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WWW7 Proceedings of the seventh international conference on World Wide Web 7
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Proceedings of the 2006 ACM SIGMOD international conference on Management of data
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Spark: top-k keyword query in relational databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
BANKS: browsing and keyword searching in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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The VLDB Journal — The International Journal on Very Large Data Bases
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ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Reachability Indexes for Relational Keyword Search
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
ICDEW '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop
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Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
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Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
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ACM Transactions on Database Systems (TODS)
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Proceedings of the VLDB Endowment
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Data & Knowledge Engineering
Size-l object summaries for relational keyword search
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Information Sciences: an International Journal
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This paper introduces a novel keyword search paradigm in relational databases, where the result of a search is an Object Summary (OS). An OS summarizes all data held about a particular Data Subject (DS) in a database. More precisely, it is a tree with a tuple containing the keyword(s) as a root and neighboring tuples as children. In contrast to traditional relational keyword search, an OS comprises a more complete and therefore semantically meaningful set of information about the enquired DS. The proposed paradigm introduces the concept of Affinity in order to automatically generate OSs. More precisely, it investigates and quantifies the Affinity of relations (i.e. Affinity) and their attributes (i.e. Attribute Affinity) in order to decide which tuples and attributes to include in the OS. Experimental evaluation on the TPC-H and Northwind databases verifies the searching quality of the proposed paradigm on both large and small databases; precision, recall, f-score, CPU and space measures are presented.