Efficient Integration and Aggregation of Historical Information

Data warehouses support the analysis of historical data. This ofteninvolves aggregation over a period of time. Furthermore, data is typicallyincorporated in the warehouse in the increasing order of a time attribute,e.g., date of a sale or time of a temperature measurement. In thispaper we propose a framework to take advantage of this append-only natureof updates due to a time attribute. The framework allows us to integratelarge amounts of new data into the warehouse and generate historicalsummaries efficiently. Query and update costs are virtually independentfrom the extent of the data set in the time dimension, making our frameworkan attractive aggregation approach for append-only data streams.A specific instantiation of the general approach is developed for MOLAP datacubes, involving a new data structure for append-only arrays withpre-aggregated values. Our framework is applicable to point data and datawith extent, e.g., hyper-rectangles.