Supporting imprecision in multidimensional databases using granularities

Online analytical processing (OLAP) technologies are being used widely, but the lack of effective means of handling data imprecision, which occurs when exact values are not known precisely or are entirely missing, represents a major obstacle in applying these technologies in many domains. The paper develops techniques for handling imprecision that aim to maximally reuse existing OLAP modeling constructs such as dimension hierarchies and granularities. With imprecise data available in the database, queries are tested to determine whether or not they may be answered precisely given the available data; if not, alternative queries unaffected by the imprecision are suggested. When processing queries affected by imprecision, techniques are proposed that take into account the imprecision in the grouping of the data, in the subsequent aggregate computation, and in the presentation of the imprecise result to the user. The approach is capable of exploiting existing OLAP query processing techniques such as pre-aggregation, yielding an effective approach with low computational overhead and that may be implemented using current technology.