A Solution of Data Inconsistencies in Data Integration -- Designed for Pervasive Computing Environment

• Published in: Journal of computer science and technology Vol. 25; no. 3; pp. 499 - 508
• Main Author: 王欣 黄林鹏 章义 徐小辉 陈俊清
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.05.2010; Springer Nature B.V; Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China
• Subjects: Accuracy; Artificial Intelligence; Computation; Computer Science; Costs; Credibility; Criteria; Data integration; Data models; Data sources; Data Structures and Information Theory; Decision making; Expenses; Experiments; Feedback; Fuzzy; Fuzzy logic; Information sharing; Information Systems Applications (incl.Internet); Mathematical models; Regular Paper; Software Engineering; Theory of Computation; Ubiquitous computing; Utility functions; 使用费用; 数据密集型; 普适计算; 环境设计; 计算环境; 质量标准; 集成解决方案; group decision making; data inconsistency; history credibility; pervasive computing; data integration
• DOI: 10.1007/s11390-010-9340-2
• ISSN: 1000-9000

New challenges including how to share information on heterogeneous devices appear in data-intensive pervasive computing environments. Data integration is a practical approach to these applications. Dealing with inconsistencies is one of the important problems in data integration. In this paper we motivate the problem of data inconsistency solution for data integration in pervasive environments. We define data qualit~ criteria and expense quality criteria for data sources to solve data inconsistency. In our solution, firstly, data sources needing high expense to obtain data from them are discarded by using expense quality criteria and utility function. Since it is difficult to obtain the actual quality of data sources in pervasive computing environment, we introduce fuzzy multi-attribute group decision making approach to selecting the appropriate data sources. The experimental results show that our solution has ideal effectiveness.