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        <identifier>oai:meral.edu.mm:recid/5403</identifier>
        <datestamp>2021-12-13T02:24:19Z</datestamp>
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          <dc:title>Querying Connected Tuple Trees for Relational Keyword Search</dc:title>
          <dc:creator>Myint Myint Thein</dc:creator>
          <dc: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.</dc:description>
          <dc:date>2012-03-13</dc:date>
          <dc:identifier>http://hdl.handle.net/20.500.12678/0000005403</dc:identifier>
          <dc:identifier>https://meral.edu.mm/records/5403</dc:identifier>
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