TATS: an efficient technique for computing temporal aggregates for data warehousing

An important use of data warehousing is to provide temporal views over the history of source data, It is significant that nearly all data warehouses a re dependent on relational database technology, yet relational databases pr ovide little or no real support for temporal data. Therefore, it is difficu lt to obtain accurate information for time-varying data, In this paper, we are going to design a temporal data warehouse to support time-varying data efficiently. For this purpose, we present a method to support temporal quer y by combining a temporal query process layer with the relational database which is used as a source database in an existing data warehouse. We introd uce the Temporal Aggregate Tree Strategy (TATS), and suggest its algorithm for the way to aggregate the time-varying data that is changed by the time when the temporal view is created. In addition, The TATS and the materializ ed view creation method of the existing data warehouse have been evaluated. As a result, the TATS reduces the size of the fact table and it shows a go od performance for the comparison factor in case of processing the query fo r time-varying data.