Tracking workflow execution with TavernaProv
- Authors: Stian Soiland-Reyes, Pinar Alper, Carole Goble; eScience Lab, University of Manchester
- Document id: https://github.com/stain/2016-provweek-tavernaprov/
- Mirror: https://s11.no/2016/provweek-tavernaprov/
- DOI: https://doi.org/10.5281/zenodo.51314
- In reply to: PROV: Three Years Later
- Published: 2016-05-09
- Modified: 2016-05-11
- Accepted: 2016-05-16
- Presented: 2016-06-06 (Pinar Alper)
- Mirrored: 2020-08-04 (moved to s11.no)
- License: Creative Commons Attribution 4.0 International License
- scientific workflow
- research object
- Also available in formats:
Apache Taverna  is a scientific workflow system for combining web services and local tools. Taverna records provenance  of workflow runs, intermediate values and user interactions, both as an aid for debugging while designing the workflow, but also as a record for later reproducibility and comparison.
Taverna also records provenance of the evolution
of the workflow definition
(including a chain of
attributions and annotations; for brevity we here focus on
how Taverna’s workflow run provenance extends PROV and
is embedded with Research Objects.
A workflow run can be exported from Taverna as a workflow data bundle; a Research Object bundle  in the form of a ZIP archive that contains the workflow definition (itself a Research Object), annotations, inputs, outputs and intermediate values, and a PROV-O trace of the workflow execution, showing every process execution within the workflow run, linking to the produced and consumed values using relative paths.
A workflow run can thus be downloaded as a single file from a Taverna Server, shared (myExperiment, SEEK), published (ROHub, Zenodo), imported in another Taverna Workbench, shown in the Databundle Viewer or modified with the DataBundle API.
is a generic model for describing provenance. While this means there are
generally many multiple ways to express the same history in PROV,
a scientific workflow run with processors and data values naturally match the
However, using PROV-O to describe the details of a Taverna execution
meant a significant increase in verbosity. To simplify query and interoperability with PROV tools, we declare
relations both with starting point terms
and as qualified terms,
e.g. to represent an
used different values as different input parameters, we provide
both a direct used, but also a
qualifiedUsage to a
Usage that specify hadRole and entity.
PROV deliberately does not mandate how to make the design decisions on what activities, entities and agents participate in a particular scenario, but for interoperability purposes this flexibility means that PROV is a kind of “XML for provenance” - a common language with a defined semantics, but which can be applied in many different ways.
One interoperability design question for representing computational
workflow runs is how much of the
workflow engine’s internal logic and language should be explicit in the PROV
representation. As we primarily wanted to convey what happened in the workflow
at the same granularity as its definition, we tried to hide provenance that
would be intrinsic to the Taverna Engine, e.g. implicit iteration is
not shown as a separate
However keeping provenance only at the dataflow level (input/outputs of workflow processes) meant that TavernaProv could not easily represent “deeper” provenance such as the intermediate values of while-loops or intermittent failures that were automatically recovered by Taverna’s retry mechanism, as we wanted to avoid unrolled workflow provenance.
Keeping the link between the workflow definition and execution is essential to understanding Taverna provenance, yet PROV doesn’t describe the structure of a Plan. Taverna’s workflow definition is in Linked Data using the SCUFL2 vocabulary, which includes many implementation details for the Taverna Engine, and so forming a meaningful query like “What is the value made by calls to webservice X” means understanding the whole conceptual model of Taverna workflow definitions.
Therefore Taverna’s PROV export also includes an
annotation with the
wfdesc  abstraction of the workflow definition, embedding user annotations and higher-level information like web service location. wfdesc deliberately leaves out execution details like iteration and parallelism controls as it primarily functions as a target for user-driven annotations about the workflow steps.
Correspondingly the provenance bundle from TavernaPROV includes a higher level wfprov abstraction of the workflow execution, with direct shortcuts like describedByProcess and describedByParameter to bypass the indirection of PROV qualified terms; simplifying queries like “Which web service consumed value Y?".
The duality between wfdesc and wfprov is similar to the “future provenance” model of P-Plan and OPMW and its workflow templates , and similarly the the split between “prospective provenance” and “retrospective provenance” of the ProvONE Data Model for Scientific Workflow Provenance. 
Identifiers and interoperability
A great advantage of using Linked Data was that we could use the same identifiers in all three formats. One challenge was that Taverna workflows are often run within a desktop user interface or on the command line, and with privacy concerns we didn’t have the luxury of a server to mint URIs; we already learnt our lesson with LSIDs .
Taverna therefore generate UUID-based structured
http:// URIs within our namespaces,
Resolving these URIs gives the scufl2-info, web-service, which provide a minimal JSON-LD wfprov/wfdesc representation identifying the URI as a provenance or workflow item, but (by design) not having access to the data bundle it can’t say anything more.
We found that our UUID-based URIs don’t play too well with
PROV Toolbox and alternative
PROV formats like
every URI ending in
/ is registered as
a separate namespace in order to form valid QNames.
A suggested improvement for TavernaProv
is to generate provly identifiers, while remaining compliant with the 10 simple rules for identifiers. 
Similarly, OWL reasoning is not generally applied by PROV-O consumers, so even though wfprov formally extends PROV in its ontology definitions, we needed to add explicitly the implied PROV-O statements in TavernaProv’s Turtle output.
Common Workflow Language
Common Workflow Language has created a workflow language specification, a reference implementation cwltool, and a large community of workflow system developers who are adding CWL support across bioinformatics, including Apache Taverna and Galaxy. Unlike wfdesc, OPMW and P-Plan, CWL workflows are primarily intended to be executed, with a strong emphasis on the dataflow between command line tools packaged as Docker images.
CWL is specified using Schema Salad, which provides JSON-LD constructs in YAML. The CWL dataflow model is inspired by wfdesc and Apache Taverna and thus have similar execution semantics and provenance requirements. CWL is planning its provenance format based on PROV-O, wfprov and JSON-LD. As one of the CWL adopters, Apache Taverna will naturally also aim to support the CWL provenance model.
To face the verbosity issue, we are considering to split out wfprov statements to a different file; as a ZIP archive the Taverna Data Bundle can contain many provenance formats. Similarly splitting out the details using PROV-O Qualified Terms to a separate file is worth considering, this could also improve PROV visualization of workflow provenance. Having such separate PROV bundles would also make it easier for Taverna to support the ProvONE model as an additional format.
PROV Links could be added to Research Object Bundles to relate its data files to the then multiple workflow provenance traces that describe their generation and usage.
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