01905nas a2200217   4500000000100000000000100001008004100002260001200043653001400055653002400069653001600093653002800109653001800137100001700155245005600172856008000228300001200308490000600320520134700326022001401673       2023                            d  c12/202310aCausality10aConjunctive Queries10aExplanation10aProbabilistic Databases10aQuery Answers1 aHichem Debbi00aExplaining Query Answers in Probabilistic Databases  uhttps://www.ijimai.org/journal/sites/default/files/2023-11/ijimai8_4_13.pdf  a140-1520 v83 aProbabilistic databases have emerged as an extension of relational databases that can handle uncertain data under possible worlds semantics. Although the problems of creating effective means of probabilistic data representation as well as probabilistic query evaluation have been addressed so widely, low attention has been given to query result explanation. While query answer explanation in relational databases tends to answer the question: why is this tuple in the query result? In probabilistic databases, we should ask an additional question: why does this tuple have such a probability? Due to the huge number of resulting worlds of probabilistic databases, query explanation in probabilistic databases is a challenging task. In this paper, we propose a causal explanation technique for conjunctive queries in probabilistic databases. Based on the notions of causality, responsibility and blame, we will be able to address explanation for tuple and attribute uncertainties in a complementary way. Through an experiment on the real-dataset of IMDB, we will see that this framework would be helpful for explaining complex queries results. Comparing to existing explanation methods, our method could be also considered as an aided-diagnosis method through computing the blame, which helps to understand the impact of uncertain attributes.  a1989-1660