Location-based top-k term querying over sliding window

Ying Xu; Lisi Chen; Bin Yao; Shuo Shang; Shunzhi Zhu; Kai Zheng; Fang Li

doi:10.1007/978-3-319-68783-4_21

Location-based top-k term querying over sliding window

Ying Xu, Lisi Chen, Bin Yao^*, Shuo Shang^*, Shunzhi Zhu, Kai Zheng, Fang Li

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

26 Citations (Scopus)

Abstract

In part due to the proliferation of GPS-equipped mobile devices, massive svolumes of geo-tagged streaming text messages are becoming available on social media. It is of great interest to discover most frequent nearby terms from such tremendous stream data. In this paper, we present novel indexing, updating, and query processing techniques that are capable of discovering top-k locally popular nearby terms over a sliding window. Specifically, given a query location and a set of geo-tagged messages within a sliding window, we study the problem of searching for the top-k terms by considering both the term frequency and the proximities between the messages containing the term and the query location. We develop a novel and efficient mechanism to solve the problem, including a quad-tree based indexing structure, indexing update technique, and a best-first based searching algorithm. An empirical study is conducted to show that our proposed techniques are efficient and fit for users’ requirements through varying a number of parameters.

Original language	English
Title of host publication	Web Information Systems Engineering – WISE 2017
Subtitle of host publication	18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I
Editors	Lu Chen, Athman Bouguettaya, Andrey Klimenko, Fedor Dzerzhinskiy, Stanislav V. Klimenko, Xiangliang Zhang, Qing Li, Yunjun Gao, Weijia Jia
Publisher	Springer Cham
Pages	299-314
Number of pages	16
Edition	1st
ISBN (Electronic)	9783319687834
ISBN (Print)	9783319687827
DOIs	https://doi.org/10.1007/978-3-319-68783-4_21
Publication status	Published - 1 Oct 2017
Event	18th International Conference on Web Information Systems Engineering, WISE 2017 - Puschino, Russian Federation Duration: 7 Oct 2017 → 11 Oct 2017 https://link.springer.com/book/10.1007/978-3-319-68783-4

Publication series

Name	Lecture Notes in Computer Science
Volume	10569
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	Information Systems and Applications, incl. Internet/Web, and HCI

Name	WISE: International Conference on Web Information Systems Engineering

Conference

Conference	18th International Conference on Web Information Systems Engineering, WISE 2017
Country/Territory	Russian Federation
City	Puschino
Period	7/10/17 → 11/10/17
Internet address	https://link.springer.com/book/10.1007/978-3-319-68783-4

User-Defined Keywords

Location
Term
Top-k

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-68783-4_21

Cite this

Xu, Y., Chen, L., Yao, B., Shang, S., Zhu, S., Zheng, K., & Li, F. (2017). Location-based top-k term querying over sliding window. In L. Chen, A. Bouguettaya, A. Klimenko, F. Dzerzhinskiy, S. V. Klimenko, X. Zhang, Q. Li, Y. Gao, & W. Jia (Eds.), Web Information Systems Engineering – WISE 2017: 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I (1st ed., pp. 299-314). (Lecture Notes in Computer Science; Vol. 10569), (Information Systems and Applications, incl. Internet/Web, and HCI), (WISE: International Conference on Web Information Systems Engineering). Springer Cham. https://doi.org/10.1007/978-3-319-68783-4_21

Xu, Ying ; Chen, Lisi ; Yao, Bin et al. / Location-based top-k term querying over sliding window. Web Information Systems Engineering – WISE 2017: 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I. editor / Lu Chen ; Athman Bouguettaya ; Andrey Klimenko ; Fedor Dzerzhinskiy ; Stanislav V. Klimenko ; Xiangliang Zhang ; Qing Li ; Yunjun Gao ; Weijia Jia. 1st. ed. Springer Cham, 2017. pp. 299-314 (Lecture Notes in Computer Science). (Information Systems and Applications, incl. Internet/Web, and HCI). (WISE: International Conference on Web Information Systems Engineering).

@inproceedings{54f0703ed8254802a23170304eecc7d2,

title = "Location-based top-k term querying over sliding window",

abstract = "In part due to the proliferation of GPS-equipped mobile devices, massive svolumes of geo-tagged streaming text messages are becoming available on social media. It is of great interest to discover most frequent nearby terms from such tremendous stream data. In this paper, we present novel indexing, updating, and query processing techniques that are capable of discovering top-k locally popular nearby terms over a sliding window. Specifically, given a query location and a set of geo-tagged messages within a sliding window, we study the problem of searching for the top-k terms by considering both the term frequency and the proximities between the messages containing the term and the query location. We develop a novel and efficient mechanism to solve the problem, including a quad-tree based indexing structure, indexing update technique, and a best-first based searching algorithm. An empirical study is conducted to show that our proposed techniques are efficient and fit for users{\textquoteright} requirements through varying a number of parameters.",

keywords = "Location, Term, Top-k",

author = "Ying Xu and Lisi Chen and Bin Yao and Shuo Shang and Shunzhi Zhu and Kai Zheng and Fang Li",

note = "This work was supported by the NSFC (U1636210, 61373156, 91438121 and 61672351), the National Basic Research Program (973 Program, No. 2015CB352403), the National Key Research and Development Program of China (2016YFB0700502), the Scientific Innovation Act of STCSM (15JC1402400) and the Microsoft Research Asia. Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG; 18th International Conference on Web Information Systems Engineering, WISE 2017 ; Conference date: 07-10-2017 Through 11-10-2017",

year = "2017",

month = oct,

day = "1",

doi = "10.1007/978-3-319-68783-4_21",

language = "English",

isbn = "9783319687827",

series = "Lecture Notes in Computer Science",

publisher = "Springer Cham",

pages = "299--314",

editor = "Lu Chen and Athman Bouguettaya and Andrey Klimenko and Fedor Dzerzhinskiy and Klimenko, {Stanislav V.} and Xiangliang Zhang and Qing Li and Yunjun Gao and Weijia Jia",

booktitle = "Web Information Systems Engineering – WISE 2017",

edition = "1st",

url = "https://link.springer.com/book/10.1007/978-3-319-68783-4",

}

Xu, Y, Chen, L, Yao, B, Shang, S, Zhu, S, Zheng, K & Li, F 2017, Location-based top-k term querying over sliding window. in L Chen, A Bouguettaya, A Klimenko, F Dzerzhinskiy, SV Klimenko, X Zhang, Q Li, Y Gao & W Jia (eds), Web Information Systems Engineering – WISE 2017: 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I. 1st edn, Lecture Notes in Computer Science, vol. 10569, Information Systems and Applications, incl. Internet/Web, and HCI, WISE: International Conference on Web Information Systems Engineering, Springer Cham, pp. 299-314, 18th International Conference on Web Information Systems Engineering, WISE 2017, Puschino, Russian Federation, 7/10/17. https://doi.org/10.1007/978-3-319-68783-4_21

Location-based top-k term querying over sliding window. / Xu, Ying; Chen, Lisi; Yao, Bin et al.
Web Information Systems Engineering – WISE 2017: 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I. ed. / Lu Chen; Athman Bouguettaya; Andrey Klimenko; Fedor Dzerzhinskiy; Stanislav V. Klimenko; Xiangliang Zhang; Qing Li; Yunjun Gao; Weijia Jia. 1st. ed. Springer Cham, 2017. p. 299-314 (Lecture Notes in Computer Science; Vol. 10569), (Information Systems and Applications, incl. Internet/Web, and HCI), (WISE: International Conference on Web Information Systems Engineering).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Location-based top-k term querying over sliding window

AU - Xu, Ying

AU - Chen, Lisi

AU - Yao, Bin

AU - Shang, Shuo

AU - Zhu, Shunzhi

AU - Zheng, Kai

AU - Li, Fang

N1 - This work was supported by the NSFC (U1636210, 61373156, 91438121 and 61672351), the National Basic Research Program (973 Program, No. 2015CB352403), the National Key Research and Development Program of China (2016YFB0700502), the Scientific Innovation Act of STCSM (15JC1402400) and the Microsoft Research Asia. Publisher Copyright: © 2017, Springer International Publishing AG

PY - 2017/10/1

Y1 - 2017/10/1

N2 - In part due to the proliferation of GPS-equipped mobile devices, massive svolumes of geo-tagged streaming text messages are becoming available on social media. It is of great interest to discover most frequent nearby terms from such tremendous stream data. In this paper, we present novel indexing, updating, and query processing techniques that are capable of discovering top-k locally popular nearby terms over a sliding window. Specifically, given a query location and a set of geo-tagged messages within a sliding window, we study the problem of searching for the top-k terms by considering both the term frequency and the proximities between the messages containing the term and the query location. We develop a novel and efficient mechanism to solve the problem, including a quad-tree based indexing structure, indexing update technique, and a best-first based searching algorithm. An empirical study is conducted to show that our proposed techniques are efficient and fit for users’ requirements through varying a number of parameters.

AB - In part due to the proliferation of GPS-equipped mobile devices, massive svolumes of geo-tagged streaming text messages are becoming available on social media. It is of great interest to discover most frequent nearby terms from such tremendous stream data. In this paper, we present novel indexing, updating, and query processing techniques that are capable of discovering top-k locally popular nearby terms over a sliding window. Specifically, given a query location and a set of geo-tagged messages within a sliding window, we study the problem of searching for the top-k terms by considering both the term frequency and the proximities between the messages containing the term and the query location. We develop a novel and efficient mechanism to solve the problem, including a quad-tree based indexing structure, indexing update technique, and a best-first based searching algorithm. An empirical study is conducted to show that our proposed techniques are efficient and fit for users’ requirements through varying a number of parameters.

KW - Location

KW - Term

KW - Top-k

UR - http://www.scopus.com/inward/record.url?scp=85031416677&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-68783-4_21

DO - 10.1007/978-3-319-68783-4_21

M3 - Conference proceeding

AN - SCOPUS:85031416677

SN - 9783319687827

T3 - Lecture Notes in Computer Science

SP - 299

EP - 314

BT - Web Information Systems Engineering – WISE 2017

A2 - Chen, Lu

A2 - Bouguettaya, Athman

A2 - Klimenko, Andrey

A2 - Dzerzhinskiy, Fedor

A2 - Klimenko, Stanislav V.

A2 - Zhang, Xiangliang

A2 - Li, Qing

A2 - Gao, Yunjun

A2 - Jia, Weijia

PB - Springer Cham

T2 - 18th International Conference on Web Information Systems Engineering, WISE 2017

Y2 - 7 October 2017 through 11 October 2017

ER -

Xu Y, Chen L, Yao B, Shang S, Zhu S, Zheng K et al. Location-based top-k term querying over sliding window. In Chen L, Bouguettaya A, Klimenko A, Dzerzhinskiy F, Klimenko SV, Zhang X, Li Q, Gao Y, Jia W, editors, Web Information Systems Engineering – WISE 2017: 18th International Conference, Puschino, Russia, October 7-11, 2017, Proceedings, Part I. 1st ed. Springer Cham. 2017. p. 299-314. (Lecture Notes in Computer Science). (Information Systems and Applications, incl. Internet/Web, and HCI). (WISE: International Conference on Web Information Systems Engineering). doi: 10.1007/978-3-319-68783-4_21

Location-based top-k term querying over sliding window

Abstract

Publication series

Conference

User-Defined Keywords

UN SDGs

Access to Document

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