Abstract
Attaching Date and Time to varying data plays a definite role in representing a dynamic domain and resources on the database systems. The conventional database stores current data and can only represent the knowledge in static sense, whereas Time-varying database represents the knowledge in dynamic sense. This paper focuses on incorporating interval-based timestamping in First Normal Form (1NF) data model. 1NF approach has been chosen for the easily implementation in relational framework as well as to provide the temporal data representation with the modeling and querying power of relational data model. Simulation results revealed that the proposed approach substantially improved the performance of temporal data representation in terms of required memory storage and queries processing time.
Highlights
Temporal Database (TDB) is database modeling technique that is considered as repositories of time-dependent data
We describe an approach for implementing temporal database in the framework of relational data model over the most widely used commercial DBMSs (Oracle RDBMS)
The data representation of temporal database in Timestamp Historical Relational (TTHR) is accomplished by firstly, defining the database object for which we want to track the historical changes of the stored data, we add for each such relations two additional columns Lifespan Start Time (LSST) and Lifespan End Time (LSET), which indicate the beginning and the end of the time interval within which the database object exists in the modeled reality [14], [19]
Summary
Temporal Database (TDB) is database modeling technique that is considered as repositories of time-dependent data. Several research works have been conducting in this research area starting from the 1970s [1] Some of these works deal with storage structure and temporal DBMS prototype, while others concentrated on query processing temporal time indexing [2][6]. The research work by Snodgrass in [7] treats the problems of temporal databases models, integrity constraints, storage structures, and implementation techniques using different DBMS. Since conventional relational database is used to store and process the data that refer to the current time [2], commercial DBMS and standards for the query language do not fully support temporal features [3], [21]. The first one is an integrated method where the time-varying features of the data are supported by an extended or modified internal model in DBMS. The greatest efficiency is offered by the first method the second method has greater popularity due to its realism
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