A novel keyword search paradigm in relational databases: Object summaries
Data & Knowledge Engineering
Size-l object summaries for relational keyword search
Proceedings of the VLDB Endowment
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A previously proposed Keyword Search paradigm produces, as a query result, a ranked list of Object Summaries (OSs); each OS summarizes all data held in a relational database about a particular Data Subject (DS). This paper further investigates the ranking of OSs and their tuples as to facilitate (1) the top-k ranking of OSs and also (2) the generation of partial size-l OSs (i.e. comprised of the l most important tuples). Therefore, a global Importance score for each tuple of the database (denoted as Im(ti)) is investigated and quantified. For this purpose, ValueRank (an extension of ObjectRank) is introduced which facilitates the estimation of scores for arbitrary databases (in contrast to PageRank-style techniques that are only effective on bibliographic databases). In addition, a variation of Combined functions are investigated for assigning an Importance score to an OS (denoted as Im(OS)) and a local Importance score of their tuples (denoted as Im(OS, ti)). Preliminary experimental evaluation on DBLP and Northwind Databases is presented.