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Efficient event detection by exploiting crowds

Published: 29 June 2013 Publication History

Abstract

Encouraging users to participate in community-based sensing and collection for the purpose of identifying events of interest for the community has found important applications in the recent years in a wide variety of domains including entertainment, transportation and environmental monitoring. One important challenge in these settings is how significant events can be detected by exploiting the data sensed, gathered and shared by the crowd, while respecting the resource costs. In this paper we investigate the use of dynamic clustering and sampling techniques that allow us to significantly reduce utilization costs by clustering low-level streams of events based on their geo-spatial locations and then selectively retrieving the ones that depict the highest interest. Our experimental results illustrate that our approach is practical, efficient and depicts good performance.

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  • (2016)Using Human Social Sensors for Robust Event Location Detection2016 International Conference on Distributed Computing in Sensor Systems (DCOSS)10.1109/DCOSS.2016.29(105-107)Online publication date: May-2016
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cover image ACM Conferences
DEBS '13: Proceedings of the 7th ACM international conference on Distributed event-based systems
June 2013
360 pages
ISBN:9781450317580
DOI:10.1145/2488222
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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Publication History

Published: 29 June 2013

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Author Tags

  1. clustering
  2. community-based participatory sensing
  3. distributed systems
  4. event detection
  5. mobile systems
  6. sampling

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DEBS '13

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DEBS '13 Paper Acceptance Rate 16 of 58 submissions, 28%;
Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

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  • (2019)DG2CEP: a near real-time on-line algorithm for detecting spatial clusters large data streams through complex event processingJournal of Internet Services and Applications10.1186/s13174-019-0107-x10:1Online publication date: 15-Apr-2019
  • (2016)Using Human Social Sensors for Robust Event Location Detection2016 International Conference on Distributed Computing in Sensor Systems (DCOSS)10.1109/DCOSS.2016.29(105-107)Online publication date: May-2016
  • (2016)A Fast and Efficient Entity Resolution Approach for Preserving Privacy in Mobile Data2016 IEEE International Congress on Big Data (BigData Congress)10.1109/BigDataCongress.2016.29(173-180)Online publication date: Jun-2016
  • (2016)Detecting Events in Online Social Networks: Definitions, Trends and ChallengesSolving Large Scale Learning Tasks. Challenges and Algorithms10.1007/978-3-319-41706-6_2(42-84)Online publication date: 3-Jul-2016
  • (2015)Personalized Event Recommendations Using Social NetworksProceedings of the 2015 16th IEEE International Conference on Mobile Data Management - Volume 0110.1109/MDM.2015.62(84-93)Online publication date: 15-Jun-2015
  • (2014)Understanding event attendance through analysis of human crowd behavior in social networksProceedings of the 8th ACM International Conference on Distributed Event-Based Systems10.1145/2611286.2611324(322-325)Online publication date: 26-May-2014
  • (2014)Exploiting Heterogeneous Data SourcesProceedings of the First International Conference on Applied Algorithms - Volume 832110.1007/978-3-319-04126-1_3(29-36)Online publication date: 13-Jan-2014
  • (2013)An architecture for detecting events in real-time using massive heterogeneous data sourcesProceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications10.1145/2501221.2501235(103-109)Online publication date: 11-Aug-2013

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