Keyword search in databases: the power of RDBMS
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Ten thousand SQLs: parallel keyword queries computing
Proceedings of the VLDB Endowment
Scalable keyword search on large data streams
The VLDB Journal — The International Journal on Very Large Data Bases
Context-sensitive document ranking
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Effective and efficient keyword query interpretation using a hybrid graph
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Keyword search in graphs: finding r-cliques
Proceedings of the VLDB Endowment
Discovering top-k teams of experts with/without a leader in social networks
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Finding information nebula over large networks
Proceedings of the 20th ACM international conference on Information and knowledge 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
Ranking structural parameters for social networks
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
Proceedings of the VLDB Endowment
Density index and proximity search in large graphs
Proceedings of the 21st ACM international conference on Information and knowledge management
Information-complete and redundancy-free keyword search over large data graphs
Proceedings of the 21st ACM international conference on Information and knowledge management
Cager: a framework for cross-page search
Proceedings of the 21st ACM international conference on Information and knowledge management
Reverse engineering complex join queries
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
ROU: advanced keyword search on graph
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Strong simulation: Capturing topology in graph pattern matching
ACM Transactions on Database Systems (TODS)
UpSizeR: Synthetically scaling an empirical relational database
Information Systems
Top-K nearest keyword search on large graphs
Proceedings of the VLDB Endowment
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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, k2, · · · , 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 top kl-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.