Optimizing SQL Queries over Text Databases Export

@proceedings{citeulike:3680189, abstract = {Text documents often embed data that is structured in nature, and we can expose this structured data using information extraction technology. By processing a text database with information extraction systems, we can materialize a variety of structured "relations," over which we can then issue regular SQL queries. A key challenge to process SQL queries in this text-based scenario is efficiency: information extraction is time-consuming, so query processing strategies should minimize the number of documents that they process. Another key challenge is result quality: in the traditional relational world, all correct execution strategies for a SQL query produce the same (correct) result; in contrast, a SQL query execution over a text database might produce answers that are not fully accurate or complete, for a number of reasons. To address these challenges, we study a family of select-project-join SQL queries over text databases, and characterize query processing strategies on their efficiency and - critically - on their result quality as well. We optimize the execution of SQL queries over text databases in a principled, cost-based manner, incorporating this tradeoff between efficiency and result quality in a user-specific fashion. Our large-scale experiments- over real data sets and multiple information extraction systems - show that our SQL query processing approach consistently picks appropriate execution strategies for the desired balance between efficiency and result quality.}, author = {Jain, A. and Doan, An H. and Gravano, L.}, booktitle = {Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on}, citeulike-article-id = {3680189}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ICDE.2008.4497472}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4497472}, doi = {10.1109/ICDE.2008.4497472}, journal = {Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on}, keywords = {entity\_search, icd, icde\_2008, text\_database}, pages = {636--645}, posted-at = {2008-11-24 03:58:47}, priority = {5}, title = {Optimizing SQL Queries over Text Databases}, url = {http://dx.doi.org/10.1109/ICDE.2008.4497472}, year = {2008} }

TY - CONF ID - citeulike:3680189 L3 - citeulike-article-id:3680189 N2 - Text documents often embed data that is structured in nature, and we can expose this structured data using information extraction technology. By processing a text database with information extraction systems, we can materialize a variety of structured "relations," over which we can then issue regular SQL queries. A key challenge to process SQL queries in this text-based scenario is efficiency: information extraction is time-consuming, so query processing strategies should minimize the number of documents that they process. Another key challenge is result quality: in the traditional relational world, all correct execution strategies for a SQL query produce the same (correct) result; in contrast, a SQL query execution over a text database might produce answers that are not fully accurate or complete, for a number of reasons. To address these challenges, we study a family of select-project-join SQL queries over text databases, and characterize query processing strategies on their efficiency and - critically - on their result quality as well. We optimize the execution of SQL queries over text databases in a principled, cost-based manner, incorporating this tradeoff between efficiency and result quality in a user-specific fashion. Our large-scale experiments- over real data sets and multiple information extraction systems - show that our SQL query processing approach consistently picks appropriate execution strategies for the desired balance between efficiency and result quality. JF - Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on EP - 645 TI - Optimizing SQL Queries over Text Databases T2 - Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on SP - 636 KW - entity_search KW - icd KW - icde_2008 KW - text_database AU - Jain, A AU - Doan, An AU - Gravano, L PY - 2008/// UR - http://dx.doi.org/10.1109/ICDE.2008.4497472 ER -