Keyword search on structured and semi-structured data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Keyword search over relational tables and streams
ACM Transactions on Database Systems (TODS)
Toward scalable keyword search over relational data
Proceedings of the VLDB Endowment
A novel keyword search paradigm in relational databases: Object summaries
Data & Knowledge Engineering
A path-oriented RDF index for keyword search query processing
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Cascading top-k keyword search over relational databases
Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP
Size-l object summaries for relational keyword search
Proceedings of the VLDB Endowment
A distributed index for efficient parallel top-k keyword search on massive graphs
Proceedings of the twelfth international workshop on Web information and data management
Efficient Top-k Keyword Search Over Multidimensional Databases
International Journal of Data Warehousing and Mining
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Due to its considerable ease of use, relational keyword search (R-KWS) has become increasingly popular. Its simplicity, however, comes at the cost of intensive query processing. Specifically, R-KWS explores a vast search space, comprised of all possible combinations of keyword occurrences in any attribute of every table. Existing systems follow two general methodologies for query processing: (i) graph based, which traverses a materialized data graph, and (ii) operator based, which executes relational operator trees on an underlying DBMS. In both cases, computations are largely wasted on graph traversals or operator tree executions that fail to return results. Motivated by this observation, we introduce a comprehensive framework for reachability indexing that eliminates such fruitless operations. We describe a range of indexes that capture various types of join reachability. Extensive experiments demonstrate that the proposed techniques significantly improve performance, often by several orders of magnitude.