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Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
The VLDB Journal — The International Journal on Very Large Data Bases
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VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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A growing number of applications are built on top of search engines and issue complex structured queries. This paper contributes a customisable ranking-based processing of such queries, specifically SQL. Similar to how term-based statistics are exploited by term-based retrieval models, ranking-aware processing of SQL queries exploits tuple-based statistics that are derived from sources or, more precisely, derived from the relations specified in the SQL query. To implement this ranking-based processing, we leverage PSQL, a probabilistic variant of SQL, to facilitate probability estimation and the generalisation of document retrieval models to be used for tuple retrieval. The result is a general-purpose framework that can interpret any SQL query and then assign a probabilistic retrieval model to rank the results of that query. The evaluation on the IMDB and Monster benchmarks proves that the PSQL-based approach is applicable to (semi-)structured and unstructured data and structured queries.