Towards the integration of heterogeneous uncertain data

Along with the rapid development of data storing and sharing techniques in terms of both hardware and software, multiple data instances scattered across multiple databases may be available to support one single task, and then making choices of data are necessary from time to time. Research has been conducted on quality or reliability evaluation for individual piece of data assisted by domain knowledge to guide the data selecting processes. However, the choice still can be very difficult if the supporting data instances are contradictory or inconsistent. This paper presents a novel data integration approach based on Credibility Measure, which was developed on the basis of Possibility Measure and Necessity Measure under the framework of fuzzy set theory and fuzzy logic. In particular, the approach is able to combine any new piece of data into the existing decision by an effective credibility revision algorithm such that the revised results have taken all the currently available information into consideration. The proposed approach is applied to a decision problem in the predictive toxicology domain to illustrate the potential in improving the effectiveness of data sharing and the robustness of decisions made from the related data sources.