Some Mathematical Computing and Simulation on Big Data
Abstract
Data is an abstract representation of the volume of seen things and real-world entities. Big data in computer science, information science, cognitive informatics, web-based computing, cloud computing, and computational intelligence is very large-scale heterogeneous data in terms of amount, complexity, semantics, distribution, and processing costs. The study of big data includes its methodology, theory, and mathematical underpinnings. In order to effectively meet the inherent complexity and rapidly rising needs in big data representation, acquisition, storage, organization, manipulation, search, retrieval, distribution, standardization, consistency, and security, big data engineering studies analytics technologies. Big data algebra will be introduced in this study as a new denotation for formal big data analytics in big data science and engineering. We investigate the cognitive underpinnings of knowledge, information, data, and intelligence. The mathematical- statistical model for large data is formally presented. The formal big data analysis, inference, mining, induction, and fusion operators are just a few examples of the algebraic operators on formal big data models that are strictly characterized in light of this. As a result, algebra can be used for mining, information elicitation, knowledge representation, and intelligent inference with massive data. In the modern fields of big data science and engineering, cognitive informatics, knowledge mining, neuroinformatics, genomics, cognitive computing, machine learning, semantic computing, cognitive robotics, cognitive linguistics, cognitive systems, computational intelligence, artificial intelligence, cloud computing, and intelligent systems, a wide range of applications of big data algebra have been found. Therefore, in order to draw a conclusion and provide a description, we willattempt to perform some technicalanalysis and simulation on certain data. This study aims to implement cryptography and artificial intelligence techniques in several mathematical computations and simulations on big data using the Python programming language. This research will study and apply several cryptographic techniques such as encryption and decryption on big data as well as artificial intelligence techniques such as neural networks and decision trees to assist data analysis and processing. The data that will be used is big data that comes from various sources such as business data, health data, and financial data. The methods used in this research are literature studies, experiments, and evaluations. The results of this research are expected to contribute to the safe and effective processing and analysis of big data and to help improve the quality of the results of data analysis.
Keywords
Cryptography, Simulation, Mathematics
Source of Fund
International
Funding Institution
BINUS
Fund
Rp.44.942.404,00
Contract Number
029/VR.RTT/III/2023