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On the Discovery of Evolving Truth

Authors:

Jiawei HanAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 675 - 684

https://doi.org/10.1145/2783258.2783277

Published: 10 August 2015 Publication History

Abstract

In the era of big data, information regarding the same objects can be collected from increasingly more sources. Unfortunately, there usually exist conflicts among the information coming from different sources. To tackle this challenge, truth discovery, i.e., to integrate multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. In many real world applications, however, the information may come sequentially, and as a consequence, the truth of objects as well as the reliability of sources may be dynamically evolving. Existing truth discovery methods, unfortunately, cannot handle such scenarios. To address this problem, we investigate the temporal relations among both object truths and source reliability, and propose an incremental truth discovery framework that can dynamically update object truths and source weights upon the arrival of new data. Theoretical analysis is provided to show that the proposed method is guaranteed to converge at a fast rate. The experiments on three real world applications and a set of synthetic data demonstrate the advantages of the proposed method over state-of-the-art truth discovery methods.

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Cited By

Zhu JSun YSong SYuan X(2024)High Precision ≠ High Cost: Temporal Data Fusion for Multiple Low-Precision SensorsProceedings of the ACM on Management of Data10.1145/36549462:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654946
Yu XShao ZDong KChen C(2024)A Novel Truth Discovery Approach for Health Recommendation SystemsIEEE Transactions on Consumer Electronics10.1109/TCE.2023.332286970:1(2435-2446)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3322869
Fu XLiu AXiong NWang TZhang S(2024)ATWR-SMR: An Area-Constrained Truthful-Worker Recruitment-Based Sensing Map Recovery Scheme for Sparse MCS in Extreme-Environment Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2023.331461511:3(3711-3724)Online publication date: 1-Feb-2024
https://doi.org/10.1109/JIOT.2023.3314615
Show More Cited By

Index Terms

On the Discovery of Evolving Truth
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

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: 10 August 2015

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KDD '15

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

Acceptance Rates

KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

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

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KDD '25

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sigkdd
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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Zhu JSun YSong SYuan X(2024)High Precision ≠ High Cost: Temporal Data Fusion for Multiple Low-Precision SensorsProceedings of the ACM on Management of Data10.1145/36549462:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654946
Yu XShao ZDong KChen C(2024)A Novel Truth Discovery Approach for Health Recommendation SystemsIEEE Transactions on Consumer Electronics10.1109/TCE.2023.332286970:1(2435-2446)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3322869
Fu XLiu AXiong NWang TZhang S(2024)ATWR-SMR: An Area-Constrained Truthful-Worker Recruitment-Based Sensing Map Recovery Scheme for Sparse MCS in Extreme-Environment Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2023.331461511:3(3711-3724)Online publication date: 1-Feb-2024
https://doi.org/10.1109/JIOT.2023.3314615
Shen XXu CZhu LLu RGuan YZhang X(2024)Blockchain-Based Lightweight and Privacy-Preserving Quality Assurance Framework in Crowdsensing SystemsIEEE Internet of Things Journal10.1109/JIOT.2023.328834911:1(974-986)Online publication date: 1-Jan-2024
https://doi.org/10.1109/JIOT.2023.3288349
Fang XShen CSun GChen HTang Y(2024)Truth Discovery of Source Dependency Perception in Dynamic ScenariosWeb and Big Data10.1007/978-981-97-2387-4_4(48-63)Online publication date: 28-Apr-2024
https://doi.org/10.1007/978-981-97-2387-4_4
Deng XWang XLi LDai HHu W(2024)A Reliable and Privacy-Preserving Truth Discovery Scheme for Mobile Crowdsensing Based on Functional EncryptionFrontiers in Cyber Security10.1007/978-981-96-0151-6_24(365-382)Online publication date: 27-Dec-2024
https://doi.org/10.1007/978-981-96-0151-6_24
Yang YZhao ZWu GZhuo XLiu QBai QLi W(2023)A Lightweight, Effective, and Efficient Model for Label Aggregation in CrowdsourcingACM Transactions on Knowledge Discovery from Data10.1145/363010218:4(1-27)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3630102
Bornemann LBleifuß TKalashnikov DNargesian FNaumann FSrivastava D(2023)Matching Roles from Temporal Data: Why Joe Biden is not only President, but also Commander-in-ChiefProceedings of the ACM on Management of Data10.1145/35889191:1(1-26)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588919
Wu GZhuo XZhou LBao XHong RWu X(2023)TIRA: Truth Inference via Reliability Aggregation on Object-Source GraphIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322530835:11(11967-11981)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TKDE.2022.3225308
Mukkamala PWu HDüdder B(2023)Reliable and Streaming Truth Discovery in Blockchain-based Crowdsourcing2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SECON58729.2023.10287465(492-500)Online publication date: 11-Sep-2023
https://doi.org/10.1109/SECON58729.2023.10287465
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