Preemptive priority-based scheduling: an appropriate engineering approach
Advances in real-time systems
DBXplorer: enabling keyword search over relational databases
Proceedings of the 2002 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
Effective keyword search in relational databases
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
BLINKS: ranked keyword searches on graphs
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
BANKS: browsing and keyword searching 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
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Progressive Keyword Search in Relational Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Scalable top-k keyword search in relational databases
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
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Keyword search in relational databases has been widely studied in recent years. Most of the previous studies focus on how to answer an instant keyword query. In this paper, we focus on how to find the top-k answers in relational databases for continuous keyword queries efficiently. As answering a keyword query involves a large number of join operations between relations, reevaluating the keyword query when the database is updated is rather expensive. We propose a method to compute a range for the future relevance score of query answers. For each keyword query, our method computes a state of the query evaluation process, which only contains a small amount of data and can be used to maintain top-k answers when the database is continually growing. The experimental results show that our method can be used to solve the problem of responding to continuous keyword searches for a relational database that is updated frequently.