the mygrid.properties file and ask for a new LSID suitable for a workflow definition. This then provides a globally unique identifier for the workflow definition (as opposed to workflow instance, each instance of a workflow within the enactor also has a unique ID but this isn't it). The original AME view can be restored by selecting the 'Workflow' tab.
Workflow Inputs and Outputs
The 'Workflow inputs' and 'Workflow outputs' nodes are used to create, respectively, workflow inputs and outputs. A new input or output can be created by right clicking on the text and selecting 'Create new input...' or the obvious corresponding option for outputs. This then displays a text dialogue into which the name of the new workflow input our output should be entered. Once created, the name can be edited by double clicking on the name and editing in the normal way.
Once a workflow input or output has been created it can be further specified by selecting the input or output node in the tree and opening the newly available tab, 'Metadata for [input or output name]'. This tab has three sub-tabs for different parts of the input description. The first of these allows the user to select a term from an ontology (this is in a fairly early stage at present) which describes the data type for this port. The example below shows a port being annotated with information specifying that it is of type 'Gene ontology Term ID:
Additional tabs allow entry of a free text description (for workflow inputs this is shown in the workflow launch panel so is a good place to put information which users of the workflow would need to populate the inputs) and tagging of the input or output with MIME types. The latter is particularly important for workflow outputs as it drives the selection of renderers within the result browser. The description panel is self explanatory, the MIME type entry is shown below :
The large button at the bottom right of the panel will completely clear the MIME type list. Outputs and inputs in Taverna can have multiple MIME types - these are regarded as hints to the rendering system rather than being proscriptive computer science style formal types. To add a new MIME type the user enters the type into the text box and hits the return key, it then appears in the type list.
Connecting Workflow Inputs to Processors
Workflow inputs may be linked to the inputs of processors within the workflow (or directly to workflow outputs) by right clicking on the particular workflow input and selecting the target from the drop down menu, either selecting a workflow output directly or selecting a processor then the workflow input within that processor. Conceptually links in Taverna always link from an output to an input, this is slightly confusing in the sense that workflow inputs are, as far as the workflow itself is concerned, outputs - they produce data. In a similar vein workflow outputs are in fact inputs: they consume data and manifest t to the outside world in the form of workflow results. The image below shows a workflow input being linked to the input of a processor:
(Slight digression) - Interesting MIME types for Taverna
The following is a list of the MIME types currently recognized by renderers in Taverna's result browser, this list is useful to know when annotating workflow outputs for obvious reasons, which is why it appears here rather than elsewhere.
text/plain=Plain Text
text/xml=XML Text
text/html=HTML Text
text/rtf=Rich Text Format
text/x-graphviz=Graphviz Dot File
image/png=PNG Image
image/jpeg=JPEG Image
image/gif=GIF Image
application/zip=Zip File
chemical/x-swissprot=SWISSPROT Flat File
chemical/x-embl-dl-nucleotide=EMBL Flat File
chemical/x-ppd=PPD File
chemical/seq-aa-genpept=Genpept Protein
chemical/seq-na-genbank=Genbank Nucleotide
At the present time the behaviour for textual types is to render them either as plain text or as an XML tree for text/xml, a rendered graphical view for text/x-graphviz and Java's best attempt at rich text or HTML for the respective types. The various 'chemical/...' types are currently rendered using SeqVISTA, another project we integrated recently, this allows a very attractive view of sequence data in particular. All image types are rendered as images, which is pretty much what you'd expect.
MIME types are also used to attempt to guess the appropriate file extension when storing results to a file system tree, this is currently the only reason to tag an output as application/zip, Taverna has no other understanding of zip files.
Processors (Top level node)
The processors in Taverna are the primary components of the workflow. They are the entities responsible for both representing and ultimately invoking the tasks from which the workflow is comprised. In addition to allowing the manipulation of the individual processors this section also exposes a summary of all the remote resources used by the workflow, this can be shown by selecting the 'Processors' node and opening the 'Remote resource usage' tab that appears:
This display summarizes remote resources by host, giving a quick overview on which tools from which institutes the workflow uses. This report can be saved in HTML form and also includes the description, author, title and LSID if present defined in the workflow metadata. The intention is that between this and the workflow diagram much of the description you'd need for a paper or poster is auto generated.
Individual Processor Nodes
Each processor in the workflow is represented by a processor node in the AME. These nodes can be expanded to show the inputs and outputs of that particular processor, selected to enable a tab containing metadata about the processor or double clicked on to edit the name of the processor.
In this case the processor is one based on Soaplab (indicated by the yellow icon colour) and has six inputs and two outputs. The input and output names are shown as children of the processor, with different icons for inputs and outputs. Selecting the 'Metadata for 'RunDot'' tab shows the following information:
The metadata panel contains a free text description (which may or may not be populated from the service by default) and an editor for the iteration strategy. More details of the iteration system can be found in the iteration section, it will not be discussed further here. Return to the standard AME view by selecting the 'Workflow' tab.
Creating New Processors
Processors are instances of components available in the 'Available Services' panel, and are created from there. This also applies to alternates (see later in this section). See the Available Services section for more information.
Connecting Processors
The port nodes under each processor node are used to connect the outputs of processors either to the inputs of other processors or to workflow outputs. This is achieved by right clicking on the output port node and selecting either a workflow output (as shown in the case below) or by selecting a processor then the appropriate input within that processor.
Customizing Processors (where available)
Some processor types may expose additional editors. For example, the processor type which emits a string constant has a very simple editor which allows the user to change the string it emits, the beanshell processor has a more sophisticated one providing a lightweight script editing environment and the nested workflow processor uses another instance of the AME as its editor. These editors, if available for the selected processor, are accessed from the right click menu on the processor node itself. The exact behaviour depends on the processor type:
Defining Temporal Constraints
It is sometimes required to prevent a processor running until some condition is true. The most common (and in fact the only currently supported) condition is that processor A should only run when processor B has completed successfully. This constraint is created by right clicking on the processor node and selecting a target processor from the 'Coordinate from' menu. The semantics of this constraint are that the processor first selected will only start running once the processor selected from the menu has completed. It's worth noting that in the case where processor A is feeding data to processor B in some way this temporal condition is implicit, an explicit statement of the condition is only required where there is no other form of linkage between the processors. In the example below the processor 'GODBGetNameByID' will only run when the processor 'LLIDFromGi' has completed:
Configuring Basic Fault Tolerance
Each processor in the workflow has its own settings for fault tolerance. These settings include the ability to retry after failures and to fall back to an alternative or set of alternative processors when all retries have been exceeded. The state machine governing this process is shown below:
The main point to take from this diagram is that during an iteration individual iterations will be retried, but that if all retries are exhausted during any single iteration the entire process will be rescheduled with an alternate. So, if there are two hundred iterations and the last one fails the entire set of two hundred will be re-run on the first alternate, assuming there is one available.
Initial configuration of the retry behaviour for each processor is done through the columns to the immediate right of the processor nodes in the AME main work area. By double clicking on these the user can change values for, from left to right, 'Retries', 'Delay' and 'Backoff'. These are the basic retry configuration options and have the following effects.
Retries - the number of times after the first attempted invocation that this processor should be run. So, if set to 1 there will be one retry after the first failed invocation.
Delay - the time delay in milliseconds between the first invocation failing and the second invocation attempting to run. Time delays can often be useful in cases where resources are less than optimally stable under heavy load, often in these cases simply waiting a second or two is enough to get a successful result.
Backoff - a factor determining how much the delay time increases for subsequent retries beyond the first. The time between attempt 'n' and 'n+1' is determined by multiplying the delay time by the backoff factor raised to the power of 'n-1'. So, if this value is set to 2 and the initial delay set to 1000 there will be a pause of one second between the first failure and the first retry, then should that in turn fail there would be a delay of 1000 x 21 = 2000 milliseconds or two seconds, the next would be four etc. Specifying a backoff factor reduces the load on heavily congested resources and is generally a good idea - the default value of 1 specifies no backoff, the delays will not increase between retries.
The 'Critical' checkbox determines what should happen if all retries and alternates have failed. If left unchecked the workflow will continue to run but processors 'downstream' of the failed processor will never be invoked, if checked then any failure here will abort the entire workflow.
The image below shows the user editing the delay factor for the processor 'FailingProcessor'. In this case the processor will retry twice after the initial failure, waiting first one second then two.
Configuring Alternate Processors
Once all retries have been used up without any successful invocation the processor concerned will fail and either silently abort all downstream processors, or abort the entire workflow, depending on whether the 'Critical' box is ticked. It is often possible, however, to have an alternative processor or list of processors which perform the same task, perhaps on a different server, and Taverna allows the user to explicitly state this in such a way that the alternate is used in place of the main processor when the latter has failed. To configure alternate processors the user expands the node for the main processor by clicking on the expand icon to the left of the processor icon. This then shows the inputs and outputs of the processor as before but also shows any defined alternates. An example of this display is shown below with the alternate fully expanded as well:
Note that the alternate has its own definable parameters for 'Retries', 'Delay' and 'Backoff'. The behaviour here is that once the main processor has failed and exceeded all retries the alternate is scheduled in its place, using the alternate's parameters for fault tolerance features.
As alternate processors may well not have the same names for inputs and outputs it is often necessary to explicitly state that a particular port in the primary processor is equivalent to another in the alternate. This is done by selecting the port in the alternate and right clicking, then selecting the port in the primary processor for which this is an equivalent. In cases where the alternate processor has port names which match those in the primary Taverna will automatically create a default identity mapping.
In this particular case the port with name 'string1' in the alternate is being mapped to the input port named 'foo' in the main processor.
Multiple alternates may be specified, if this is the case the order in which alternates will be used can be changed by right clicking on the alternate node and selecting 'Promote alternate' or 'Demote alternate' as appropriate. Alternates may also be removed from this menu:
Configuring Multithreading
The 'Threads' property determines how many concurrent instances of the processor should be used during an iteration. This property only ever has any effect therefore if there is some kind of iteration occurring over the input data. To understand why you might want to change this property consider the following example.
Suppose there is a service which takes one second to run. On the face of it starting five instances of this service simultaneously will take exactly the same time as running five in series, assuming here that there is a single CPU backing the service. There is, however, another issue to consider; services invoked over a network link incur an additional time cost called latency - this is the time taken for the data to be transported to and from the service, the time to set up any network traffic etc. If we have this one second process but in addition half a second before and after to send the data clearly there will be a one second pause on the service end between iterations corresponding to the time to send the result back and for the enactor to send the next input data item. If, however, multiple threads run concurrently the enactor is sending data at the same time as the service is working on the previous input - the service in this case never has to wait for the next input item as it has already been sent while it was working on the previous one.
In the case of the service described above, running five iterations in series would take 5 x (0.5 + 1 + 0.5) = 10 seconds. If we were to run all five in parallel the time taken is 0.5 + 5 x (1) + 0.5 = 6 seconds, the half second latency occurs at the start and end of the entire process rather than per iteration. Clearly under these conditions running multiple worker threads is beneficial.
The other case where this is a reasonable thing to do is where the service is backed by some significant multiprocessor compute resource such as a cluster. If we were to send tasks in series the cluster would only ever have one job running at any time, whereas a parallel invocation would allow the workflow to saturate the cluster, running all nodes in the cluster at full capacity and completing massively faster. If our example above were run on a five node compute cluster the total time would actually be 0.5 + 1 +0.5 = 2 seconds, the five jobs all running simultaneously on different units within the cluster.
Taverna sets a limit on the maximum number of threads for each processor type. It is also generally a good idea to check with the administrator of the service concerned before hammering it with massively parallel requests, not all services can take this kind of load!
Data Link Nodes
Each data link (between workflow inputs, processors and workflow outputs) in the workflow appears under the 'Data links' node in the AME. This display is largely informational although data links may also be deleted from the right click context menu. Note that any data links connected to a processor are deleted if that processor is also deleted; there are no 'hanging links' in a workflow model.
Naming for the data links is in the form of the processor name and port name for the output or data source, separated by a colon, then an arrow and the same syntax for the sink or input port it links to. In the case of workflow inputs and outputs there is no processor name, just the name of the output or input.
Control Link Nodes
As with the data links these are primarily for information but also allow removal of individual control links. The naming syntax consists of the processor acting as the gate condition, two colons then the name of the processor being controlled by it. So, 'foo::bar' represents a condition stating that 'bar' may not start until 'foo' has finished.
Workflow Diagram
This component provides a read only configurable view of the workflow in graphical form. Workflow inputs, outputs and processors appear as coloured boxes with arrows between them to represent data and control links. Options are available to configure the layout and level of detail shown and the resultant diagram may be saved to disc in a number of different formats. It is intended to allow a quick visual overview of the structure of a workflow.
Toolbar
The toolbar consists of five items:
Save as...
Dot format text
Portable Network Graphics format image
Scalable Vector Graphics format image
Show types checkbox
Port display
Alignment control
Fit to window checkbox
Diagram Save Options
All saved files will have the same layout, port display, alignment and type display as the current visible image. They will not, however, be scaled in any way. The various options are to save as dot format text (the native format used internally and by GraphViz from AT&T research), PNG image (a bitmap graphic format suitable for web pages, Word documents etc) and SVG (a vector graphic format, preferable if you have tools such as Adobe Illustrator which can work with it as there is no loss of detail from scaling, particularly suitable therefore for posters).
Selecting any of the save options will display a file chooser in a format appropriate to your operating system. You will need to add the appropriate file extension yourself, saving dot files as .text, png as .png and svg as .svg appropriately.
Show Types
This checkbox determines whether the diagram should include information about the MIME types and collection structures being transferred between processors in the workflow. The image below shows the diagram panel displaying a workflow with no port information, vertical layout and no types.
If the 'Show types' checkbox is selected the display changes to the following:
Each solid dark arrow (a data link) has two types. The upper of these is the type of information flowing out of the output of the processor the link is from, the lower that of the input the link is to. In most cases these will be identical, for example in the case of the link between processors 'id' and 'Create_moby_data' the type is plain text at both ends of the link.
A more interesting point in the above image is the link between 'getDragonSimpleAnnotatedImages' and 'Parse_moby_data'. In this case the output is a higher cardinality than the input. A list of XML text items is being fed into a port which takes a single XML text item. This is not a problem, Taverna will automatically iterate over the input set, running the 'Parse_moby_data' multiple times. In general Taverna syntactic type expressions are of the form l('text/plain'), 'text/xml' or l(l(l('image/png'))), The l(...) indicates a list of the enclosed type, and can be nested arbitrarily deeply, the mime type or types enclosed in the single quotes act as hints to the rendering and other systems as to how the data should be displayed. More information on exactly how the collection structures are used to power the iteration system is available in the section on iteration.
Port Display Selection
Diagram views can have one of three settings for port display. The two images above were generated with no ports shown; this is useful to get a view of the overall structure of the workflow. It is impossible, however, to see exactly where the data links are connecting - the processors 'id' and 'namespace' both feed into 'Create_moby_data' but there is no indication as to which inputs they connect to. This additional information can be enabled by selecting either 'All ports' or 'Bound ports' from the drop down list:
Data links now connect explicitly to particular inputs and outputs, in this vertical view the outputs of each processor are below the processor name and the inputs above. The 'Bound ports' view shows only ports which are the subject of a data link. Switching to the 'All ports' view shows all processor inputs and outputs whether used or not. This view is often rather unwieldy when processors have upwards of forty inputs (this is not uncommon). For layout reasons the default alignment switches to 'Horizontal' when this is selected, in the view below it has been explicitly reset back to the 'Vertical' state for comparison purposes:
The unused ports 'stringvalue', 'floatvalue' and 'intvalue' on 'Create_moby_data' are now visible.
Alignment Control
The default alignment shows workflow inputs at the top of the diagram, outputs at the bottom with data therefore always flowing from top to bottom. For some diagram views the layout is better with data flowing from left to right, this can be selected by choosing the 'Horizontal' alignment option from the drop down menu. The previous display with horizontal rather than vertical alignment is shown below:
This view is generally more compact when all ports are shown, especially for processor types such as Soaplab which tend to have large number of inputs.
Fit to Window Checkbox
When selected the current diagram is scaled to fit entirely within the bounds of the Workflow diagram window, when de-selected the scrollbars can be used to scroll around the full scale image. This setting has no effect on any saved diagrams, it only affects the view within Taverna.
Processor Colours
Colour is used both in the diagram and explorer views to denote the service architecture behind each processor. The diagram below shows a workflow including examples of each different processor type:
The eight different processor types in this workflow are as follows:
TypeExampleDescriptionString ConstantnamespaceLocal operation to emit a single constant string.BioMobyBlastFasta...Service based on BioMoby, see HYPERLINK "http://www.biomoby.org" http://www.biomoby.org.Local JavaCreate_moby_dataLocal operation coded as a Java class, used for common or particularly generic functionality.SeqHoundTitleFromGiCall to a SeqHound server, see HYPERLINK "http://www.blueprint.org/seqhound" http://www.blueprint.org/seqhound for more details.SoaplabFormatSequenceService wrapper around a legacy command line tool such as, in this case, seqret from the EMBOSS tool set.WorkflowFixGeneOntologyIDA nested workflow exposed as a single operation.Web ServicecreateSessionA processor accessing a standard SOAP service.BeanshellFetchOntoGlyphUser editable scripting operation, in this instance using interpreted Java. Available Services
This component provides facilities to manage the various services available to the workflow designer and allows the user to manage service libraries, create instances of a service in the form of a processor within the workflow, search services and other such functionality.
Service Panel Organization
Services are shown in a hierarchical view. The exact semantics of this hierarchy vary by service type; soaplab, for example, has some concept of service categories so the view for a soaplab server shows the top level installation, then child nodes corresponding to the various categories and finally leaf nodes corresponding to actual services:
Biomoby services are located by accessing a central moby repository, in this case the hierarchy is constructed from a root node specifying the repository, then child nodes for each site hosting services and leaf nodes for each individual service:
In general all service types follow this pattern, the leaf nodes are always individual services with other nodes representing some kind of containment.
A special case is that of workflow nodes - these are nodes representing entire Taverna workflows but in addition may be expanded to show copies of the services within those workflows, effectively allowing the user to reuse components from another workflow without having to figure out where the component was originally found. An example of this is shown below:
Adding Instances of a Service to the Workflow
The primary function of the service panel is to allow users to create new processors within the current workflow. This can be accomplished either through drag and drop or through the context menus available by right clicking on the nodes in the service panel.
Creation using Drag and Drop
Any node representing a service may be dragged from the service selection panel into the AME. This also applies to nodes representing entire workflows in which case the workflow will be created as a nested workflow processor. The effect of a drop into the AME varies by location. If the node is dropped into area 1 in the diagram below it will be created as a new processor. If, however, area 2 is the target the effect will be to create a new alternate to the processor node the service was dragged onto.
Creation using Menu Options
As an alternative, new processors may be created by right clicking on the service to be used; this shows the following options:
Selecting 'Add to model' will create a new processor with the same name as the service node. If this name is unavailable the workbench will append a number to it until it is, creating processor names such as 'getDot1' rather than 'getDot'. If the 'Add to model with name...' is selected a dialogue is shown allowing the user to explicitly name the processor (processors may be renamed at any time from the AME by double clicking on their names there). The 'Add as alternate to...' submenu allows the user to specify that the new processor should be created as an alternate to the selected pre-existing processor in the workflow.
Discovering the Service for a given Processor
As processor names often do not correspond to the names of items in the service panel it can be helpful, especially when debugging a non functional workflow, to find out which node in the service panel would have produced a specific processor in the AME. To do this simply drag the processor node from the AME into the tree area of the service panel, the node corresponding to the service of which the processor is an instance will be selected and displayed in red if found. Some nodes, most noticeably beanshell scripts and string constants, may not be found correctly, this is due to the way they are represented within the services panel but for the majority of cases this approach works.
Importing a Workflow from the Service Panel
An additional option appears in the context menu when a workflow node is selected. This 'Import workflow...' option allows the user to import the entire contents of the workflow specified in the same way as if it had been loaded from the AME. Rather than being created as a nested workflow processor the workflow is loaded alongside any existing processors. To avoid name collisions (all processor names must be unique within a given workflow) the user can in addition specify a prefix string which is prepended to all processor names in the new workflow. The dialogue to set the prefix is shown when the 'Import workflow...' option is selected, pressing return without entering any text will be interpreted as 'no prefix'.
Searching Over the Service Panel
The service panel may be searched by regular expression. For convenience the text box in the toolbar allows the user to enter a fragment of a service name and either use the return key or the search icon to the right of the text box. As the user types all matching services are shown highlighted in red, when the search is activated by return key or search button the tree is collapsed then re-expanded to show all nodes matching the specified search. This is particularly useful if the user knows exactly what he or she is looking for. The image below shows the search in action:
Fetching Service Descriptions
Some types of service support a level of self description. Services based on soaplab, Biomoby, Seqhound and nested workflows can all provide textual descriptions of the operation the service performs. In order to fetch these descriptions the user can select the 'Fetch descriptions' option from the context menu on any node in the service panel other than the root. This will then start fetching descriptions in the background and adding them to the service panel as and when they are found. Descriptions are fetched for all nodes below the selected one in the tree, including the selected node itself. So, the user can fetch descriptions for a single operation, soaplab category, entire biomoby installation etc:
Descriptions are not fetched by default because, depending on the service concerned, they may incur a significant cost in terms of time and network activity. For example, to describe a soaplab service the service itself must be contacted and the description fetched - while each one is relatively small automatically fetching all of them would bring a slower network connection to its knees.
Taverna will not re-fetch a description when there is one present. This has particular relevance in the case of workflow processors. The child nodes (corresponding to the processors within the workflow) may have manually annotated descriptions added using the AME when that workflow was created, these descriptions will not be overwritten by any automatically fetched ones. In addition Taverna will flag resources with no descriptions and not attempt to re-fetch each time.
Populating the Services Panel
The service panel may be populated with available services in several distinct ways:
Context Menu
The context menu for the root node in the service panel, labelled 'Available Processors' can be used to add single 'scavengers' to the tree. A scavenger in this context is a part of Taverna's code which allows it to find services. There are a variety of options the user can select:
The Talisman scavenger can be ignored here, it is extremely specialized and only applicable to those already using the Talisman web application system. The others, however, operate as follows:
Biomoby Scavenger
Asks the user for the location of a Moby Central repository. It uses this repository to determine all available hosts and their services and adds the tree thus created to the service panel.
WSDL Scavenger
Asks the user for the location of a Web Service Description Language (WSDL) file on the web. It inspects this file to pull out all appropriate operations and adds them as WSDL based service nodes to the services panel.
Workflow Scavenger
Asks the user for the URL to a single XScufl definition file. Adds the workflow as a node in its own right and also adds each processor within the workflow as a child node before adding the workflow node to the services panel.
Soaplab Scavenger
Asks the user for the root URL of a soaplab server. Interrogates the server to get all available application categories then for each category creates a list of services in that category. Creates a new soaplab hierarchy and adds it to the services panel.
Collect from Web
Asks the user for an initial URL, uses this to start a web crawl looking for XScufl definitions and WSDL files. If it finds any it will add them below a top level 'web crawl starting from...' node in the service panel. This scavenger runs in the background as it can potentially take a considerable amount of time to complete.
Collect from Model
Inspects the current workflow (shown in the AME) and infers the existence of any services the workflow uses. For example, if a workflow uses one operation from a soaplab installation this operation will notice that, infer the existence of the soaplab server and create a new soaplab scavenger which will then contain all the other operations that server supports. This is an explicit version of the implicit functionality enabled by the 'watch loads' checkbox.
'Watch Loads' Option
If the 'Watch loads' checkbox in the Available Services toolbar is selected the current workflow will be watched. Any time a new service is added the services panel will initiate a new 'Collect from Model' operation. This means that a user can import all the services a particular workflow uses by simply loading the workflow then resetting it.
Drag and Drop
XScufl definitions or directories may be dragged onto the toolbar area of the Available Services window. This initiates a file traversal looking for XScufl files, and can be used to load a library of workflows from the local file system. In a sense this is the file equivalent of the 'Collect from Web' option in the context menu. If a directory is dropped a corresponding hierarchy in the tree is created, all leaf nodes are workflows and their child processes:
Default Initial Services
The service panel can be preloaded with services by specifying various defaults in the mygrid.properties file (see configuration section). Specifically, the following service types can be preloaded into the service panel:
Web services based on a WSDL description
Soaplab installations
Biomoby Central Directories
Web crawl based searches for workflows and WSDL files
The values for these properties correspond to the user inputs required for the equivalent context menu options.
Enactor Launch Panel
The Enactor Launch Panel (ELP) allows the user to enter input data for a workflow then launch the workflow enactor with that data. It is only shown if the workflow requires some input, if the workflow has no user specified inputs this panel is never shown and the enactment status display is shown directly.
The image below shows the initial state of the ELP when a workflow with one input called 'GINumber' is enacted:
The user selects the input from the tree and a new pane appears showing any workflow input metadata specified when the workflow was created in the AME. In this case this includes a free text description and MIME type:
To create a new value for this input the user selects the 'New Input' or 'New List' options from the toolbar. These allow the user to respectively create a new single input or a new collection. Inputs in Taverna are either single items or collections, collections in turn containing either single items or other collections but never both. This constraint is enforced in the input panel. Suppose the user wishes to specify a list of GI numbers as input for this workflow, he or she clicks on the 'New List' button then three times on the 'New Input' button to create three textual items within the list. The individual items initialize with a default textual value:
For textual data the value may be entered directly into the pane on the right of this window. For textual or binary data the data may also be loaded directly from a file (the 'Load' option) or URL (the 'Load from URL' option). Binary data such as images may only be loaded from these buttons, the text input area will be disabled in these cases.
Multiple inputs within a list may be loaded from a collection of files by selecting the 'Load Inputs' option from the toolbar. This then prompts the user to select one or more files on disk and loads the contents of each file into a separate data item. This is useful if you have a directory containing a large set of input files, much faster than creating and loading each one individually.
Input data items may be removed by selecting the item and clicking on the 'Remove' button in the toolbar.
Internally Taverna uses an XML representation of the entire set of inputs. This representation may be viewed by selecting the 'Input Document' node in the tree, the display looks something like the following:
The exact contents of the XML view are unimportant at this point. It is potentially useful, however, to save the entire input document for later re-use, in particular when a workflow is failing on certain input sets. The document may be saved in XML form from the 'Save Input Doc' and reloaded from the 'Load Input Doc' option.
Once all inputs have been defined the workflow may be enacted by clicking the 'Run Workflow' button at the bottom right of the window.
Enactment Status Panel
The Enactment Status Panel (ESP) shows the current progress of a workflow invocation, displays results to the user for browsing and handles storage of those results to local or remote data stores in a variety of formats. It appears when the user invokes a workflow; in the case of workflows with user defined inputs it appears after the user has entered any input data into the ELP, otherwise it appears immediately. There are four main tabs shown at different stages during and after a workflow enactment:
Enactment Status
This tab, which is active at all times during and after a workflow enactment, performs two roles. Firstly it shows a summary of the current state of each processor. This is displayed in a table, with one row per processor. The columns indicate processor type, name, current status, the time the status last changed and any additional information:
Processor States
The Last event field shows the most recent state change of the specified processor. All processors start with the ProcessScheduled state. When all the dependencies for a processor are satisfied (all data available and any control links activated) the processor is transitioned to the 'Invoking' state or 'InvokingWithIteration' In the case of iteration occurring over the inputs to that processor. In the event of failure the processor will potentially go to any of 'WaitingToRetry', 'AlternateScheduled' or 'ServiceFailure' states depending on how fault tolerance is configured. The state diagram under the basic fault tolerance section gives more details of the possible states in graphical form, the events correspond to states in that diagram.
Some events, especially 'InvokingWithIteration' carry additional information which is conveyed in the Event detail column. In the case of iteration this shows how many iterations are expected and the index of the current one.
Inspecting Intermediate Results
The second role of the status display is to allow the user to inspect intermediate inputs and outputs of individual processors both during and after a workflow invocation. This is especially helpful to determine the reasons for abnormal workflow operation (such as a processor failing) or to check that the data is really what the user expects when designing the workflow.
To inspect the inputs and outputs of a given processor the user selects the processor in the status table. The inputs and outputs (if present, a process which is still scheduled will have neither, one which is invoking or failed will have no outputs) are then displayed in tabs below the status table. This part of the display is identical to the result browser in functionality.
Result Browser
The Results tab is activated once the workflow has completed. It is automatically selected when this happens, meaning that very fast running workflows may never display the workflow status tab (although it can always be manually selected). This section allows the user to view both individual result items and any collection structures such as lists in which they occur. The output for a sample workflow is shown below:
This display shows six named outputs. These correspond to workflow outputs defined in the AME when the workflow was created. Note that if a particular part of the workflow has failed some output tabs may not be present, only outputs which contain results are displayed here.
Visualizing Individual Result Items
To show an individual result the user selects it from the panel on the left of the result display. This then displays that item in the panel to the right using the default renderer for that data type. In the example below the BLAST report output from the workflow is too large to be shown in the panel on the left (hence the 'Click to view...' notation) but can be displayed as plain text by selecting it:
Visualising Result Collections
Some outputs will contain more than one result. In the example below the 'GOTerms' output contains a list of plain text items. As these items are relatively short the actual values are shown in the left hand panel, individual items could be selected as in the example above but in addition the list itself can be selected. In this case the right hand panel will attempt to display all the items in a list, as these items are all textual this looks like a single document containing one item per line:
Non Textual Outputs
Workflows may output binary data in various forms. Provided the correct MIME types are specified for the workflow output the renderer selection mechanism will by default select an appropriate renderer component. In the example below the output 'GraphImage' has been selected. This output has been annotated with the 'image/png' MIME type from the AME's output metadata editor. As a result the default renderer is one that can understand and display images:
As with textual items collections of images (or other binary data) are also handled, the following image is from a workflow using the EMBOSS tools - these generate lists of images corresponding to pages of output, in this case from the 'plot' tool rendering a multiple sequence alignment:
Selecting a Renderer
In some cases there may be multiple available renderers for the data item. For example, all the 'text/....' types can be rendered as plain text. There are several however which may also be rendered in some more specific way as well, for example as an XML tree or rich text format document. The example below shows a Genbank record displayed as text. If, however, the user right clicks on the data item he or she is presented with a menu offering other rendering options:
By selecting the SeqVista renderer the user is then presented with a much more friendly display. This renderer is an embedded instance of the SeqVISTA tool ( HYPERLINK "http://zlab.bu.edu/SeqVISTA/" http://zlab.bu.edu/SeqVISTA/) and is used to display a variety of sequence file formats:
The SeqVista renderer is currently attached to the chemical/seq-aa-genpept, chemical/seq-na-genbank, chemical/x-swissprot, chemical/x-embl-dl-nucleotide and chemical/x-ppd MIME types.
Saving Results to Disk
Results may be saved to disk either as single items or as entire result sets.
Saving Single Items
Single result items may be saved by right clicking on the item and selecting 'Save to file' from the context menu:
Saving Entire Result Set as XML
The 'Save as XML' button allows the entire result set to be stored as a single XML document. This can be reloaded into the data viewer tool (included with the workbench distribution) and could be regarded as the master data format. Files in this format allow the entire result display panel to be reproduced, including the various other export options and metadata such as data item LSIDs. When selected this prompts the user for a location for the save file. The XML file produced is not intended to be human readable!
Saving Entire Result Set as a Set of Files
The 'Save to disk' button allows the user to save all data items into a directory structure on the local disk. This is effectively the same as selecting all items individually and using the 'Save to file' option on each one with the additional feature that it handles collections such as lists. The user is prompted for the name of a new directory into which all workflow results are stored. In the case of single items the results are saved as files with the filename being derived from the output name, in the case of collections a new directory is created under the top level result directory with the name of the workflow output. Items within the collection structure are then created with names generated from numbers starting at zero. An example output directory show in the Windows XP explorer would look like this:
The single items 'BlastReport', 'GraphImage', 'ProteinSequence' and 'Title' have been created as files with the appropriate file extensions (guessed from the MIME types) with the two lists 'GOTerms' and 'LocusLink' created as directories. Opening the 'GOTerms' folder shows the individual list items as distinct files:
Note that as most services run under UNIX variant operating systems the text files are likely to have UNIX line endings, opening them with Notepad will generally result in a garbled mess, Wordpad or Emacs will work better. This only applies to Windows users, Linux and Solaris users should use whatever text editor is available on their installation.
Exporting Results to Excel
The 'Excel' button allows the export of all textual results up to two dimensions (maximum depth of collections is a list of lists of text) to a single Excel spreadsheet. When selected the user is prompted for the name of an Excel file (extension .xls). This is more or less meaningful depending on the workflow. An example workflow which emits a list of GO terms and their descriptions might produce an output such as the following in Excel:
In the case of this workflow there is a direct correlation between item 'n' in the name list and item 'n' in the id list, be aware that this is a property of this particular workflow and may not hold in general. In this case, however, the resultant spreadsheet is a sensible tool to view the relationship between these outputs.
Result Table
The 'Result Table' tab allows the user to view some of the relationships between workflow inputs, intermediate results and workflow outputs within the current workflow invocation. In the example below the same workflow used to generate the previous Excel screenshot is shown. The selected cell 'tumor necrosi...' is shown in full in the right hand panel. The cells highlighted pale blue in the table show intermediate values and workflow inputs which were used to obtain the selected cell's value. In this case it shows that the workflow input named 'gi' with value '4557225' resulted in the GOIDFromGi:return intermediate output with value '43120' which then in turn resulted in the selected cell. Cells coloured pale blue to the left are ancestors giving rise to the selected data item, those to the right (none in this view) are descendents who's values are derived in some way from the selected cell:
Process Report
The 'Process report' tab provides a view on the process log for the workflow. This is displayed whether the workflow succeeded or not. For each processor in the workflow a node is created in the log, events are then appended on to this node. The events are the same as those in the status display, with additional information added as child nodes onto the event node concerned. This display is only of interest when debugging workflows, in the event of failures it can potentially show enough information to diagnose the problem. This information, however, is generally not very friendly, it often consists of Java stack traces and error reports directly extracted from the service access plug-ins. An example of a successful enactment trace is shown below:
Browsing the myGrid Information Repository
The MIR can be searched using the MIR Browser plug-in to Taverna. It connects to an instance of the myGrid Information Repository and allows navigation and manipulation of the entities contained within the repository.
The Information Model Browser can be launched from within the myGrid Taverna workbench, via the 'Tools and Workflow Invocation' menu. A window will open up, and after a moment of communicating with the MIR, it should display a list of users.
If an error message pops up then it is likely that the browser is having trouble connecting to the MIR, and you might want to double-check that the MIR is correctly installed and configured, and that the mir.wsdl property above is correct.
To login, select a user and type in a password. As implied by the message at the top of the window, the password field is currently just a placeholder and any password will work. Clicking the login button will complete the login, and switch the browser to the Information Model tree view, shown here. The tree shows a representation of the objects in the information model and the relationships between them. The tree starts with the selected user as the object to start browsing from, but you can change this at any time by selecting a new object, and clicking on the button in the toolbar.
By right-clicking on any relationship, you can add new or existing items to it or, if only one object is allowed, then you can change that object. Workflows can be added to the MIR as the content of an operation, and they can then be reloaded back into Taverna by right-clicking on the content and selecting Load Workflow.
In order to avoid having infinitely recursive branches and to generally just stop the tree from becoming too complicated, the tree only creates children for the first instance of an object that it finds. For any other instances, the icon for the object is rendered transparently, and no children are created. You can select any one of those other instances, in order to make that one the one with the children, assuming that this won't create any recursion.
At the bottom of the window is the workflow context. This is information used by another taverna plug-in to create new information model objects related to a specific workflow enactment. With a workflow context, when a workflow is run, a new experiment instance is created, and linked to the experiment design specified in the workflow context. In order to change the context, all you need to do is select a user, an organization or an experiment design in the tree and the selected item will replace the corresponding item in the workflow context.
Custom Lab Book Views
Each lab book can have a custom view based on a query. The query can be edited by right-clicking on the lab-book and editing its properties. The query requires an information model class and optionally one restriction (attribute, condition, value), all separated by commas:
If you don't include a restriction, then it will retrieve all the objects with that type. For example, this would return all the ExperimentDesigns in the MIR:
ExperimentDesign
The result of the query is displayed in the folder 'Rule Matches'. This would return all the ExperimentDesigns with the name 'Enactor Scenario':
ExperimentDesign,name,=,"Enactor Scenario"
If you leave any of the entries blank, other than the information model type, then it will fetch all instances of that type. The type can be one of the following values:
Study
Person
ExperimentDesign
ServiceDescription
Operation
OperationTrace
ScuflWorkflowTrace
ExperimentInstance
LSDocument
DocumentCollection
DataProvenance
LabBookView
Organization
Programme
Address
StudyParticipationEpisode
ActualInputParameter
ActualOutputParameter
FormalInputParameter
FormalOutputParameter
CreationType
AffiliationEpisode
OperationContent
OperationType
DataType
Format
The myGrid information model
The myGrid Information Model (IM) defines the basic concepts through which different aspects of an e-Science process can be represented and linked. By providing shared data abstractions that underpin important service interactions, the IM promotes synergy between myGrid components. The IM is defined in UML, and equivalent XML Schema definitions have been derived from the UML to facilitate the design of the myGrid service interfaces. An important feature of the IM is that it does not model application-specific data, but rather treats such data as opaque, and delegates responsibility for its interpretation to users and to application-specific services. As such, concepts such as sequence or gene are not explicitly described in the IM. Rather, the IM captures information that is common to, and may even be shared by, many e-Science applications, such as scientists, studies and experiments. A consequence of this design decision is that the myGrid components are less coupled to each other and to a particular domain, and so are more easily deployable in different contexts. However, interpretation and processing of the results for the end user becomes more difficult, and falls largely on the application developers shoulders.
Sections REF _Ref87775662 \r \h 4.1 to REF _Ref87849800 \r \h 4.4 provide a more detailed account of the information model entities considering them in logical sub-groups.
People and Organisations
Figure 4. SEQ Figure \* ARABIC 1 People and organisations
REF _Ref87774008 \h \* MERGEFORMAT Figure 4.1 illustrates a UML diagram of the entities related to the modeling of people and their affiliation. The relationship of a Person with an Organizational Structure is captured by an Affiliation, where the period of the affiliation and the status of the person are recorded using Affiliation Episode.
The e-Science Experiments
Figure 4. SEQ Figure \* ARABIC 2 e-Science experiments
The UML diagram in REF _Ref87775957 \h \* MERGEFORMAT Figure 4.2 illustrates the classes related to the modelling of the e-Science experiments. In summary: a Study encapsulates an e-Scientists research focus and models the e-Science experiments A Programme is a structuring device for grouping other Studies and can be used to represent e.g. a project or sub-project. An Experiment Design represents the method to be used (typically as a workflow script) to solve a scientific problem. An Experiment Instance is an application of an Experiment Design and represents some executing or completed task. A persons relationship with a study is captured by a Study Participation, and the history of their relationship is captured as a collection of StudyParticipationEpisodes each with a definite start date and a (possibly undefined) end date. The StudyParticipation is the primary means of authorizing a Person to access or modify resources associated with a Study and of identifying provenance (e.g. creator of a document, instigator of a workflow enactment).
Experiment and Data Provenance
Figure 4. SEQ Figure \* ARABIC 3 Experiment and data provenance
The UML diagram in REF _Ref87850541 \h \* MERGEFORMAT Figure 4.3 illustrates the entities that model data provenance. E-science experiments, represented by ExperimentInstances contain an Operation Trace which identifies the exact inputs (i.e. Actual Input Parameter) and outputs (i.e. Actual Output Parameter). These actual parameters have types defined by the corresponding formal parameters of the operation ( HYPERLINK \l "_Operations" see Section 4.4). The actual data values used as inputs or generated as outputs are defined by LSDocument and DocumentCollection entities. Note that a Document Collection may contain many LSDocuments and that a DocumentCollection is defined as a sub-type of LSDocument, which effectively facilitates the definition of arbitrary collection hierarchies.
The exact trace information varies with the type of operation invoked. However, for a Scufl workflow operation (i.e. ScuflWorkflowTrace), it includes traces of the sub-operations invoked, recursively. An Operation Trace represents the experiment provenance (i.e. inputs, outputs and the intermediate results of an experiment). Another type of provenance defined in the model is the Data Provenance which primarily indicates a data items creation method (defined by the CreationType entity) and time.
Operations
The UML diagram in REF _Ref87850637 \h \* MERGEFORMAT Figure 4.4 shows the entities modelling the operation definitions. The method of an experiment definition is represented by a computational Operation, and its type by an OperationType. These two classes are extension points for myGrid. Currently known or anticipated operation types represent Scufl workflows, distributed queries, Soaplab requests and web service operations. Because web services typically support more than one operation grouped in ports, the model also recognizes the notion of a Service, though only their individual operations can supply the method for an Experiment Definition. The formal input and output parameters of an operation are defined by Formal Input Parameter and Formal Output Parameter entities respectively. The DataType entity provides information on semantic (i.e. defined in an ontology) and syntactic types of parameters. The type information can also include a number of Formats identified by ontological concepts.
Figure SEQ Figure \* ARABIC 4.4 Operations
Life Science Identifiers
LSIDs (Life Science Identifiers) are a standard way to identify life science objects, and a standard mechanism to retrieve data and metadata about that object. The standard was developed by members of the now defunct I3C (Interoperable Informatics Infrastructure Consortium) and has become a final adopted standard of the OMG (Object Management Group). The final specification can be found on the HYPERLINK "http://www.omg.org/cgi-bin/doc?dtc/04-05-01" OMG website.
Their development was driven by the need to cross reference between many life science databases and so integrate diverse sources of information for such tasks as drug discovery. Each database uses a different identification scheme for objects and a different mechanism to return their associated data. A useful first step in making integration easier is to have a common identification scheme and mechanism to retrieve information from the databases.
Of course there are other hurdles to integration information across different databases. For example, databases may label a biological entity, such as a protein, in completely different ways, or databases may differ in how they define classes of biological entity, such as a gene, which makes integration of information difficult. LSIDs are purely technology to make identification more uniform. There is nothing in the standard that addresses these higher level issues in the community.
What is an LSID?
The identifier consists of five parts:
urn:lsid:AuthorityID:NamespaceID:ObjectID:RevisionID
Example: urn:lsid:ncbi.nlm.nig.gov:GenBank:NM_000513:2
LSID Designator: A mandatory preface that notes that the item being identified is a life science-specific resource
Authority Identifier: An Internet domain owned by the organization that assigns an LSID to a resource
Namespace Identifier: The name of the resource (e.g., a database) chosen by the assigning organization
Object Identifier: The unique name of an item (e.g., a gene name or a publication tracking number) as defined within the context of a given database
Revision Identifier: An optional parameter to keep track of different versions of the same item
The aim is to use existing object identifiers where possible, so in the GenBank example above NM_00513 is an existing GenBank accession number for a human mRNA sequence.
Use of LSIDs in myGrid
myGrid components are themselves responsible for creating, identifying and storing results. To make the components more cohesive, we also need a standard scheme for identification and have chosen LSID.
The Practicalities
As a user of myGrid, the creation of LSIDs and their management should be relatively hidden. One place where LSIDs are visible is in the results panel of Taverna. This shows what a centrally assigned myGrid LSID looks like:
Figure 5.1 Excerpt of Taverna Enactor Invocation Panel showing the LSID that has been assigned to the new result (urn:lsid:www.mygrid.org.uk:lsdocument:Y2UGZ2LTK99).
This new LSID will then follow that data item around and can be used to find it later using a number of different applications.
Configuring Taverna to use LSIDs
It is important that the same LSID is not given to two different pieces of data. To ensure that an LSID is never reused, the LSID standard describes an LSID Assigning Service. When an application such as Taverna needs an LSID it contacts the Assigning Service which creates a new unique LSID. Each Assigning Service has the authority to assign LSIDs for a set number of authorities and namespaces of objects. For example the core myGrid Assigning Service can assign LSIDs of the form urn:lsid:www.mygrid.org.uk and using a list of namespaces corresponding to information model entities. To tell Taverna where to find the Assigning Service you need to add a line in the mygrid.properties configuration file found in the /conf subdirectory.
#--------------------------------------------------------------------
taverna.lsid.providerclass = org.embl.ebi.escience.baclava.AssigningServiceClient taverna.lsid.asclient.endpoint = HYPERLINK "http://localhost:8080/authority/services/AssigningWebService" http://localhost:8080/authority/services/AssigningWebService taverna.lsid.asclient.ns.wfdefinition = operation
taverna.lsid.asclient.ns.wfinstance = experimentinstance taverna.lsid.asclient.ns.datathingleaf = lsdocument taverna.lsid.asclient.ns.datathingcollection = documentcollection #--------------------------------------------------------------------
Retrieving an Object
You can use Internet Explorer to retrieve an object using an LSID. IBM Life Sciences have developed a LSID client plug-in called LSID Launchpad. Download the latest version from HYPERLINK "http://www-124.ibm.com/developerworks/oss/lsid/" http://www-124.ibm.com/developerworks/oss/lsid/
INCLUDEPICTURE "http://darwin.zoology.gla.ac.uk/~rpage/portal/images/launchpad_small.gif" \* MERGEFORMATINET
Figure 5.2 A Screenshot of the LSID Launchpad from IBM.
Once installed, open Internet Explorer and in the address bar type lsidres: followed by the specific LSID. Internet Explorer should then display any metadata retrieved for that life science object and provide options to download the data in various formats.
When the plug-in goes off to find information about a LSID, it uses the second part of the ID, HYPERLINK "http://www.mygrid.org.uk" www.mygrid.org.uk in our example. It expects to find an LSID authority on this machine. From communication with the authority service it can then look for the actual data and metadata for that object. It is not always possible to put the authority at that location ( HYPERLINK "http://www.mygrid.org.uk/authority" http://www.mygrid.org.uk/authority) in our case, so for these special cases we tell the plug-in to look elsewhere using its configuration page.
To use myGrid LSIDs add a new authority mapping whose authority id is HYPERLINK "http://www.mygrid.org.uk" www.mygrid.org.uk and whose host is HYPERLINK "http://phoebus.cs.man.ac.uk:8081/authority" http://phoebus.cs.man.ac.uk:8081/authority
This can be tested with the following LSID urn:lsid:www.mygrid.org.uk:person:2111111
In the future more applications will be able to retrieve information given an LSID and seamlessly bridge between LSIDs from different authorities.
myGrid LSID components
For those deploying myGrid or just wanting to know more about the components used to provide LSID services the following section provides a more technical overview.
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Figure 5.4 Overall architecture of LSID components
The major LSID components that can be deployed within myGrid are:
LSID Assigning Service: used by Taverna and the mIR when they need to give an object a new unique myGrid LSID.
LSID Authority: used by LSID Launchpad when a user wants to retrieve information about an object identified with an LSID. The authority does not store the information itself but knows the location of other services that can provide the data for that object or any associated metadata.
LSID Data Resolver: The actual service used to retrieve an objects data. Usually this just passes the request on to the place where the actual data is stored. In the case of myGrid this is the mIR.
LSID Metadata Resolver: The actual service used to retrieve any metadata associated with an object. Metadata may be stored in several locations. In the case of myGrid, metadata is stored both in the mIR and in KAVE.
The data resolver is intimately tied to the myGrid Information Repository (mIR), and the metadata resolver is intimately tied both to the mIR and KAVE. When myGrid is deployed with LSID support, it is the combination of these six services that provide the support.
The life cycle of an LSID
A data item gets created as the product of a workflow, or it enters myGrid via storage in the mIR. The workflow enactor or the mIR need to assign a new LSID to the data item. They do this by contacting the LSID Assigning Service. Both Taverna and the mIR have configuration information to tell them where this Assigning Service is. The Assigning Service replies either with a single LSID or a block of LSIDs depending on what is required.
The data item is stored together with its new LSID. During the workflow this is achieved using the MIR-Taverna-plug-in. It monitors the progress of the workflow and stores items in the mIR as they are produced in the workflow. Both intermediate and final results are stored. If the item is being directly stored in the mIR, using the mIR Browser for example, it is the mIR itself which takes responsibility for obtaining the new LSID and storing both together
Metadata accrues for this object. If created during a workflow, several types of provenance information are stored in the mIR, linked to the new data item, such as when it was created, by whom, and during which workflow. In addition to this semantic provenance information is stored in KAVE using the KAVE plug-in. It stores information such as the semantic type of the data item, what it has been derived from and more conceptual relationships with other data items. Metadata in the mIR, conforms to a fixed, well-defined schema that primarily links a data item with other entities in the myGrid Information Model (link). Metadata in KAVE is more extensible, can be specific for one subject area (e.g. bioinformatics sequence analysis) and has integral links to ontologies.
A user wishes to retrieve information about an object, whether it is the actual data (e.g. a nucleotide sequence) or metadata (such as how it was derived and by whom). Metadata is often retrieved first. The client application contacts the LSID Authority pointed to by the authority domain of the LSID (A mapping file on the client may redirect to an authority in a different location). The Authority replies with a list of services that can provide data and metadata for this LSID (This is provided as a WSDL file [Web Services Description Language]) The client application uses the information in this WSDL file to contact the appropriate metadata resolvers. Several metadata resolvers may be available. Currently myGrid does not make use of these multiple Foreign Authorities as described in the final LSID specification. The myGrid lsid-metadata resolver contacts both the local mIR and KAVE service (the location of which is configured within the metadata resolver). It translates the response from the mIR into RDF (currently not all the types of entity are converted). It then combines this with any statements in KAVE concerning this object and sends the reply back to the client.
Retrieval of data is simpler. The client application contacts the lsid-data resolver which retrieves the mIR entry for that LSID in XML format.
LSIDs are never reused. The aim is to always provide the same data for a given LSID.
Gathering knowledge from workflows
Once a workflow has been run and produced a set of data it is important to store that data somewhere and also the context in which is was generated.
Several options are available from within Taverna to save results.
Figure 6.1 The results window in Taverna showing several options to manually save results
Each result can be saved individually by right clicking on the item. The result set can also be saved as a set of files in one go, or as a single XML document. However, this fails to store the context in which is created (i.e. how it has been created in the workflow).
In order to store more information, two plug-ins are available: the MIR-taverna-plug-in and the KAVE-Plug-in.
The MIR-taverna-plug-in stores intermediate and final results from a workflow run into the mIR. This plug-in also records which workflow the results were produced from, the user for which the workflow was run, and the experiment they were performing.
The KAVE-plug-in stores additional semantic information, such as the semantic types of final results. The derivation links between each data item (e.g. this blast result is derived from this query sequence and these parameters).
Adding semantic information
Two types of semantic information can be attached to the workflow. The first is a concept from an ontology to describe the type of a final result. This can be done in Taverna by right clicking on a workflow output, which opens a window on part of the myGrid ontology. Selecting a concept results in that output being annotated with that concept.
Figure 6.2 Annotating a workflow output fasta_out with the concept nucleotide_sequence_data from the myGrid ontology.
When the workflow is run, a statement is stored (as RDF triples) in KAVE which paraphrases as this specific result is of type nucleotide_sequence_data.
Other statements link a data item to any preceding data items in the workflow that it is derived from. If the user knows that these links have a specific meaning in the context of the workflow they can add these links by right clicking on a processor in the Advanced model explorer. An editor window appears, in which specific relationships can be typed in, and the subject and object of those relationships specified in terms of the inputs and outputs to that processor in the workflow. For example, the following figure shows the template editor for a repeat masker processor, in which the output masked is the masked_sequence_of the input sequence direct data. When the workflow is run an RDF statement will be added to KAVE, which links these two data items with the masked_sequence_of statement.
These relationship types will in future be obtained from an ontology to ensure uniformity across workflows.
Figure 6.3 Template editor showing how a new template can be added.
Browsing Provenance
A simple view of the type of provenance collected can be obtained from the HYPERLINK \l "_Browsing_the_myGrid_Information Rep" MIR browser and HYPERLINK \l "_myGrid_LSID_components" LSID Launchpad. See the relevant sections of this user guide for more information on these applications.
Useful Definitions
Workflow - A set of components and relations between them used to define a complex process from simple building blocks. Relations may be in the form of data links, transferring information from the output of one component to the input of another, or in the form of control links which state some conditions on the execution of a component. An example of a control link is the basic temporal ordering 'do not run component A until component B has completed'. In Taverna a workflow is realized by an instance of the workflow data model, this appears on disk or on the web as an XML file in the XScufl format.
Workflow Component - A workflow component is a reusable building block which performs some well defined function within a process. In the bioinformatics domain we can regard any command line tool or PERL script as a component, the critical definition is that this component should be atomic in nature, it cannot be split into smaller units. Components may consume information and may emit information, for example a BLAST job is a component which consumes a sequence and some search parameters (library, matrix, sensitivity etc) and which emits a report containing the sequence homologies found. Components may be located on any computational resource accessible via the internet or on the user's local workstation.
Workflow Service - All services are also Components, we use the term Service explicitly to refer to those components which are hosted on a computational resource external to the user's local workstation. Services have some underlying implementation technology such as SOAP (Simple Object Access Protocol) although this is hidden behind Taverna's abstraction layer as far as end users are concerned.
Enactor - A workflow enactor is the entity responsible for coordinating the invocation of components within Workflows. It may be manifested either as a software component within the workbench (as in Taverna), or as a service with a workflow definition and input data. The enactor manages the entire invocation process including progress reporting, data transfer between Components and any other housekeeping required.
The UML model (in Rational XDE format), the XML Schema definition and its java bindings are available as part of the myGrid download (within the myGrid information Repository (MIR) module).
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