@inproceedings{aa7af4dbcd834f4d83907ec26ac9cdea,
title = "COBRA: Compression via abstraction of provenance for hypothetical reasoning",
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.",
author = "Daniel Deutch and Yuval Moskovitch and Noam Rinetzky",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 35th IEEE International Conference on Data Engineering, ICDE 2019 ; Conference date: 08-04-2019 Through 11-04-2019",
year = "2019",
month = apr,
day = "1",
doi = "10.1109/ICDE.2019.00228",
language = "الإنجليزيّة",
series = "Proceedings - International Conference on Data Engineering",
publisher = "IEEE Computer Society",
pages = "2016--2019",
booktitle = "Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019",
address = "الولايات المتّحدة",
}