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Abstract:Much of the research on bitemporal databases has focused on the modeling of time-related data with either attribute or tuple timestamping. While the attribute-timestamping approach attaches bitemporal data to attributes, the tuple-timestamping approach splits the object's history into several tuples. Although there have been numerous studies on bitemporal data models, there is no work contrasting these two common approaches in terms of system performance and ease of use. In this paper, we compared interval-based attribute-and tuple-timestamped bitemporal data models by running sample queries to measure processing time, and then we evaluated their usability by using the same data.Our tests indicate that the attribute-timestamping model with one-level nested approach required less time and used less disk space; therefore, it is more appropriate for modeling bitemporal data.

A data warehouse is considered a key aspect of success for any decision support system. Research on temporal databases have produced important results in this field, and data warehouses, which store historical data, can clearly benefit from such studies. A slowly changing dimension is a dimension in which any of its attributes in a data warehouse can change infrequently over time. Although different solutions have been proposed, each has its own particular disadvantages. The authors propose the Object-Relational Temporal Data Warehouse (O-RTDW) model for the slowly changing dimensions in this research work. Using this approach, it is possible to keep track of the whole history of an object in a data warehouse efficiently. The proposed model has been implemented on a real data set and tested successfully. Several limitations implied in other solutions, such as redundancy, surrogate keys, incomplete historical data, and creation of additional tables are not present in our solution.

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Abstract:A nested bitemporal relational data model and its query language are implemented. The bitemporal atom (BTA) is the fundamental construct to represent temporal data and it contains 5 components: a value, the lower and upper bounds of valid time, and the lower and upper bounds of the recoding time. We consider 2 types of data structures for storing BTAs: 1) string representation and 2) abstract data-type representation. We also develop a preprocessor for translating a bitemporal structured query language (BtSQL) statement into standard SQL statements. The BtSQL includes the select, insert, delete, and update statements of the SQL, extended for bitemporal relational databases. It supports bitemporal, historical, and current context. Bitemporal context is for auditing purposes, historical context is for querying past states of a bitemporal database, and current context is for querying the snapshot state of a bitemporal database. We also evaluate the performance of the 2 alternative implementation methods by considering retrieval, insertion, and update queries.