Abstract—Data warehouses have been considered to be the key aspect of success for any Decision Support System (DSS). Temporal database research has produced important results in this field. Data warehouses store historical data, and therefore could clearly benefit from the research on temporal databases. Temporal data warehouses join the two fields of temporal databases and data warehouse research. This paper introduces a bitemporal data warehouse model that both valid time and transaction time are attached to attributes. Data warehouse objects and cubes are created with multidimensional bitemporal relational database. Performance of available well-known relational database and bitemporal extension for data warehouse is evaluated and compared in terms of execution time and disk space consumption with the set of queries.

Index Terms—Attribute time stamping, big data, bitemporal databases, data warehouse, OLAP.

Canan Eren Atay is with Dokuz Eylul University, Tinaztepe Campus, Computer Engineering Department, Buca, Izmir, Turkey.
Gözde Alp is with Marmara University, Goztepe Campus, Computer Engineering Department, Kadıkoy, Istanbul, Turkey.

Cite:Canan Eren Atay, Gözde Alp, "Modeling and Querying Multidimensional Bitemporal Data Warehouses," International Journal of Computer and Communication Engineering vol. 5, no. 2, pp. 110-119, 2016.