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Constrained truth discovery (extended abstract)

  • Chen Ye
  • , Hongzhi Wangy
  • , Kangjie Zheng
  • , Youkang Kong
  • , Rong Zhuv
  • , Jing Gao
  • , Jianzhong Li
  • Hangzhou Dianzi University
  • Alibaba Group Holding Ltd.
  • Peng Cheng Laboratory
  • SUNY Buffalo

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

1 Scopus citations

Abstract

Aggregating the information provided by multiple data sources, which is also known as information integration , plays an important role in data analytics. Since there often exists recording errors, intentional errors, conflicts and outdated data across different data sources, finding the true attribute values of each entity is a fundamental task of crucial importance [3]. The process to fulfill this task is called truth discovery , which has been extensively studied in the literature.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PublisherIEEE Computer Society
Pages2356-2357
Number of pages2
ISBN (Electronic)9781728191843
DOIs
StatePublished - Apr 2021
Event37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Online, Chania, Greece
Duration: Apr 19 2021Apr 22 2021

Publication series

NameProceedings - International Conference on Data Engineering
Volume2021-April
ISSN (Print)1084-4627
ISSN (Electronic)2375-0286

Conference

Conference37th IEEE International Conference on Data Engineering, ICDE 2021
Country/TerritoryGreece
CityVirtual, Online, Chania
Period04/19/2104/22/21

Keywords

  • Arithmetic constraints
  • Denial constraints
  • Iterative process
  • Source weights
  • Truth discovery

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