DBXplorer: enabling keyword search over relational databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient IR-style keyword search over relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Efficient Merging and Filtering Algorithms for Approximate String Searches
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
SPARK: A Keyword Search Engine on Relational Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Keyword search on structured and semi-structured data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Efficient approximate search on string collections
Proceedings of the VLDB Endowment
Seaform: search-as-you-type in forms
Proceedings of the VLDB Endowment
Keyword-based search and exploration on databases
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
KEYRY: a keyword-based search engine over relational databases based on a hidden Markov model
ER'11 Proceedings of the 30th international conference on Advances in conceptual modeling: recent developments and new directions
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In many database applications, search is still executed via form based query interfaces, which are then translated into SQL statements to find matching records. Ranking is usually not implemented unless users have explicitly indicated how to rank the matching records, e.g., in the ascending order of year. Often, this approach is neither intuitive nor user friendly (especially with many search fields in a query form). It also requires application developers to design schema-specific query forms and develop specific programs that understand these forms. In this work, we propose to demonstrate the ColumbuScout system that aims at quickly building and deploying a local search engine over one or more large databases. The ColumbuScout system adopts a search-engine-style approach for searches over local databases. It introduces its own indexing structures and storage designs, to improve its overall efficiency and scalability. We will demonstrate that it is simple for application developers to deploy ColumbuScout over any databases, and ColumbuScout is able to support search engine-like types of search over large databases efficiently and effectively.