MULTI-SOURCED INFORMATION TRUSTWORTHINESS ANALYSIS: APPLICATIONS AND THEORY

In the era of Big Data, data entries, even describing the same objects or events, can come from a variety of sources. There are some sources that typically provide accurate information, but due to various reasons such as recording errors, device malfunction, background noise and intent to manipulate the data, some other sources may contain noisy or even erroneous information. Therefore, it is inevitable that information from multiple sources is conflicting with each other. To discover useful knowledge, which is usually deeply buried in those complicate multi-sourced data, we have to conduct information trustworthiness analysis on all available data sources. In this thesis, we propose a series of approaches of multi-sourced information trustworthiness analysis, including reliability-aware information integration and inconsistency detection to efficiently and effectively discover both trustworthy and untrustworthy information, respectively.

Grantor dc:publisher

State University of New York at Buffalo

Year dc:date.issued

2018

Author dc:creator

• Xiao, Houping; 0000-0002-6981-8842

Contributors dc:contributor

• Gao, Jing
• Computer Science and Engineering

• computer science

Statement dc:rights

• Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.
• Copyright retained by author.

Language dc:language

eng

Handle dc:identifier

http://hdl.handle.net/10477/78039

OAI record oai:identifier

https://ubir.buffalo.edu/oai/request?verb=GetRecord&metadataPrefix=qdc&identifier=oai%3Aubir.buffalo.edu%3A10477%2F78039