Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Choenni, S., Blok, H.E., Leertouwer, E. (2006). Handling Uncertainty and Ignorance in Databases: A Rule to Combine Dependent Data. In: Li Lee, M., Tan, KL., Wuwongse, V. (eds) Database Systems for Advanced Applications. DASFAA 2006. Lecture Notes in Computer Science, vol 3882. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11733836_23
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DOI: https://doi.org/10.1007/11733836_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33337-1
Online ISBN: 978-3-540-33338-8
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