Querying connected tuple trees for relational keyword search

Keyword-based search in relational database is an easy and effective way for ordinary users or Web users to access relational databases. Even though relational database management systems (RDBMs) have provided full-text 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.