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All Journal IJISCS (International Journal Of Information System and Computer Science) Journal of Innovation Information Technology and Application (JINITA)
Eugène MBUYI MUKENDI
Faculty of Science and Technology, University of Kinshasa, Kinshasa, D.R.Congo
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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STUDY AND IMPROVEMENT OF PERFORMANCE OF NoSQL DATABASES: MongoDB, HBase and OrientDB. Noel Bila; Bopatriciat Boluma Mangata; Eugène MBUYI MUKENDI; Parfum BUKANGA CHRISTIAN
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : STMIK Pringsewu Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1262

Abstract

This dissertation adds to the various research works in the field of NoSQL "Not only SQL" databases. These new models propose a new way of organizing and storing data designed mainly to remedy the constraints imposed by the ACID properties on relational models. Our objective was to develop a comparative performance study, between three NoSQL solutions widely used in the market, namely: MongoDB, HBase and OrientDB, to propose to decision makers, elements of information for possible choices of the best appropriate solution for their companies. The Benchmark used to decide between these solutions is the Yahoo Cloud Serving Benchmark.
Big Data Architectures and Concepts Audrey Tembo Welo; Hervé Lubaki Kinzonzi; Noel Bila Khonde; Eugène Mbuyi Mukendi
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.1876

Abstract

Nowadays, the processing of big data has become a major preoccupation for businesses, not only for storage and processing but also for operational requirements such as speed, maintaining performance with scalability, reliability, availability, security, and cost control; ultimately enabling them to maximize their profits by using the new possibilities offered by Big Data. In this article, we will explore and exploit the concepts and architectures of Big Data, in particular through the Hadoop open-source framework, and see how it meets the needs set out above, in its cluster structure, its components, its Lambda and Kappa architectures, and so on. We are also going to deploy Hadoop in a virtualized Linux environment, with several nodes, under the Oracle Virtual Box virtualization software, and use the experimental method to compare the processing time of the MapReduce algorithm on two DataSets with successively one, two, and three and four Datanodes, and thus observe the gains in processing time with the increase in the number of nodes in the cluster
Machine Learning based on Probabilistic Models Applied to Medical Data: The Case of Prostate Cancer Anaclet Tshikutu Bikengela; Remy Mutapay Tshimona; Pierre Kafunda Katalay; Simon Ntumba Badibanga; Eugène Mbuyi Mukendi
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.1879

Abstract

The growth in the amount of data in companies puts analysts in difficulties when extracting hidden knowledge from data. Several models have emerged that focus on the notion of distances while ignoring the notion of conditional probability density. This research study focuses on segmentation using mixture models and Bayesian networks for medical data mining. As enterprise data becomes large, there is a way to apply data mining methods to make sense of it using classification methods. We designed different models with different architectures and then applied these models to the medical database. The algorithms were implemented for the real data. The objective is to classify individuals according to the conditional probability density of random variables, in addition to identifying causalities between traits from tests of conditional independence and a correlation measure, both based on χ2. After a quick illustration of several models (decision tree, SVM, K-means, Bayes), we applied our method to data from an epidemiological study (done at the University of Kinshasa University clinics) of case-control of prostate cancer. Thus, we found after interpretation of the results followed by discussion that our model allows us to classify a new individual with an accuracy of 96%.
Co-Authors Anaclet Tshikutu Bikengela Audrey Tembo Welo Bopatriciat Boluma Mangata Hervé Lubaki Kinzonzi Noel Bila Noel Bila Khonde Parfum BUKANGA CHRISTIAN Pierre Kafunda Katalay Remy Mutapay Tshimona Simon Ntumba Badibanga