Data management in original software development for hybrid models

Abstract

Huge amount of data is generated nowadays by many and various sources and their management is a problem of high importance in science, especially in research and development. Complex phenomena can be accurately modeled using various sources of large amount of data which either cross check the models, or improve their accuracy. Data management is an important issue in actual modeling context, which must be solved at both, concept level and applications' layer. The paper presents a ‘checklist’ of questions to be answered when the data management layer of a new computer aided model must be conceived and implemented. Moreover, finding the rules of the ‘game’, i.e. the best policies for the rapid development of the data management foundation of the models leads to a valuable knowhow in the rapid composite models’ development or even in hybrid models’ development. The paper presents the conclusions of a thorough study of the MySQL, Oracle, Microsoft SQL Server and MariaDB RDBMSs. A subsequent study will approach MongoDB, Amazon DynamoDB and Databricks in order to synchronize the models’ development requirements with the actual cloud approaches: IaaS, PaaS and SaaS. To conclude, in the huge volume of data actual conditions, an intelligent data persistency strategy within a model is a goal for a smart and visionary analyst, who is, according to Blaise Pascal, a “thinking reed”, i.e. “Man is but a reed, the most feeble thing in nature, but he is a thinking reed”.