Multi-dimensional aggregation: a viable solution for interval data

Now a day multi-dimensional data modeling and aggregate query processing which are key assets of business intelligence solutions are being frequently realized to the unorthodox data. For interval values which are recorded when the data is on hold, multidimensional aggregation is the only viable solution and the author emphasizes over this aspect in this paper. Actually, such intervals reflect the state of reality of either current data or such data which were part of the present database. Every possible challenge which interval data throws upon is resolved in this paper through introduction of aggregation operator. Although the intervals are unknown at first but they eventually depend on the actual data and it turns out to be quiet handy while associating them with the resulting tuples. Only those result groups are selected for this purpose, which are specified partially. The interval data signifies that data holds either for each interim in the interval or entire interval and in both of these two cases it faces contention with the operators. In this paper, the author presents the empirical analysis of the aggregation operator after its implementation over the huge industrial data sets and claims that it holds an edge over the other temporal aggregation algorithms. © 2020, Bharati Vidyapeeth's Institute of Computer Applications and Management.