Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging

M.R. Huq, Alessandra Mileo, Andreas Wombacher

Research output: Book/ReportReportProfessional

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Abstract

Data provenance allows scientists in different domains validating their models and algorithms to find out anomalies and unexpected behaviors. In previous works, we described on-the-fly interpretation of (Python) scripts to build workflow provenance graph automatically and then infer fine-grained provenance information based on the workflow provenance graph and the availability of data. To broaden the scope of our approach and demonstrate its viability, in this paper we extend it beyond procedural languages, to be used for purely declarative languages such as logic programming under the stable model semantics. For experiments and validation, we use the Answer Set Programming solver oClingo, which makes it possible to formulate and solve stream reasoning problems in a purely declarative fashion. We demonstrate how the benefits of the provenance inference over the explicit provenance still holds in a declarative setting, and we briefly discuss the potential impact for declarative programming, in particular for instance-driven debugging of the model in declarative problem solving.
Original languageUndefined
Place of PublicationEnschede
PublisherCentre for Telematics and Information Technology (CTIT)
Number of pages12
Publication statusPublished - May 2013

Publication series

NameCTIT Technical Report Series
PublisherUniversity of Twente, Centre for Telematica and Information Technology (CTIT)
No.TR-CTIT-13-11
ISSN (Print)1381-3625

Keywords

  • Logic Programming
  • EWI-23336
  • METIS-297630
  • IR-86059
  • Data Provenance
  • Provenance Graph

Cite this

Huq, M. R., Mileo, A., & Wombacher, A. (2013). Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging. (CTIT Technical Report Series; No. TR-CTIT-13-11). Enschede: Centre for Telematics and Information Technology (CTIT).
Huq, M.R. ; Mileo, Alessandra ; Wombacher, Andreas. / Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging. Enschede : Centre for Telematics and Information Technology (CTIT), 2013. 12 p. (CTIT Technical Report Series; TR-CTIT-13-11).
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Huq, MR, Mileo, A & Wombacher, A 2013, Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging. CTIT Technical Report Series, no. TR-CTIT-13-11, Centre for Telematics and Information Technology (CTIT), Enschede.

Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging. / Huq, M.R.; Mileo, Alessandra; Wombacher, Andreas.

Enschede : Centre for Telematics and Information Technology (CTIT), 2013. 12 p. (CTIT Technical Report Series; No. TR-CTIT-13-11).

Research output: Book/ReportReportProfessional

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T1 - Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging

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N2 - Data provenance allows scientists in different domains validating their models and algorithms to find out anomalies and unexpected behaviors. In previous works, we described on-the-fly interpretation of (Python) scripts to build workflow provenance graph automatically and then infer fine-grained provenance information based on the workflow provenance graph and the availability of data. To broaden the scope of our approach and demonstrate its viability, in this paper we extend it beyond procedural languages, to be used for purely declarative languages such as logic programming under the stable model semantics. For experiments and validation, we use the Answer Set Programming solver oClingo, which makes it possible to formulate and solve stream reasoning problems in a purely declarative fashion. We demonstrate how the benefits of the provenance inference over the explicit provenance still holds in a declarative setting, and we briefly discuss the potential impact for declarative programming, in particular for instance-driven debugging of the model in declarative problem solving.

AB - Data provenance allows scientists in different domains validating their models and algorithms to find out anomalies and unexpected behaviors. In previous works, we described on-the-fly interpretation of (Python) scripts to build workflow provenance graph automatically and then infer fine-grained provenance information based on the workflow provenance graph and the availability of data. To broaden the scope of our approach and demonstrate its viability, in this paper we extend it beyond procedural languages, to be used for purely declarative languages such as logic programming under the stable model semantics. For experiments and validation, we use the Answer Set Programming solver oClingo, which makes it possible to formulate and solve stream reasoning problems in a purely declarative fashion. We demonstrate how the benefits of the provenance inference over the explicit provenance still holds in a declarative setting, and we briefly discuss the potential impact for declarative programming, in particular for instance-driven debugging of the model in declarative problem solving.

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Huq MR, Mileo A, Wombacher A. Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging. Enschede: Centre for Telematics and Information Technology (CTIT), 2013. 12 p. (CTIT Technical Report Series; TR-CTIT-13-11).