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Demo: Distilling likely truth from noisy streaming data with Apollo

Published: 01 November 2011 Publication History

Abstract

At CPSWeek 2011, the authors presented a demonstration of Apollo, a fact-finder for participatory sensing that ranks archived human-centric and sensor data by credibility. The current demonstration significantly extends our previous work by allowing Apollo to operate on live streaming data; in this case, live Twitter feeds. As the role of humans as sensors increases in emerging sensing applications, a principled approach becomes necessary to address the problem of ascertaining the veracity of sources and observations made by them. Participatory and social sensing applications may use potentially unreliable or unverified sources, such as a phone-based sensing application that grows virally in a large un-vetted population, a disaster-response application, where conflicting damage assessment reports may come from large numbers of different volunteers, or a military application, where friendly observers at a remote location may make hard-to-verify claims about local events. Apollo analyzes noisy data that increasingly plagues human-centric sensing to determine which items of information are more likely to be true.

References

[1]
A. Galland, S. Abiteboul, A. Marian, and P. Senellart. Corroborating information from disagreeing views. In WSDM, pages 131--140, 2010.
[2]
H. K. Le, H. Ahmadi, D. Wang, O. Fatemieh, Y. Sarwar, M. Gupta, J. Pasternack, T. Abdelzaher, J. Han, D. Roth, B. Szymanski, S. Adali, R. Ganti, F. Ye, and H. Lei. Apollo: Towards factfinding in participatory sensing. In IPSN (demo abstract), 2011.
[3]
J. Pasternack and D. Roth. Knowing what to believe (when you already know something). In COLING, 2010.

Cited By

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  • (2025)Hyper-parameter Recommendation for Truth DiscoveryDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_18(277-292)Online publication date: 12-Jan-2025
  • (2024)Efficient privacy-preserving federated logistic regression with poor-quality usersPeer-to-Peer Networking and Applications10.1007/s12083-024-01840-618:1(1-12)Online publication date: 2-Dec-2024
  • (2022)Achieving Privacy-Preserving and Lightweight Truth Discovery in Mobile CrowdsensingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.305440934:11(5140-5153)Online publication date: 1-Nov-2022
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Published In

cover image ACM Conferences
SenSys '11: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
November 2011
452 pages
ISBN:9781450307185
DOI:10.1145/2070942

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2011

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Author Tags

  1. data fusion
  2. participatory sensing
  3. quality of information

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  • Demonstration

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Overall Acceptance Rate 174 of 867 submissions, 20%

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Cited By

View all
  • (2025)Hyper-parameter Recommendation for Truth DiscoveryDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_18(277-292)Online publication date: 12-Jan-2025
  • (2024)Efficient privacy-preserving federated logistic regression with poor-quality usersPeer-to-Peer Networking and Applications10.1007/s12083-024-01840-618:1(1-12)Online publication date: 2-Dec-2024
  • (2022)Achieving Privacy-Preserving and Lightweight Truth Discovery in Mobile CrowdsensingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.305440934:11(5140-5153)Online publication date: 1-Nov-2022
  • (2019)Secure and Reliable Decentralized Truth Discovery Using Blockchain2019 IEEE Conference on Communications and Network Security (CNS)10.1109/CNS.2019.8802712(1-8)Online publication date: Jun-2019
  • (2016)A Survey on Truth DiscoveryACM SIGKDD Explorations Newsletter10.1145/2897350.289735217:2(1-16)Online publication date: 25-Feb-2016
  • (2015)A Multi-Stage Method for Connecting Participatory Sensing and Noise SimulationsSensors10.3390/s15020226515:2(2265-2282)Online publication date: 22-Jan-2015

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