AB  - Keyword search on relational databases provides users with insights that they can not easily observe using the traditional RDBMS techniques. Here, an l-keyword query is specified by a set of l keywords, {k1, k 2, ? , kl}. It finds how the tuples that contain the keywords are connected in a relational database via the possible foreign key references. Conceptually, it is to find some structural information in a database graph, where nodes are tuples and edges are foreign key references. The existing work studied how to find connected trees for an l-keyword query. However, a tree may only show partial information about how those tuples that contain the keywords are connected. In this paper, we focus on finding communities for an l-keyword query. A community is an induced subgraph that contains all the l-keywords within a given distance. We propose new efficient algorithms to find all/top-k communities which consume small memory, for an l-keyword query. For topk l-keyword queries, our algorithm allows users to interactively enlarge k at run time. We conducted extensive performance studies using two large real datasets to confirm the efficiency of our algorithms. © 2009 IEEE.
AU  - Qin, L
AU  - Yu, JX
AU  - Chang, L
AU  - Tao, Y
DA  - 2009/07/08
DO  - 10.1109/ICDE.2009.67
EP  - 735
JO  - Proceedings - International Conference on Data Engineering
PY  - 2009/07/08
SP  - 724
TI  - Querying communities in relational databases
Y1  - 2009/07/08
Y2  - 2026/06/20
ER  -