COBRA: Compression via abstraction of provenance for hypothetical reasoning

Daniel Deutch, Yuval Moskovitch, Noam Rinetzky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Data analytics often involves hypothetical reasoning: repeatedly modifying the data and observing the induced effect on the computation result of a data-centric application. Recent work has proposed to leverage ideas from data provenance tracking towards supporting efficient hypothetical reasoning: instead of a costly re-execution of the underlying application, one may assign values to a pre-computed provenance expression. A prime challenge in leveraging this approach for large-scale data and complex applications lies in the size of the provenance. To this end, we present a framework that allows to reduce provenance size. Our approach is based on reducing the provenance granularity using abstraction.We propose a demonstration of COBRA, a system that allows examine the effect of the provenance compression on the anticipated analysis results. We will demonstrate the usefulness of COBRA in the context of business data analysis.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PublisherIEEE Computer Society
Pages2016-2019
Number of pages4
ISBN (Electronic)9781538674741
DOIs
StatePublished - 1 Apr 2019
Externally publishedYes
Event35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Conference on Data Engineering
Volume2019-April
ISSN (Print)1084-4627

Conference

Conference35th IEEE International Conference on Data Engineering, ICDE 2019
Country/TerritoryChina
CityMacau
Period8/04/1911/04/19

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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