Paper Info
Title
Integrate Inconsistent And Heterogeneous Data Based On User Feedback
Abstract
Purpose - Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data. An important issue on data integration is the existence of conflicts among the different data sources. Data sources may conflict with each other at data level, which is defined as data inconsistency. The purpose of this paper is to aim at this problem and propose a solution for data inconsistency in data integration.Design/methodology/approach - A relational data model extended with data source quality criteria is first defined. Then based on the proposed data model, a data inconsistency solution strategy is provided. To accomplish the strategy, fuzzy multi-attribute decision-making (MADM) approach based on data source quality criteria is applied to obtain the results. Finally, users feedbacks strategies are proposed to optimize the result of fuzzy MADM approach as the final data inconsistent solution.Findings - To evaluate the proposed method, the data obtained from the sensors are extracted. Some experiments are designed and performed to explain the effectiveness of the proposed strategy. The results substantiate that the solution has a better performance than the other methods on correctness, time cost and stability indicators.Practical implications - Since the inconsistent data collected from the sensors are pervasive, the proposed method can solve this problem and correct the wrong choice to some extent.Originality/value - In this paper, for the first time the authors study the effect of users feedbacks on integration results aiming at the inconsistent data.
Year
DOI
Venue
2015
10.1108/IJICC-04-2014-0013
INTERNATIONAL JOURNAL OF INTELLIGENT COMPUTING AND CYBERNETICS
Keywords
Field
DocType
Decision making, Data fusion, Data inconsistency, Data integration, User feedback
Data integration,Data warehouse,Data mining,Ontology-based data integration,Data modeling,Data quality,Computer science,Logical data model,Artificial intelligence,Data model,Machine learning,Change data capture
Journal
Volume
Issue
ISSN
8
2
1756-378X
Citations 
PageRank 
References 
0
0.34
16
Authors
3
Name
Order
Citations
PageRank
lihua lu1441.84
Hengzhen Zhang21215.27
Xiao-Zhi Gao316522.58