Resolving conflicts among unbalanced multi-source data when multi-value objects exist

Xiu Susie Fang, Quan Z. Sheng, Jian Yang, Guohao Sun*, Xianzhi Wang, Yihong Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

When considering multi-value objects, the inevitable unbalanced data distribution is overlooked by the existing truth discovery methods. In this work, we propose a confidence interval based approach (CIMTD) to tackle this issue. We estimate source reliability from two aspects, i.e., the ability to claim the correct number of value(s) and specific value(s). To reflect real reliability for both 'big' and 'small' sources, confidence intervals of enriched estimation are considered. While estimating source reliability, uncertainty degrees are introduced to model object differences. Confidence intervals are also considered to reflect real uncertainty degrees for both 'hot' and 'cold' objects. CIMTD outperforms baseline methods on real-world datasets.

Original languageEnglish
Title of host publicationIEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages2
ISBN (Electronic)9781665409261
ISBN (Print)9781665409278
DOIs
Publication statusPublished - 2022
Event2022 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2022 - Virtual, Online, United States
Duration: 2 May 20225 May 2022

Conference

Conference2022 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2022
Country/TerritoryUnited States
CityVirtual, Online
Period2/05/225/05/22

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