An Efficient and Robust Tuple Timestamp Hybrid Historical Relational Data Model

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

Optimized Model of Ledger Database Management to handle Vehicle Registration

In recent years, versioning of business data has become increasingly important in enterprise solutions. In the context of big data, where the 5Vs (Volume, Velocity, Variety, Value, and Veracity) play a pivotal role, the management of data versioning gains even greater significance. As enterprises grapple with massive volumes of data generated at varying velocities and exhibiting diverse formats, ensuring the accuracy, completeness, and consistency of data throughout its lifecycle becomes paramount. System of record solutions, which cover most enterprise solutions, require the management of data life history in both system and business time. This means that information must be stored in a way that covers past, present, and future states, such as a contract with a start date in the past and an end date in the future that may require correction at any point during its lifetime. While some systems offer transaction time rollback features, they do not address the business life history dimension of a contract or asset, which requires the developer to code the business rules of the requirements. The relational data model is unable to inherently use relational constraints where a business time dimension of the data is required, as it is a “current view” and not designed for this purpose. Therefore, there is a need for better autonomous capabilities for version control of data, which will bring new functionality and cost reduction in application development and maintenance, reduce coding complexity, and increase productivity. This paper presents an approach to relational data management that relieves the developer from the need to code the business rules for versioning. The framework, called Ld8a, works with a standard Oracle database that keeps the developer in the “current view” paradigm but allows them to specify the point in time that logical insert, update, and delete events take place with the infrastructure autonomously maintaining relational correctness of the dataset across time. The Ld8a framework has been used to address the vehicle registration scenario used by AWS in presenting the capabilities of the Quantum Ledger Database product. This approach offers a solution that maintains referential integrity by the infrastructure across time, making version control of data easier and more efficient for developers

Combining DL-LiteNbool with branching time: a gentle marriage

We study combinations of the description logic DL-Lite_{bool}^N with the branching temporal logics CTL* and CTL. We analyse two types of combinations, both with rigid roles: (i) temporal operators are applied to concepts and to ABox assertions, and (ii) temporal operators are applied to concepts and Boolean combinations of concept inclusions and ABox assertions. For the resulting logics, we present algorithms for the satisfiability problem and (mostly tight) complexity bounds ranging from ExpTime to 3ExpTime