Efficient type-ahead search on relational data: a TASTIER approach
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Keyword search on structured and semi-structured data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Efficient continuous top-k keyword search in relational databases
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Toward scalable keyword search over relational data
Proceedings of the VLDB Endowment
Providing built-in keyword search capabilities in RDBMS
The VLDB Journal — The International Journal on Very Large Data Bases
Improving web database search incorporating users query information
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Efficient fuzzy full-text type-ahead search
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient name disambiguation in digital libraries
WAIM'11 Proceedings of the 12th international conference on Web-age information management
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
Efficient Top-k Keyword Search Over Multidimensional Databases
International Journal of Data Warehousing and Mining
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A common approach to performing keyword search over relational databases is to find the minimum Steiner trees in database graphs. These methods, however, are rather expensive as the minimum Steiner tree problem is known to be NP-hard. Further, these methods cannot benefit from DBMS capabilities. We propose a new concept called Compact Steiner Tree (CSTree), which can be used to approximate the Steiner tree problem for answering top-k keyword queries efficiently. We propose a structure-aware index, together with an effective ranking mechanism for fast, progressive and accurate retrieval of $top$-$k$ highest ranked CSTrees. The proposed techniques can be implemented using a standard RDBMS to benefit from its indexing and query processing capability. The experimental results show that our method achieves high search efficiency and result quality comparing to existing state-of-the-art approaches.