Efficient top K temporal spatial keyword search

Chengyuan Zhang; Lei Zhu; Weiren Yu; Jun Long; Fang Huang; Hongbo Zhao

doi:10.1007/978-3-030-04503-6_7

Efficient top K temporal spatial keyword search

Chengyuan Zhang, Lei Zhu, Weiren Yu, Jun Long^*, Fang Huang, Hongbo Zhao

^*Corresponding author for this work

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in many emerging applications such as location based services and social networks. Due to their importance, a large body of work has focused on efficiently computing various spatial keyword queries. In this paper, we study the top-k temporal spatial keyword query which considers three important constraints during the search including time, spatial proximity and textual relevance. A novel index structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web objects with high rates. Base on SSG-tree an efficient algorithm is developed to support top-k temporal spatial keyword query. We show via extensive experimentation with real spatial databases that our method has increased performance over alternate techniques.

Original language	English
Title of host publication	Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers
Publisher	Springer
Pages	80-92
Number of pages	13
ISBN (Print)	9783030045029
DOIs	https://doi.org/10.1007/978-3-030-04503-6_7
Publication status	Published - 2018
Event	22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia Duration: 3 Jun 2018 → 3 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11154 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018
Country/Territory	Australia
City	Melbourne
Period	3/06/18 → 3/06/18

Bibliographical note

Access to Document

10.1007/978-3-030-04503-6_7

Efficient Top K Temporal Spatial Keyword Search
© Springer Nature Switzerland AG 2018
Accepted author manuscript, 201 KB

https://arxiv.org/abs/1805.02009

Cite this

Zhang, C., Zhu, L., Yu, W., Long, J., Huang, F., & Zhao, H. (2018). Efficient top K temporal spatial keyword search. In Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers (pp. 80-92). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11154 LNAI). Springer. https://doi.org/10.1007/978-3-030-04503-6_7

Zhang, Chengyuan ; Zhu, Lei ; Yu, Weiren et al. / Efficient top K temporal spatial keyword search. Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers. Springer, 2018. pp. 80-92 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Efficient top K temporal spatial keyword search",

abstract = "Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in many emerging applications such as location based services and social networks. Due to their importance, a large body of work has focused on efficiently computing various spatial keyword queries. In this paper, we study the top-k temporal spatial keyword query which considers three important constraints during the search including time, spatial proximity and textual relevance. A novel index structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web objects with high rates. Base on SSG-tree an efficient algorithm is developed to support top-k temporal spatial keyword query. We show via extensive experimentation with real spatial databases that our method has increased performance over alternate techniques.",

author = "Chengyuan Zhang and Lei Zhu and Weiren Yu and Jun Long and Fang Huang and Hongbo Zhao",

note = "{\textcopyright} Springer Nature Switzerland AG 2018; 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018 ; Conference date: 03-06-2018 Through 03-06-2018",

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Zhang, C, Zhu, L, Yu, W, Long, J, Huang, F & Zhao, H 2018, Efficient top K temporal spatial keyword search. in Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11154 LNAI, Springer, pp. 80-92, 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, Australia, 3/06/18. https://doi.org/10.1007/978-3-030-04503-6_7

Efficient top K temporal spatial keyword search. / Zhang, Chengyuan; Zhu, Lei; Yu, Weiren et al.
Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers. Springer, 2018. p. 80-92 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11154 LNAI).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Efficient top K temporal spatial keyword search

AU - Zhang, Chengyuan

AU - Zhu, Lei

AU - Yu, Weiren

AU - Long, Jun

AU - Huang, Fang

AU - Zhao, Hongbo

PY - 2018

Y1 - 2018

N2 - Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in many emerging applications such as location based services and social networks. Due to their importance, a large body of work has focused on efficiently computing various spatial keyword queries. In this paper, we study the top-k temporal spatial keyword query which considers three important constraints during the search including time, spatial proximity and textual relevance. A novel index structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web objects with high rates. Base on SSG-tree an efficient algorithm is developed to support top-k temporal spatial keyword query. We show via extensive experimentation with real spatial databases that our method has increased performance over alternate techniques.

AB - Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in many emerging applications such as location based services and social networks. Due to their importance, a large body of work has focused on efficiently computing various spatial keyword queries. In this paper, we study the top-k temporal spatial keyword query which considers three important constraints during the search including time, spatial proximity and textual relevance. A novel index structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web objects with high rates. Base on SSG-tree an efficient algorithm is developed to support top-k temporal spatial keyword query. We show via extensive experimentation with real spatial databases that our method has increased performance over alternate techniques.

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U2 - 10.1007/978-3-030-04503-6_7

DO - 10.1007/978-3-030-04503-6_7

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers

PB - Springer

T2 - 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018

Y2 - 3 June 2018 through 3 June 2018

ER -

Zhang C, Zhu L, Yu W, Long J, Huang F, Zhao H. Efficient top K temporal spatial keyword search. In Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers. Springer. 2018. p. 80-92. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2018 Nov 21. doi: 10.1007/978-3-030-04503-6_7

Efficient top K temporal spatial keyword search

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