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Privacy Aware Data Management and Chase
Fundamenta Informaticae - Special issue ISMIS'05
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Privacy Aware Data Management and Chase
Fundamenta Informaticae - Special issue ISMIS'05
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A rule-based chase algorithm (called Chase2), presented in this paper, provides a strategy for predicting what values should replace the null values in a relational database. When information about an object is partially incomplete (a set of weighted values of the same attribute can be treated as an allowed attribute value), Chase2 is decreasing that incompleteness. In other words, when several weighted values of the same attribute are assigned to an object, Chase2 will increase their standard deviation. To make the presentation clear and simple, we take an incomplete information system S of type λ as the model of data. To begin Chase2 process, each attribute in S that has either unknown or incomplete values for some objects in S is set, one by one, as a decision attribute and all other attributes in S are treated as condition attributes. Assuming that d is the decision attribute, we take a subsystem S1 of S by selecting from S any object x such that d(x) ≠ NULL. Now, the subsystem S1 is used for extracting rules describing values of attribute d. In the next step, each incomplete slot in S which is in the column corresponding to attribute d is chased by previously extracted rules from S1, describing d. All other incomplete attributes in a database are processed the same way. This concludes the first loop of Chase2. The whole process is recursively repeated till no more new values can be predicted by Chase2. In this case, we say that a fixed point in values prediction was reached.