Optimizations of Database Management Systems for Real Time IoT Edge Applications

The exponential growth of IoT-type systems has led to a reconsideration of the field of database management systems in terms of storing and handling high-volume data. Recently, many real-time Database Management Systems(DBMS) have been developed to address issues such as security, managing concurrent access to stored data, and optimizing data query performance. This paper studies methods that allow to reduce the temporal validity range for common DBMS. The primary purpose of IoT edge devices is to generate data and make it available for machine learning or statistical algorithms. This is achieved inside the Knowledge Discovery in Databases process. In order to visualize and obtain critical Data Mining results, all the device-generated data must be made available as fast as possible for selection, preprocessing and data transformation. In this research we investigate if IoT edge devices can be used with common DBMS proper configured in order to access data fast instead of working with Real Time DBMS. We will study what kind of transactions are needed in large IoT ecosystems and we will analyze the techniques of controlling concurrent access to common resources (stored data). For this purpose, we built a series of applications that are able to simulate concurrent writing operations to a common DBMS in order to investigate the performance of concurrent access to database resources. Another important procedure that will be tested with the developed applications will be to increase the availability of data for users and data mining applications. This will be achieved by using field indexing.