Query Analytics over Probabilistic Databases with Unmerged Duplicates

Ekaterini Ioannou, Minos Garofalakis

Research output: Contribution to journalArticleScientificpeer-review


Recent entity resolution approaches exhibit benefits when addressing the problem through unmerged duplicates: instances describing real-world objects are not merged based on apriori thresholds or human intervention, instead relevant resolution information is employed for evaluating resolution decisions during query processing using “possible worlds” semantics. In this paper, we present the first known approach for efficiently handling complex analytical queries over probabilistic databases with unmerged duplicates. We propose the ENTITY-JOIN operator that allows expressing complex aggregation and iceberg/top-k queries over joins between tables with unmerged duplicates and other database tables. Our technical content includes a novel indexing structure for efficient access to the entity resolution information and novel techniques for the efficient evaluation of complex probabilistic queries that retrieve analytical and summarized information over a (potentially, huge) collection of possible resolution worlds. Our extensive experimental evaluation verifies the benefits of our approach.
Original languageEnglish
Pages (from-to)2245-2260
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number8
Publication statusPublished - 1 Aug 2015
Externally publishedYes


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