research-article

On socio-spatial group query for location-based social networks

Authors:

De-Nian Yang,

Chih-Ya Shen,

Wang-Chien Lee,

Ming-Syan ChenAuthors Info & Claims

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 949 - 957

https://doi.org/10.1145/2339530.2339679

Published: 12 August 2012 Publication History

Get Access

Abstract

Challenges faced in organizing impromptu activities are the requirements of making timely invitations in accordance with the locations of candidate attendees and the social relationship among them. It is desirable to find a group of attendees close to a rally point and ensure that the selected attendees have a good social relationship to create a good atmosphere in the activity. Therefore, this paper proposes Socio-Spatial Group Query (SSGQ) to select a group of nearby attendees with tight social relation. Efficient processing of SSGQ is very challenging due to the tradeoff in the spatial and social domains. We show that the problem is NP-hard via a proof and design an efficient algorithm SSGSelect, which includes effective pruning techniques to reduce the running time for finding the optimal solution. We also propose a new index structure, Social R-Tree to further improve the efficiency. User study and experimental results demonstrate that SSGSelect significantly outperforms manual coordination in both solution quality and efficiency.

Supplementary Material

JPG File (311b_t_talk_8.jpg)

Download
17.48 KB

MP4 File (311b_t_talk_8.mp4)

Download
396.39 MB

References

[1]

N. Roussopoulos, S. Kelley and F. Vincent. Nearest Neighbor Queries. SIGMOD, 1995.

Digital Library

Google Scholar

[2]

T. Lappas, K. Liu, and E. Terzi. Finding a Team of Expertsin Social Networks. KDD, 2009.

Digital Library

Google Scholar

[3]

C.-T. Li and M.-K. Shan. Team Formation for Generalized Tasksin Expertise Social Networks. SocialCom, 2010.

Digital Library

Google Scholar

[4]

M. Sozio and A. Gionis. The Community-Search Problem and Howto Plan a Successful Cocktail Party. KDD, 2010.

Digital Library

Google Scholar

[5]

D. Papadias, Q. Shen, Y. Tao and K. Mouratidis. Group Nearest Neighbor Queries. ICDE, 2004.

Digital Library

Google Scholar

[6]

Y. Tao, D. Papadias and Q. Shen. Continuous Nearest NeighborSearch. VLDB, 2002.

Digital Library

Google Scholar

[7]

D.-N. Yang, Y.-L. Chen, W.-C. Lee and M.-S. Chen. OnSocial-Temporal Group Query with Acquaintance Constraint. VLDB, 2011.

Digital Library

Google Scholar

[8]

Y. Gao, B. Zheng, W.-C. Lee, G. Chen. Continuous VisibleNearest Neighbor Queries. EDBT, 2009.

Digital Library

Google Scholar

[9]

Y. Gao and B. Zheng. Continuous Obstructed Nearest NeighborQueries in Spatial Databases. SIGMOD, 2009.

Digital Library

Google Scholar

[10]

G. Hjaltason and H. Samet. Distance Browsing in SpatialDatabases. TODS, 1999.

Digital Library

Google Scholar

[11]

M. Ye, P. Yin, W.-C. Lee and D.-L. Lee. Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation. SIGIR, 2011.

Digital Library

Google Scholar

Cited By

View all

Haldar NLi JAkhtar NJia YMian A(2024)Co-Engaged Location Group Search in Location-Based Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3327405(1-16)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3327405
Chang CLu CTeng SChang MHo YShen C(2024)Maximizing $(k,L)$-Core With Edge Augmentation in Multilayer GraphsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333209111:3(3931-3943)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3332091
Chang Y(2023)Kernel-wise difference minimization for convolutional neural network compression in metaverseFrontiers in Big Data10.3389/fdata.2023.12003826Online publication date: 4-Aug-2023
https://doi.org/10.3389/fdata.2023.1200382
Show More Cited By

Index Terms

On socio-spatial group query for location-based social networks
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

Recommendations

Spatial-aware interest group queries in location-based social networks
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Location-based social networks, such as Foursquare and Facebook Places, are bridging the gap between the physical world and online social networking services through acquired user locations. Some social networks released check-in services that allow ...
Query Processing in Location-Based Social Networks
WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

The widespread proliferation of location-acquisition techniques and GPS-embedded mobile devices resulted in the generation of geo-tagged data at unprecedented scale and have essentially enhanced the user experience in location-based services associated ...
Socio-spatial affiliation networks

We model a location-based social network as an affiliation network, where the affiliations are the locations visited by the users.We identify clear signs of location-based homophily.The type of common locations between users encodes more information ...

Comments

Information & Contributors

Information

Published In

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

1616 pages

ISBN:9781450314626

DOI:10.1145/2339530

General Chair:
Qiang Yang
Hong Kong University of Science and Technology
,
Program Chairs:
Deepak Agarwal
LinkedIn
,
Jian Pei
Simon Fraser University

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

KDD '12

Sponsor:

KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 16, 2012

Beijing, China

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

102
Total Citations
View Citations
1,308
Total Downloads

Downloads (Last 12 months)35
Downloads (Last 6 weeks)2

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Haldar NLi JAkhtar NJia YMian A(2024)Co-Engaged Location Group Search in Location-Based Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3327405(1-16)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3327405
Chang CLu CTeng SChang MHo YShen C(2024)Maximizing $(k,L)$-Core With Edge Augmentation in Multilayer GraphsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333209111:3(3931-3943)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3332091
Chang Y(2023)Kernel-wise difference minimization for convolutional neural network compression in metaverseFrontiers in Big Data10.3389/fdata.2023.12003826Online publication date: 4-Aug-2023
https://doi.org/10.3389/fdata.2023.1200382
Hsu BYeh LChang MShen C(2023)Willingness Maximization for Ego Network Data Extraction in Multiple Online Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3207150(1-14)Online publication date: 2023
https://doi.org/10.1109/TKDE.2022.3207150
Yang JShen CChang MHo YLu C(2023)Similarity-Aware Sampling for Machine Learning-Based Goal-Oriented Subgraph ExtractionICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10279825(5589-5594)Online publication date: 28-May-2023
https://doi.org/10.1109/ICC45041.2023.10279825
Zong CDong ZYang XWang BQiu TZhu H(2023)Efficiently Answering Why-Not Questions on�Radius-Bounded k-Core SearchesDatabase Systems for Advanced Applications10.1007/978-3-031-30675-4_7(93-109)Online publication date: 15-Apr-2023
https://doi.org/10.1007/978-3-031-30675-4_7
Liu JZhong Y(2022)Time-Weighted Community Search Based on InterestApplied Sciences10.3390/app1214707712:14(7077)Online publication date: 13-Jul-2022
https://doi.org/10.3390/app12147077
Shen CKo SLee GLee WYang D(2022)Density Personalized Group QueryProceedings of the VLDB Endowment10.14778/3574245.357424916:4(615-628)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.14778/3574245.3574249
Liu GLi LLiu GWu X(2022)Social Group Query Based on Multi-Fuzzy-Constrained Strong SimulationACM Transactions on Knowledge Discovery from Data10.1145/348164016:3(1-27)Online publication date: 30-Jun-2022
https://dl.acm.org/doi/10.1145/3481640
Zhu HLiu WYin JZheng LHuang XXu JLee W(2022)Continuous Geo-Social Group Monitoring in Dynamic LBSNsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3218844(1-14)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3218844
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Spatial-aware interest group queries in location-based social networks

Query Processing in Location-Based Social Networks

Socio-spatial affiliation networks