Data warehouses support the analysis of historical data. This often involves aggregation over a period of time. Furthermore, data is typically incorporated in the warehouse in the increasing order of a time attribute, e.g., date of a sale or time of a temperature measurement. In this paper we propose a framework to take advantage of this append-only nature of updates due to a time attribute. The framework allows us to integrate large amounts of new data into the warehouse and generate historical summaries efficiently. Query and update costs are virtually independent from the extent of the data set in the time dimension, making our framework an attractive aggregation approach for append-only data streams. A specific instantiation of the general approach is developed for MOLAP data cubes, involving a new data structure for append-only arrays with pre-aggregated values. Our framework is applicable to point data and data with extent, e.g., hyper-rectangles.
|Number of pages||12|
|Journal||Proceedings of the ACM SIGMOD International Conference on Management of Data|
|Publication status||Published - 17 Sep 2002|
|Event||ACM SIGMOD 2002 Proceedings of the ACM SIGMOD International Conference on Managment of Data - Madison, WI, United States|
Duration: 3 Jun 2002 → 6 Jun 2002
ASJC Scopus subject areas
- Information Systems