Provenance-based analysis of data-centric processes

Provenance-based analysis of data-centric processes We consider in this paper static analysis of the possible executions of data-dependent applications, namely applications whose control flow is guided by a finite-state machine, as well as by the state of an underlying database. We note that previous work in this context has not addressed two important features of such analysis, namely analysis under hypothetical scenarios, such as changes to the application’s state machine and/or to the underlying database, and the consideration of meta-data, such as cost or access privileges. Observing that semiring-based provenance has been proven highly effective in supporting these two features for database queries, we develop in this paper a semiring-based provenance framework for the analysis of data-dependent processes, accounting for hypothetical reasoning and meta-data. The development addresses two interacting new challenges: (1) combining provenance annotations for both information that resides in the database and information about external inputs (e.g., user choices) and (2) finitely capturing infinitely many process executions. We have implemented our framework as part of the PROPOLIS system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Provenance-based analysis of data-centric processes

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-015-0390-5
Publisher site
See Article on Publisher Site

Abstract

We consider in this paper static analysis of the possible executions of data-dependent applications, namely applications whose control flow is guided by a finite-state machine, as well as by the state of an underlying database. We note that previous work in this context has not addressed two important features of such analysis, namely analysis under hypothetical scenarios, such as changes to the application’s state machine and/or to the underlying database, and the consideration of meta-data, such as cost or access privileges. Observing that semiring-based provenance has been proven highly effective in supporting these two features for database queries, we develop in this paper a semiring-based provenance framework for the analysis of data-dependent processes, accounting for hypothetical reasoning and meta-data. The development addresses two interacting new challenges: (1) combining provenance annotations for both information that resides in the database and information about external inputs (e.g., user choices) and (2) finitely capturing infinitely many process executions. We have implemented our framework as part of the PROPOLIS system.

Journal

The VLDB JournalSpringer Journals

Published: Jun 18, 2015

References

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