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Item
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Querying Connected Tuple Trees for Relational Keyword Search
http://hdl.handle.net/20.500.12678/0000005403
http://hdl.handle.net/20.500.12678/00000054033a96d956-9629-4afb-9ce2-0bd667cac9b0
7010d09e-675a-4115-a067-7590d45dc97a
Publication type | ||||||
---|---|---|---|---|---|---|
Conference paper | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Querying Connected Tuple Trees for Relational Keyword Search | |||||
Language | en | |||||
Publication date | 2012-03-13 | |||||
Authors | ||||||
Myint Myint Thein | ||||||
Description | ||||||
Keyword-based search in relational database is an easy and effective way for ordinary users or Web users to access relational database. Even though relational database management systems (RDBMs) have provided fulltext search capabilities, they do not support keyword-based search model. The text databases and relational databases are different that is a challenging task to apply the keyword search techniques in information retrieval (IR) to DB. A common method to performing keyword search in relational database is to generate the minimum connected tuple sets in schema graph transformed from relations. Although existing candidate network (CN) generation methods retrieve a set of joining tuples, they are still problem which is causing large overhead for CNs generation. In this paper, we propose a new candidate network generation algorithm (Heuristic_CNGen) based on the iterative deepening A* (IDA*) algorithm. The proposed algorithm produces a minimum number of CNs according to the maximum number of tuple set. We generate CNs for a given keyword query. And then, we identify the connected tuple tree as a result according to generated CNs. We evaluate the proposed method on DBLP. |
||||||
Keywords | ||||||
Keyword-based, Relational Database, Candidate Network, Connected Tuple Tree | ||||||
Identifier | 10.1109/infrkm.2012.6204992 | |||||
Conference papers | ||||||
CAMP'12 | ||||||
13 March, 2012 | ||||||
International Conference on Information Retrieval & Knowledge Management | ||||||
Kuala Lumpur, Malaysia | ||||||
https://ieeexplore.ieee.org/xpl/conhome/6200786/proceeding |