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Scientific Workflow Provenance Repository and Publishing Toolkit

Saumen Dey
Saumen Dey
Bio: 

Ph.D. student, Dept. of Computer Science; MBM Systems and Operations Research, University of Calcutta, India; B.Sc, Mathematics, Jadavpur University, India
Research Interests: Privacy-Aware Provenance Publication, Data Intensive Web application, and Cloud Computing.
Publications: Dey, S., Zinn, D., Ludäscher, B.: PROPUB: Towards a Declarative Approach for Publishing Customized, Policy-Aware Provenance. In: Scientific and Statistical Database Management Conference (to appear). (2011); Dey, S., Zinn, D., Ludäscher, B.: Publishing Privacy-Aware Provenance by Inventing Anonymous Nodes. Resource Discovery (RED) 2011 Workshop (part of Extended Semantic Web Conference 2011).; Missier, P., Ludäscher, B., Bowers, S., Dey, S., Sarkar, A., Shrestha, B., Altintas, I., Anand, M., Goble, C.: Linking multiple workflow provenance traces for interoperable collaborative science. In: Workflows in Support of Large-Scale Science (WORKS), 2010, IEEE 1–8

Project Description: 

Scientific workflow systems are increasingly used to automate scientific computations and data analysis and visualization pipelines. An important feature of scientific workflow systems is their ability to record and subsequently query and visualize provenance information. Provenance includes the processing history and lineage of data, and can be used, e.g., to validate/invalidate outputs, debug workflows, document authorship and attribution chains, etc. and thus facilitate “reproducible science”. We aim to develop (1) a provenance repository system for publishing and sharing data provenance collected from runs of a number of scientific workflow systems (Kepler, Taverna, Vistrails), together with (2) a provenance trace publication system that allows scientists to interactively and graphically select relevant fragments of a provenance trace for publishing. The selection may be driven by the need to protect private information, thus including hiding, abstracting, or anonymizing irrelevant or sensitive parts. Part (1) will be based on a DataONE-extension of the Open Provenance Model (D1-OPM) and leverage an earlier Summer of Code project. In particular, the provenance toolkit includes an API for managing workflow provenance (i.e., uploading into and retrieving from a data storage back-end). Part (2) will implement a new policy-aware approach to publishing provenance, which aims at reconciling a user’s (selective) provenance publication requests, with agreed upon provenance integrity constraints. For an existing rule-based backend, a graphical user environment needs to be developed that lets users select, abstract, hide, and anonymize provenance graph fragments prior to their publication.

Primary Mentor: 
Bertram Ludaescher
Secondary Mentor: 
Paolo Missier