首页|Findings from University Mohamed Boudiaf-M'sila in the Area of Artificial Intell igence Reported (Explaining Query Answers In Probabilistic Databases)
Findings from University Mohamed Boudiaf-M'sila in the Area of Artificial Intell igence Reported (Explaining Query Answers In Probabilistic Databases)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing-Artificial Intelligence have been published. According to news reporting o ut of Msila, Algeria, by NewsRx editors, the research stated, "Probabilistic dat abases have emerged as an extension of relational databases that can handle unce rtain data under possible worlds semantics. Although the problems of creating ef fective means of probabilistic data representation as well as probabilistic quer y evaluation have been addressed so widely, low attention has been given to quer y result explanation." Our news journalists obtained a quote from the research from University Mohamed Boudiaf-M'sila, "While query answer explanation in relational databases tends to answer the question: why is this tuple in the query result? In probabilistic da tabases, we should ask an additional question: why does this tuple have such a p robability? Due to the huge number of resulting worlds of probabilistic database s, query explanation in probabilistic databases is a challenging task. In this p aper, we propose a causal explanation technique for conjunctive queries in proba bilistic 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 ."
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