Answering Keyword Queries on XML Using Materialized Views

Authors:
Ziyang Liu;Yi Chen
Affiliations:
Department of Computer Science and Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ 85287 - 8809, USA. ziyang.liu@asu.edu;Department of Computer Science and Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ 85287 - 8809, USA. yi@asu.edu
Venue:
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Year:
2008

Citing 0
Cited 9

Reasoning and identifying relevant matches for XML keyword search

Proceedings of the VLDB Endowment
Retrieving meaningful relaxed tightest fragments for XML keyword search

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Keyword search on structured and semi-structured data

Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
LCA-based selection for XML document collections

Proceedings of the 19th international conference on World wide web
A survey on XML keyword search

APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Processing keyword search on XML: a survey

World Wide Web
MAXLCA: a new query semantic model for XML keyword search

Journal of Web Engineering
Exploiting and Maintaining Materialized Views for XML Keyword Queries

ACM Transactions on Internet Technology (TOIT)
DMVI: a dynamic materialized view index for efficiently discovering usable views for progressive queries

CASCON '12 Proceedings of the 2012 Conference of the Center for Advanced Studies on Collaborative Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Answering queries using materialized views has been well studied in the context of structured queries and has shown significant performance benefits. Despite the popularity of keyword search over XML data, it is an open problem whether materialized views can be leveraged for query evaluation. In this paper, we investigate this problem and present techniques for answering keyword queries using a minimal number of materialized views. Experimental evaluation demonstrates the efficiency of the proposed techniques.