A temporal database (see Temporal Database) contains time-varying data. Time is an important aspect of all real-world phenomena. Events occur at specific points in time; objects and the relationships among objects exist over time. The ability to model this temporal dimension of the real world is essential to many computer applications, such as accounting, banking, econometrics, geographical information systems, inventory control, law, medical records, multi-media, process control, reservation systems, and scientific data analysis. Conventional databases represent the state of an enterprise at a single moment of time. Although the contents of the database continue to change as new information is added, these changes are viewed as modifications to the state, with the old, out-of-date data being deleted from the database. The current contents of the database may be viewed as a snapshot of the enterprise. When a conventional database is used, the attributes involving time are manipulated solely by the application programs, with little help

Queries in temporal databases often employ the coalesce operator, either to coalesce results of projections, or data which are not coalesced upon storage. Therefore, the performance and the optimisation schemes utilised for this operator is of major importance for the performance of temporal DBMSs. Insofar, performance studies for various algorithms that implement this operator have been conducted, however, the joint optimisation of the coalesce operator with other algebraic operators that appear in the query execution plan has only received minimal attention. In this paper, we propose a scheme for combining the coalesce operator with selection operators which are applied to the valid time of the tuples produced from a coalescing operation. The proposed scheme aims at reducing the number of tuples that a coalescing operator must process, while at the same time allows the optimiser to exploit temporal indices on the valid time of the data.

Real-world data management applications generally manage temporal data, i.e., they manage multiple states of time-varying data. Many contributions have been made by the research community for how to better model, store, and query temporal data. In particular, several dozen temporal data models and query languages have been proposed. Motivated in part by the emergence of non-traditional data management applications and the increasing proliferation of temporal data, this paper puts focus on the aggregation of temporal data. In particular, it provides a general framework of temporal aggregation concepts, and it discusses the abilities of five approaches to the design of temporal query languages with respect to temporal aggregation. Rather than providing focused, polished results, the paper’s aim is to explore the inherent support for temporal aggregation in an informal manner that may serve as a foundation for further exploration. 1