SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Join synopses for approximate query answering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
XRANK: ranked keyword search over XML documents
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Efficient keyword search for smallest LCAs in XML databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Bidirectional expansion for keyword search on graph databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Finding and approximating top-k answers in keyword proximity search
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Effective keyword search in relational databases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Flexible and efficient XML search with complex full-text predicates
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Spark: top-k keyword query in relational databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Effective keyword-based selection of relational databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Keyword search on relational data streams
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Towards keyword-driven analytical processing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Discover: keyword search in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient IR-style keyword search over relational databases
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
Keyword Search on Spatial Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Keyword search on structured and semi-structured data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Multi-dimensional keyword-based image annotation and search
Proceedings of the 2nd International Workshop on Keyword Search on Structured Data
Providing built-in keyword search capabilities in RDBMS
The VLDB Journal — The International Journal on Very Large Data Bases
Query expansion based on clustered results
Proceedings of the VLDB Endowment
Alternative query generation for XML keyword search and its optimization
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
Pragmatic correlation analysis for probabilistic ranking over relational data
Expert Systems with Applications: An International Journal
Semantic Query Expansion using Cluster Based Domain Ontologies
International Journal of Information Retrieval Research
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Given a set Q of keywords, conventional keyword search (KS) returns a set of tuples, each of which (i) is obtained from a single relation, or by joining multiple relations, and (ii) contains all the keywords in Q. This paper proposes a relevant problem called frequent co-occurring term (FCT) retrieval. Specifically, given a keyword set Q and an integer k, a FCT query reports the k terms that are not in Q, but appear most frequently in the result of a KS query with the same Q. FCT search is able to discover the concepts that are closely related to Q. Furthermore, it is also an effective tool for refining the keyword set Q of traditional keyword search. While a FCT query can be trivially supported by solving the corresponding KS query, we provide a faster algorithm that extracts the correct results without evaluating any KS query at all. The effectiveness and efficiency of our techniques are verified with extensive experiments on real data.