Utility-based resolution of data inconsistencies

Abstract

A virtual database system is software that provides unified access to multiple information sources. If the sources are overlapping in their contents and independently maintained, then the likelihood of inconsistent answers is high. Solutions are often based on ranking (which sorts the different answers according to recurrence) and on fusion (which synthesizes a new value from the different alternatives according to a specific formula). In this paper we argue that both methods are flawed, and we offer alternative solutions that are based on knowledge about the performance of the source data; including features such as recentness, availability, accuracy and cost. These features are combined in a flexible utility function that expresses the overall value of a data item to the user. Utility allows us to (1) define meaningful ranking on the inconsistent set of answers, and offer the topranked answer as a preferred answer; (2) determine whether a fusion value is indeed better than the initial values, by calculating its utility and comparing it to the utilities of the initial values; and (3) discover the best fusion: the fusion formula that optimizes the utility. The advantages of such performance-based and utility-driven ranking and fusion are considerable.

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

“…However, none of them take into account the influence of data source quality. [4,12] are about data inconsistency solution based on data source properties. However, they cannot adapt to the constantly changing data source quality in the pervasive computing environment.…”

“…By contrast, we solve this problem by the group decision making method. However, [4,12] can only process the data source properties with quantitative values. As described in [13], this cannot satisfy the needs of business application and more and more researchers consider it unsuitable.…”

“…However, our solution's accuracy can be increased according to users' feedback about the satisfaction degree of data inconsistency solution. Finally, different from [4,12], we also analyze the efficiency of our solution by performing experiments.…”

Fusionplex: resolution of data inconsistencies in the integration of heterogeneous information sources

“…Although more specific, this information is still represented by the value null. The fusion of feature values (including the necessary normalizations) is covered in detail in [20].…”

“…Moreover, it should also be possible to generate automatically the optimal fusion policy: the fusion coefficients that optimize the utility function. Initial results on this issue are described in [20].…”