It has been widely recognized that in many real-life database applications there is growing demand to model uncertainty and ignorance. However the relational model does not provide this possibility. Through the years a number of efforts has been devoted to the capture of uncertainty and ignorance in databases. Most of these efforts attempted to capture uncertainty using the classic probability theory. As a consequence, the limitations of probability theory are inherited by these approaches, such as the problem of information loss. In this paper, we extend the relational model with uncertainty and ignorance without these limitations posed by the other approaches. Our approach is based on the so-called theory of belief functions, which may be considered as a generalization of probability theory. Belief functions have an attractive mathematical underpinning and many intuitively appealing properties.
|Place of Publication||Enschede, The Netherlands|
|Publisher||Centre for Telematics and Information Technology (CTIT)|
|Number of pages||12|
|Publication status||Published - Jul 2004|
|Name||CTIT Technical Report Series|
|Publisher||University of Twente, Centre for Telematics and Information Technology (CTIT)|
Choenni, R. S., Blok, H. E., & Fokkinga, M. M. (2004). Extending the Relational Model with Uncertainty and Ignorance. (CTIT Technical Report Series; No. 04-29). Enschede, The Netherlands: Centre for Telematics and Information Technology (CTIT).