Cited By
View all- Nikolov ASchwarte AHütter C(2013)FedSearchProceedings of the 12th International Semantic Web Conference - Part I10.1007/978-3-642-41335-3_27(427-443)Online publication date: 21-Oct-2013
Selectivity estimation for hybrid queries over text-rich data graphs
Pages 383 - 394
Abstract
Many databases today are text-rich, comprising not only structured, but also textual data. Querying such databases involves predicates matching structured data combined with string predicates featuring textual constraints. Based on selectivity estimates for these predicates, query processing as well as other tasks that can be solved through such queries can be optimized. Existing work on selectivity estimation focuses either on string or on structured query predicates alone. Further, probabilistic models proposed to incorporate dependencies between predicates are focused on the relational setting. In this work, we propose a template-based probabilistic model, which enables selectivity estimation for general graph-structured data. Our probabilistic model allows dependencies between structured data and its text-rich parts to be captured. With this general probabilistic solution, BN+, selectivity estimations can be obtained for queries over text-rich graph-structured data, which may contain structured and string predicates (hybrid queries). In our experiments on real-world data, we show that capturing dependencies between structured and textual data in this way greatly improves the accuracy of selectivity estimates without compromising the efficiency.
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Index Terms
- Selectivity estimation for hybrid queries over text-rich data graphs
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Published In
March 2013
793 pages
ISBN:9781450315975
DOI:10.1145/2452376
- General Chair:
- Giovanna Guerrini,
- Program Chair:
- Norman W. Paton
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Published: 18 March 2013
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View all- Nikolov ASchwarte AHütter C(2013)FedSearchProceedings of the 12th International Semantic Web Conference - Part I10.1007/978-3-642-41335-3_27(427-443)Online publication date: 21-Oct-2013
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