Size-l Object Summaries for Relational Keyword Search
Georgios J. Fakas, Zhi Cai, Nikos Mamoulis

TL;DR
This paper introduces methods for retrieving concise Object Summaries of size-l in relational databases, improving user experience by providing meaningful, compact data summaries efficiently.
Contribution
It defines size-l Object Summaries and proposes three algorithms for their efficient generation, along with an optimal but exponential-time approach.
Findings
Algorithms are effective and efficient based on experiments
Proposed methods produce meaningful concise summaries
Experimental results on DBLP and TPC-H databases verify performance
Abstract
A previously proposed keyword search paradigm produces, as a query result, a ranked list of Object Summaries (OSs). An OS is a tree structure of related tuples that summarizes all data held in a relational database about a particular Data Subject (DS). However, some of these OSs are very large in size and therefore unfriendly to users that initially prefer synoptic information before proceeding to more comprehensive information about a particular DS. In this paper, we investigate the effective and efficient retrieval of concise and informative OSs. We argue that a good size-l OS should be a stand-alone and meaningful synopsis of the most important information about the particular DS. More precisely, we define a size-l OS as a partial OS composed of l important tuples. We propose three algorithms for the efficient generation of size-l OSs (in addition to the optimal approach which…
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Taxonomy
TopicsData Management and Algorithms · Advanced Database Systems and Queries · Web Data Mining and Analysis
