research-article

Geo-Social Co-location Mining

Authors:

Michael Weiler,

Klaus Arthur Schmid,

Nikos Mamoulis,

Matthias RenzAuthors Info & Claims

GeoRich'15: Second International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data

Pages 19 - 24

https://doi.org/10.1145/2786006.2786010

Published: 31 May 2015 Publication History

Abstract

Modern technology to capture geo-spatial information produces a huge flood of geo-spatial and geo-spatio-temporal data with a new user mentality of utilizing this technology to voluntarily share information. This location information, enriched with social information, is a new source to discover new and useful knowledge. This work introduces geo-social co-location mining, the problem of finding social groups that are frequently found at the same location. This problem has applications in social sciences, allowing to research interactions between social groups and permitting social-link prediction. It can be divided into two sub-problems. The first sub-problem of finding spatial co-location instances, requires to properly address the inherent uncertainty in geo-social network data, which is a consequence of generally very sparse check-in data, and thus very sparse trajectory information. For this purpose, we propose a probabilistic model to estimate the probability of a user to be located at a given location at a given time, creating the notion of probabilistic co-locations. The second sub-problem of mining the resulting probabilistic co-location instances requires efficient methods for large databases having a high degree of uncertainty. Our approach solves this problem by extending solutions for probabilistic frequent itemset mining. Our experimental evaluation performed on real (but anonymized) geo-social network data shows the high efficiency of our approach, and its ability to find new social interactions.

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Cited By

Qiu GTang GLi CLuo LGuo DShen Y(2024)A Complete and Comprehensive Semantic Perception of Mobile Traveling for Mobile Communication ServicesIEEE Internet of Things Journal10.1109/JIOT.2023.330747811:3(5467-5490)Online publication date: 1-Feb-2024
https://doi.org/10.1109/JIOT.2023.3307478
Wang WWu JNepal SdaSilva AHedlund EMurphy ERogers CHuckins J(2021)On the Transition of Social Interaction from In-Person to Online: Predicting Changes in Social Media Usage of College Students during the COVID-19 Pandemic based on Pre-COVID-19 On-Campus ColocationProceedings of the 2021 International Conference on Multimodal Interaction10.1145/3462244.3479888(425-434)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3462244.3479888
Burini FCortesi NPsaila G(2020)From Data to Rhizomes: Applying a Geographical Concept to Understand the Mobility of Tourists from Geo-Located TweetsInformatics10.3390/informatics80100018:1(1)Online publication date: 24-Dec-2020
https://doi.org/10.3390/informatics8010001
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Index Terms

Geo-Social Co-location Mining
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Data management systems
    1. Database management system engines
  2. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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Published In

cover image ACM Conferences

GeoRich'15: Second International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data

May 2015

44 pages

ISBN:9781450336680

DOI:10.1145/2786006

General Chairs:
Kyriakos Mouratidis
Singapore Management University, Singapore
,
Matthias Renz
Ludwig-Maximilians University Munich, Germany
,
Program Chairs:
Tobias Emrich
Ludwig-Maximilians University Munich, Germany
,
Andreas Züfle
Ludwig-Maximilians University Munich, Germany
,
Krzysztof Janowicz
University of California, Santa Barbara, USA

Copyright © 2015 ACM.

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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: 31 May 2015

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SIGMOD/PODS'15

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SIGMOD

SIGMOD/PODS'15: International Conference on Management of Data

May 31 - June 4, 2015

VIC, Melbourne, Australia

Acceptance Rates

GeoRich'15 Paper Acceptance Rate 5 of 13 submissions, 38%;

Overall Acceptance Rate 25 of 50 submissions, 50%

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Cited By

Qiu GTang GLi CLuo LGuo DShen Y(2024)A Complete and Comprehensive Semantic Perception of Mobile Traveling for Mobile Communication ServicesIEEE Internet of Things Journal10.1109/JIOT.2023.330747811:3(5467-5490)Online publication date: 1-Feb-2024
https://doi.org/10.1109/JIOT.2023.3307478
Wang WWu JNepal SdaSilva AHedlund EMurphy ERogers CHuckins J(2021)On the Transition of Social Interaction from In-Person to Online: Predicting Changes in Social Media Usage of College Students during the COVID-19 Pandemic based on Pre-COVID-19 On-Campus ColocationProceedings of the 2021 International Conference on Multimodal Interaction10.1145/3462244.3479888(425-434)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3462244.3479888
Burini FCortesi NPsaila G(2020)From Data to Rhizomes: Applying a Geographical Concept to Understand the Mobility of Tourists from Geo-Located TweetsInformatics10.3390/informatics80100018:1(1)Online publication date: 24-Dec-2020
https://doi.org/10.3390/informatics8010001
Yoo JShao WBinzani K(2020)Spatial Association Pattern Mining Using In-Memory Computational FrameworkBig Data – BigData 202010.1007/978-3-030-59612-5_17(239-246)Online publication date: 18-Sep-2020
https://doi.org/10.1007/978-3-030-59612-5_17
Yoo JBow M(2019)A framework for generating condensed co-location sets from spatial databasesIntelligent Data Analysis10.3233/IDA-17375223:2(333-355)Online publication date: 4-Apr-2019
https://doi.org/10.3233/IDA-173752
Oh B(2019)Parallel Algorithm for Spatial Data Mining Using CUDAJOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE10.14801/JAITC.2019.9.2.899:2(89-97)Online publication date: 31-Dec-2019
https://doi.org/10.14801/JAITC.2019.9.2.89
Isaj SPedersen T(2019)Seed-Driven Geo-Social Data ExtractionProceedings of the 16th International Symposium on Spatial and Temporal Databases10.1145/3340964.3340973(11-20)Online publication date: 19-Aug-2019
https://dl.acm.org/doi/10.1145/3340964.3340973
Ge YYao ZLi H(2019)Computing Co-location Patterns in Spatial Data with Extended Objects: a Scalable Buffer-based ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2930598(1-1)Online publication date: 2019
https://doi.org/10.1109/TKDE.2019.2930598
Yoo JBoulware DKimmey D(2019)Parallel co-location mining with MapReduce and NoSQL systemsKnowledge and Information Systems10.1007/s10115-019-01381-yOnline publication date: 21-Aug-2019
https://doi.org/10.1007/s10115-019-01381-y
吴萍(2018)Research on Spatial Co-Locations Mining: A SurveyComputer Science and Application10.12677/CSA.2018.8303808:03(328-338)Online publication date: 2018
https://doi.org/10.12677/CSA.2018.83038
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