Shemon Rawat
ABSTRACT
In recent years, a large amount of data is collected from multiple sources and the demands for analyzing these data have increased enormously. Data sharing is a valuable part of this data-intensive and collaborative environment due to the synergies and added values created by multi-modal datasets generated from different sources. In this work, we introduce a technique that can be used for quantifying the degree of information gain (IG) that may be obtained over data sharing. Our method captures both where- (to compute the IG over values) and how-provenance (to find matching records) and accurately computes the IG based on them. We conduct a preliminary evaluation to show the runtime of our approach over a real-world dataset.
- E. Ainy, P. Bourhis, SB. Davidson, D. Deutch, and Tova Milo. 2015. Approximated Summarization of Data Provenance. In CIKM. 483--492.Google Scholar
- Peter Buneman, Sanjeev Khanna, and Wang-Chiew Tan. 2001. Why and Where: A Characterization of Data Provenance. In ICDT. Springer, 316--330.Google ScholarDigital Library
- Surajit Chaudhuri, Bolin Ding, and Srikanth Kandula. 2017. Approximate Query Processing: No Silver Bullet. In SIGMOD. ACM, 511--519.Google Scholar
- Shihyen Chen, Bin Ma, and Kaizhong Zhang. 2009. On the similarity metric and the distance metric. Theor. Comput. Sci. 410, 24--25 (2009), 2365--2376.Google ScholarDigital Library
- James Cheney, Laura Chiticariu, and Wang-Chiew Tan. 2009. Provenance in databases: Why, how, and where. Now Publishers Inc.Google Scholar
- Per-Erik Danielsson. 1980. Euclidean distance mapping. Computer Graphics and image processing 14, 3 (1980), 227--248.Google Scholar
- Daniel Deutch, Yuval Moskovitch, and Noam Rinetzky. 2019. Hypothetical Reasoning via Provenance Abstraction. In SIGMOD. 537--554.Google Scholar
- Erich Grädel and Val Tannen. 2017. Semiring Provenance for First-Order Model Checking. arXiv preprint arXiv.1712.01980 (2017).Google Scholar
- T.J. Green, G. Karvounarakis, and V. Tannen. 2007. Provenance semirings. In PODS. 31--40.Google Scholar
- Taeho Jung, Seokki Lee, and Wenyi Tang. 2021. Using Provenance to Evaluate Risk and Benefit of Data Sharing. In TaPP.Google Scholar
- Taeho Jung, Xiang-Yang Li, Wenchao Huang, Jianwei Qian, Linlin Chen, Junze Han, Jiahui Hou, and Cheng Su. 2017. AccountTrade: Accountable protocols for big data trading against dishonest consumers. In INFOCOM. IEEE, 1--9.Google Scholar
- Sven Köhler, Bertram Ludäscher, and Daniel Zinn. 2013. First-Order Provenance Games. In In Search of Elegance in the Theory and Practice of Computation. Springer, 382--399.Google Scholar
- Seokki Lee, Bertram Ludäscher, and Boris Glavic. 2018. Provenance summaries for answers and non-answers. Proceedings of the VLDB Endowment 11, 12 (2018), 1954--1957.Google ScholarDigital Library
- Seokki Lee, Bertram Ludäscher, and Boris Glavic. 2018. PUG: a framework and practical implementation for why and why-not provenance. The VLDB Journal (2018), 1--25.Google Scholar
- Seokki Lee, Bertram Ludäscher, and Boris Glavic. 2020. Approximate Summaries for Why and Why-not Provenance. Proc. VLDB Endow. 13, 6 (2020), 912--924.Google ScholarDigital Library
- Kaiyu Li and Guoliang Li. 2018. Approximate Query Processing: What is New and Where to Go? - A Survey on Approximate Query Processing. Data Sci. Eng. 3, 4 (2018), 379--397.Google Scholar
- Tobias Müller, Benjamin Dietrich, and Torsten Grust. 2018. You Say'What', I Hear'Where'and'Why':(Mis-) Interpreting SQL to Derive Fine-Grained Provenance. arXiv preprint arXiv:1805.11517 (2018).Google Scholar
- Mohammad Norouzi, David J Fleet, and Russ R Salakhutdinov. 2012. Hamming distance metric learning. In Advances in neural information processing systems. 1061--1069.Google Scholar
- Jane Xu, Waley Zhang, Abdussalam Alawini, and Val Tannen. 2018. Provenance Analysis for Missing Answers and Integrity Repairs. Data Engineering (2018), 39.Google Scholar
- Liu Yang and Rong Jin. 2006. Distance metric learning: A comprehensive survey. Michigan State Universiy 2, 2 (2006), 4.Google Scholar
Index Terms
- Measuring information gain using provenance
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