research-article

Efficient keyword search on large tree structured datasets

Authors:

Aggeliki Dimitriou,

Dimitri TheodoratosAuthors Info & Claims

KEYS '12: Proceedings of the Third International Workshop on Keyword Search on Structured Data

Pages 63 - 74

https://doi.org/10.1145/2254736.2254749

Published: 20 May 2012 Publication History

Abstract

Keyword search is the most popular paradigm for querying XML data on the web. In this context, three challenging problems are (a) to avoid missing useful results in the answer set, (b) to rank the results with respect to some relevance criterion and (c) to design algorithms that can efficiently compute the results on large datasets.

In this paper, we present a novel multi-stack based algorithm that returns as an answer to a keyword query all the results ranked on their size. Our algorithm exploits a lattice of stacks each corresponding to a partition of the keyword set of the query. This feature empowers a linear time performance on the size of the input data for a given number of query keywords. As a result, our algorithm can run efficiently on large input data for several keywords. We also present a variation of our algorithm which accounts for infrequent keywords in the query and show that it can significantly improve the execution time. An extensive experimental evaluation of our approach confirms the theoretical analysis, and shows that it scales smoothly when the size of the input data and the number of input keywords increases.

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Cited By

Cai ZKalamatianos GFakas GMamoulis NPapadias D(2020)Diversified spatial keyword search on RDF dataThe VLDB Journal10.1007/s00778-020-00610-z29:5(1171-1189)Online publication date: 12-Mar-2020
https://doi.org/10.1007/s00778-020-00610-z
Fakas GCai YCai ZMamoulis N(2018)Thematic ranking of object summaries for keyword searchData & Knowledge Engineering10.1016/j.datak.2017.08.002113(1-17)Online publication date: Jan-2018
https://doi.org/10.1016/j.datak.2017.08.002
Dimitriou ATheodoratos DSellis T(2015)Top-k-size keyword search on tree structured dataInformation Systems10.1016/j.is.2014.07.00247:C(178-193)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.is.2014.07.002
Show More Cited By

Index Terms

Efficient keyword search on large tree structured datasets
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
    2. Information retrieval query processing

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Published In

cover image ACM Conferences

KEYS '12: Proceedings of the Third International Workshop on Keyword Search on Structured Data

May 2012

78 pages

ISBN:9781450311984

DOI:10.1145/2254736

General Chairs:
Ling Tok Wang
National University of Singapore, Singapore
,
Ge Yu
Northeastern University, China
,
Jiaheng Lu
Renmin University of China, China
,
Wei Wang
The University of New South Wales, Australia

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 May 2012

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SIGMOD/PODS '12

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SIGMOD

SIGMOD/PODS '12: International Conference on Management of Data

May 20, 2012

Arizona, Scottsdale

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Cited By

Cai ZKalamatianos GFakas GMamoulis NPapadias D(2020)Diversified spatial keyword search on RDF dataThe VLDB Journal10.1007/s00778-020-00610-z29:5(1171-1189)Online publication date: 12-Mar-2020
https://doi.org/10.1007/s00778-020-00610-z
Fakas GCai YCai ZMamoulis N(2018)Thematic ranking of object summaries for keyword searchData & Knowledge Engineering10.1016/j.datak.2017.08.002113(1-17)Online publication date: Jan-2018
https://doi.org/10.1016/j.datak.2017.08.002
Dimitriou ATheodoratos DSellis T(2015)Top-k-size keyword search on tree structured dataInformation Systems10.1016/j.is.2014.07.00247:C(178-193)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.is.2014.07.002
Aksoy CDimitriou ATheodoratos D(2015)Reasoning with patterns to effectively answer XML keyword queriesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-015-0384-324:3(441-465)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s00778-015-0384-3
Dass AAksoy CDimitriou ATheodoratos D(2014)Exploiting Semantic Result Clustering to Support Keyword Search on Linked DataWeb Information Systems Engineering – WISE 201410.1007/978-3-319-11749-2_34(448-463)Online publication date: 2014
https://doi.org/10.1007/978-3-319-11749-2_34
Aksoy CDimitriou ATheodoratos DWu X(2013)XReason: A Semantic Approach That Reasons with Patterns to Answer XML Keyword QueriesDatabase Systems for Advanced Applications10.1007/978-3-642-37487-6_24(299-314)Online publication date: 2013
https://doi.org/10.1007/978-3-642-37487-6_24

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