TY - GEN
T1 - COBRA
T2 - 35th IEEE International Conference on Data Engineering, ICDE 2019
AU - Deutch, Daniel
AU - Moskovitch, Yuval
AU - Rinetzky, Noam
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85068014333&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2019.00228
DO - 10.1109/ICDE.2019.00228
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AN - SCOPUS:85068014333
T3 - Proceedings - International Conference on Data Engineering
SP - 2016
EP - 2019
BT - Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PB - IEEE Computer Society
Y2 - 8 April 2019 through 11 April 2019
ER -