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A Review on Big Data Stream Processing Applications: Contributions, Benefits, and Limitations Alwaisi, Shaimaa Safaa Ahmed; Abbood, Maan Nawaf; Jalil, Luma Fayeq; Kasim, Shahreen; Mohd Fudzee, Mohd Farhan; Hadi, Ronal; Ismail, Mohd Arfian
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.737

Abstract

The amount of data in our world has been rapidly keep growing from time to time.  In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. Many researchers in recent years proposed some different kind of frameworks for processing the big data streaming. In this work, we explore and present in detail some of the recent achievements in big data streaming in term of contributions, benefits, and limitations. As well as some of recent platforms suitable to be used for big data streaming analytics. Moreover, we also highlight several issues that will be faced in big data stream processing. In conclusion, it is hoped that this study will assist the researchers in choosing the best and suitable framework for big data streaming projects.
Arsitektur Sistem Point of Sale (POS) Multi-Cabang Berbasis Web untuk Perusahaan Ritel Terdistribusi: Studi Kasus pada Raffi Collection Mardhatillah; Aldo Erianda; Ronal Hadi
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 7 No 2 (2026)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.7.2.606

Abstract

In the modern retail ecosystem, managing distributed store operations through manual interventions presents critical vulnerabilities regarding data consistency, synchronization delays, and operational inefficiencies. This research addresses these systemic limitations by designing and implementing a web-based, centralized Multi-Branch Point of Sale (POS) system tailored for Raffi Collection, a retail enterprise managing three geographically dispersed branches in Bukittinggi. Adopting the structured Waterfall development methodology—encompassing requirements analysis, system design, implementation, testing, and maintenance—the platform was engineered utilizing the Laravel MVC framework and a centralized MySQL relational database management system. The system architecture incorporates a robust Role-Based Access Control (RBAC) mechanism defining distinct operational permissions for Super Admins and Branch Admins. Key functional workflows feature real-time transaction processing with automated cryptographic-like receipt string generation, an advanced cross-branch stock transfer approval workflow, and dynamic multi-criteria reporting engines covering sales, expenditures, inventory mutations, and net profit-loss analytics. Empirical system testing validated absolute data synchronization across all nodes, strict enforcement of security policies (demonstrated by automated 403 Access Denied responses to unauthorized routing), and precise financial auditing capability. The implementation effectively eliminates data redundancy, mitigates recording latencies, and provides business stakeholders with real-time, data-driven decision support tools