Abstract
Provenance information describes the origins and the history of data in its life cycle. Responsibility captures the notion of degree of causality and tells us which facts are the most influential in the lineage. Since responsibility cannot be computed by a relational query, the analysis of lineage becomes an essential tool to compute responsibility of tuples in the query results. We extend the definitions of causality and responsibility of a tuple t for the answer r to those of a set of tuples for the answer r, and Co-Trees to P-Trees for read-once functions. By using P-Trees, we develop an efficient algorithm to compute responsibilities of tuples in read-once formulas, and a novel algorithm to find top-k responsibility tuples in read-once functions. Finally, experimental evaluation on TPC-H data shows substantial efficiency improvement when compared to the state of the art.
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Qin, B., Wang, S., Du, X. (2013). Efficient Responsibility Analysis for Query Answers. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37487-6_20
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DOI: https://doi.org/10.1007/978-3-642-37487-6_20
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