Ranking of Object Summaries

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(t_i)) 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, t_i)). Preliminary experimental evaluation on DBLP and Northwind Databases is presented.