Size-l Object Summaries for Relational Keyword Search

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 requires exponential time). Experimental evaluation on DBLP and TPC-H databases verifies the effectiveness and efficiency of our approach.