In many applications, uncertainty and ignorance go hand in hand. Therefore, to deliver database support for effective decision making, an integrated view of uncertainty and ignorance should be taken. So far, most of the efforts attempted to capture uncertainty and ignorance with probability theory. In this paper, we discuss the weakness to capture ignorance with probability theory, and propose an approach inspired by the Dempster-Shafer theory to capture uncertainty and ignorance. Then, we present a rule to combine dependent data that are represented in different relations. Such a rule is required to perform joins in a consistent way. We illustrate that our rule is able to solve the so-called problem of information loss, which was considered as an open problem so far.
|Title of host publication||Proceedings of the 11th International Conference on Database Systems for Advanced Applications (DASFAA 2006)|
|Editors||Mong Li Lee, Kian-Lee Tan, Vilas Wuwongse|
|Place of Publication||Berlin, Germany|
|Number of pages||15|
|Publication status||Published - Apr 2006|
|Name||Lecture Notes in Computer Science|