An Efficient and Robust Tuple Timestamp Hybrid Historical Relational Data Model

Abstract

This paper proposes a novel, efficient and robust tuple time stamped hybrid historical relational model for dealing with temporal data. The primary goal of developing this model is to make it easier to manage historical data robustly with minimal space requirements and retrieve it more quickly and efficiently. The model's efficiency and results were revealed when it was applied to an employee database. The proposed model's performance in terms of query execution time and space requirements is compared to a single relational data model. The obtained results show that the proposed model is approximately 20% faster than the conventional single relational data model. Memory consumption results also show that the proposed model's memory cost at different frequencies is significantly reduced, which is approximately 30% less than the single relational data model for a set of queries. Because net cost is strongly related to query execution time and memory cost, the suggested model's net cost is also significantly reduced. The proposed tuple timestamp hybrid historical model acts as generic, accurate and robust model. It provides the same functionality as previous versions, as well as hybrid functionality of previously proposed models, with a significant improvement in query execution speed and memory usage. This model is effective and reliable for the use in a wide range of temporal database fields, including insurance, geographic information systems, stocks and finance (e.g. Finacle in Banking), data warehousing, scientific databases, legal case histories, and medical records.