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to generate web service request citation. Ghoshal and
Plale (Ghoshal and Plale, 2013) presented the most
relevant approach to ProvAnalyser. They explore the
options of deriving workflow provenance from exist-
ing log files. However, their focus is on collecting
provenance from different types of logs of distributed
applications. Our approach leverages Senaps event
log to capture interoperable provenance and analyse
it to understand and reproduce workflow outputs.
6 CONCLUSION
This work shows that provenance data can be captured
from scientific workflow systems’ event logs that can
verify the quality of their data products and allow the
analysis of workflows execution traces to make them
understandable and reusable. The logs can be filtered
and transformed into standardised provenance data
using a specialised model. This transformation allows
the recording of valuable information into a standard-
ised and workflow system-independent format that is
both interoperable and intelligible to the provenance
users. Also, the storage volumes of the provenance
required to perform data and workflow quality assess-
ments and analysis are smaller than the log size, indi-
cating the practical scalability of this transformation
process. While the workflow execution provenance
recorded from the event log can answer most of the
user queries, it is not always enough and, where it is
not, workflow prospective provenance can be inferred
and used. However, to enable comprehensive prove-
nance analytics, the systems should consider captur-
ing prospective and evolution provenance information
in their logs.
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