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Research ArticleOpen accessEvery word in the titleTransformative Effects of Big Data on Advanced Data Analytics: Open Issues and Critical ChallengesShabnam Kumari, P. MuthulakshmiWe are living in the period of the information era (i.e., where data/information is the new oil for companies/industries). The term big data had been authored to depict this age. This study characterizes a lot on the idea of big data and its progress over the previous decade to till date. This study presents the meaning of this new idea … …Journal of Computer Science, Vol. 18, Issue 6 · 14 Jun 2022 · 6,349 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Fuzzy Clustering Based Method for the Spatio-Temporal Data AnalysisAnahita Zolghadr, Afsaneh JalalianSpatiotemporal data is a type of data that is collected by the sensors. This type of data has two spatial and temporal dimensions. There are many challenges in analyzing spatiotemporal big data. Common evaluation metrics of clustering methods are not appropriate for spatiotemporal data. Previous clustering methods and the conventional evaluation metrics are efficient for data like time series with … …Journal of Computer Science, Vol. 18, Issue 6 · 13 Jun 2022 · 5,638 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Data-Sharing Model to Secure Borders Using an Artificial-Intelligence-Based Risk Engine and Big-Data ConceptsMohammad S. Al Rousan, Benedetto IntrigilaThe primary aim of this research is to develop a framework for data management and sharing that will enable countries to share complex data about known and unknown high-risk passengers to streamline border-control security processes through the use of big data analytics and Artificial Intelligence (AI). A total of 15 semi-structured interviews were used to gather qualitative data. A thematic … …American Journal of Applied Sciences, Vol. 19, Issue 1 · 2 May 2022 · 6,937 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleData Clustering-based Metaheuristic for Physical Internet Supply Chain NetworkAbdelsamad Chouar, Samir Tetouani, Aziz Soulhi, Jamila ElalamiIn this study, a data clustering-driven technique is proposed for a Physical Internet Supply Chain Network (PI-SCN) to reduce data complexity, process time compression, and lankness of process optimization. Given a set of data points, a clustering algorithm aims to classify each data-points into a specific group. Each group should have similar properties and/or features, while data points in different … …Journal of Computer Science, Vol. 18, Issue 4 · 21 Apr 2022 · 6,472 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleGeneration Z Health Trends: Data Mining of Sleep and Lifestyle PatternsFrancka Sakti Lee, Johanes Fernandes Andry, Kevin Christianto et al.This study analyzes sleep health and lifestyle data among Generation Z using RapidMiner to identify key issues such as insomnia and stress within this demographic. The research is motivated by the increasing concerns surrounding sleep health and lifestyle choices that significantly impact overall well-being. The primary objective is to identify patterns and correlations in health data to inform targeted interventions … …Journal of Computer Science, Vol. 21, Issue 9 · 19 Oct 2025 · 3,632 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Systematic Literature Review for Implementing Data Ops in the Data Warehouse Lifecycle during the ETL PhaseAhmed Bahaa, Sherif Ragab Eldemerdash, Hanan FahmyNowadays, no one can deny the importance of Data Ware House (DWH) in all organizations. The most important components in Data Ware House (DWH) are the Extraction, Transformation, Loading (ETL) phase. Data cleaning is a basic piece of the transformation stage in Data Warehousing. This may affect critical activities such as data collection and decision-making in various organizations Data Ops … …Journal of Computer Science, Vol. 17, Issue 11 · 10 Nov 2021 · 7,547 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleData Preparation in Machine Learning for Condition-based MaintenanceOns Masmoudi, Mehdi Jaoua, Amel Jaoua, Soumaya YacoutUsing Machine Learning (ML) prediction to achieve a successful, cost-effective, Condition-Based Maintenance (CBM) strategy has become very attractive in the context of Industry 4.0. In other fields, it is well known that in order to benefit from the prediction capability of ML algorithms, the data preparation phase must be well conducted. Thus, the objective of this paper is to investigate … …Journal of Computer Science, Vol. 17, Issue 6 · 4 Jun 2021 · 8,412 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleBig Data in Educational Institutions using RapidMiner to Predict Learning EffectivenessEvaristus Didik Madyatmadja, David Jumpa Malem Sembiring, Sinek Mehuli Br Perangin Angin et al.Schools, universities, colleges and other educational institutions store a lot of data related to students and teachers as well as those related to education. You can analyze this data to gain insights that can improve the operational efficiency of your organization. Educational institution's needs based on student behavior, student exam results, each student's growth and changed educational regulations can be … …Journal of Computer Science, Vol. 17, Issue 4 · 29 Apr 2021 · 6,834 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleDevelopment of Big Data Classifier for Biomedicine Early Diagnosis: An Experimental Approach Using Machine Learning MethodsMa Beth Solas Concepcion, Bobby Dioquino Gerardo, Frank Ibañez Elijorde et al.In the fast-phase world, data availability is abundant due to a rapid adaptation increase of big data technologies. Large amounts of data have been generated and collected at an unprecedented speed and scale, introducing a revolution in medical research practices for biomedicine informatics. Thus, there is an immense demand for statistically rigorous approaches, especially in the medical diagnosis discipline. Therefore, … …Journal of Computer Science, Vol. 20, Issue 4 · 1 Feb 2024 · 5,510 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAnomaly Detection Algorithms for Streaming Data: Performance ComparisonZirije HasaniToday’s most of the data are streaming time-series data, where is very important anomaly detection over this data because gives significant information of possible critical situations. Detecting anomalies in big streaming data is yet difficult task because we have to process them in real time, even before they are stored and instantly alarm on potential threats. For real time streaming … …Journal of Computer Science, Vol. 16, Issue 7 · 18 Jul 2020 · 7,288 accessesView PDFCite