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In recent years, there has been a lot of interest in the field of keywordquerying relational databases. A variety of systems such as DBXplorer [ACD02],Discover [HP02] and ObjectRank [BHP04] have been proposed. Another such systemis BANKS, which enables data and schema browsing together with keyword-basedsearch for relational databases. It models tuples as nodes in a graph,connected by links induced by foreign key and other relationships. The size ofthe database graph that BANKS uses is proportional to the sum of the number ofnodes and edges in the graph. Systems such as SPIN, which search on PersonalInformation Networks and use BANKS as the backend, maintain a lot ofinformation about the users' data. Since these systems run on the userworkstation which have other demands of memory, such a heavy use of memory isunreasonable and if possible, should be avoided. In order to alleviate thisproblem, we introduce EMBANKS (acronym for External Memory BANKS), a frameworkfor an optimized disk-based BANKS system. The complexity of this frameworkposes many questions, some of which we try to answer in this thesis. Wedemonstrate that the cluster representation proposed in EMBANKS enablesin-memory processing of very large database graphs. We also present detailedexperiments that show that EMBANKS can significantly reduce database load timeand query execution times when compared to the original BANKS algorithms.

EMBANKS: Towards Disk Based Algorithms For Keyword-Search In Structured Databases