Abstract
Models of computation are fundamental notions in computer science; consequently, they have been the subject of countless research papers, with numerous novel models proposed even in recent years. Amongst a multitude of different approaches, many of these methods draw inspiration from the biological processes observed in nature. P systems, or membrane systems, make an analogy between the communication in computing and the flow of information that can be perceived in living organisms. These systems serve as a basis for various concepts, ranging from the fields of computational economics and robotics to the techniques of data clustering. In this paper, such utilization of these systems—membrane system–based clustering—is taken into focus. Considering the growing number of data stored worldwide, more and more data have to be handled by clustering algorithms too. To solve this issue, bringing these methods closer to the data, their main element provides several benefits. Database systems equip their users with, for instance, well-integrated security features and more direct control over the data itself. Our goal is if the type of the database management system is given, e.g., NoSQL, but the corporation or the research team can choose which specific database management system is used, then we give a perspective, how the algorithms written like this behave in such an environment, so that, based on this, a more substantiated decision can be made, meaning which database management system should be connected to the system. For this purpose, we discover the possibilities of a clustering algorithm based on P systems when used alongside NoSQL database systems, that are designed to manage big data. Variants over two competing databases, MongoDB and Redis, are evaluated and compared to identify the advantages and limitations of using such a solution in these systems.
Highlights
One of the main goals of data mining is to observe previously unrecognized correlations and patterns in the designated data set
P systems, otherwise known as membrane systems are concurrent models of computation stemming from nature, namely, from the processes in biological cells in living organisms, as the authors of [1] described them
NoSQL database systems provide the user with simple, yet efficient mechanisms to store and retrieve large volumes of data— these systems are often utilized in big data applications
Summary
One of the main goals of data mining is to observe previously unrecognized correlations and patterns in the designated data set. This procedure involves the use of the different kinds of learning methods, to be specific, reinforcement, supervised, and unsupervised learning. NoSQL (or non-relational) database systems provide the user with simple, yet efficient mechanisms to store and retrieve large volumes of data— these systems are often utilized in big data applications. The mechanisms of authentication and encryption are directly implemented and immediately available. Two such systems are the widely used MongoDB and Redis database systems. High-level programming languages, such as Python 3—for which the pymongo and redis packages enable the user to access MongoDB and Redis database methods directly—can be a basis of any desired user-defined algorithms aiming to take advantage of the potential of these systems
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