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 incons...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.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 60970010the National Basic Research 973 Program of China under Grant No. 2009CB320705the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20090073110026
文摘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.
