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Journal articles on the topic 'Architecture analytics'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Kumar, S. Senthil, and Ms V. Kirthika. "Big Data Analytics Architecture and Challenges, Issues of Big Data Analytics." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (October 31, 2017): 669–73. http://dx.doi.org/10.31142/ijtsrd4673.

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2

Marah, Bockarie Daniel, Zilong Jing, Tinghuai Ma, Raeed Alsabri, Raphael Anaadumba, Abdullah Al-Dhelaan, and Mohammed Al-Dhelaan. "Smartphone Architecture for Edge-Centric IoT Analytics." Sensors 20, no. 3 (February 7, 2020): 892. http://dx.doi.org/10.3390/s20030892.

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The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloud/core for analysis and data storage. This research, therefore, focuses on formulating an edge-centric IoT architecture for smartphones which are very popular electronic devices that are capable of executing complex computational tasks at the network edge. A novel smartphone IoT architecture (SMIoT) is introduced that supports data capture and preprocessing, model (i.e., machine learning models) deployment, model evaluation and model updating tasks. Moreover, a novel model evaluation and updating scheme is provided which ensures model validation in real-time. This ensures a sustainable and reliable model at the network edge that automatically adjusts to changes in the IoT data subspace. Finally, the proposed architecture is tested and evaluated using an IoT use case.

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Hinojosa-Palafox, Eduardo A., Oscar M. Rodríguez-Elías, José A. Hoyo-Montaño, Jesús H. Pacheco-Ramírez, and José M. Nieto-Jalil. "An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions." Sensors 21, no. 13 (June 23, 2021): 4282. http://dx.doi.org/10.3390/s21134282.

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The architecture design of industrial data analytics system addresses industrial process challenges and the design phase of the industrial Big Data management drivers that consider the novel paradigm in integrating Big Data technologies into industrial cyber-physical systems (iCPS). The goal of this paper is to support the design of analytics Big Data solutions for iCPS for the modeling of data elements, predictive analysis, inference of the key performance indicators, and real-time analytics, through the proposal of an architecture that will support the integration from IIoT environment, communications, and the cloud in the iCPS. An attribute driven design (ADD) approach has been adopted for architectural design gathering requirements from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair, and overhaul (MRO) scenarios. Data management drivers presented consider new Big Data modeling analytics techniques that show data is an invaluable asset in iCPS. An architectural design reference for a Big Data analytics architecture is proposed. The before-mentioned architecture supports the Industrial Internet of Things (IIoT) environment, communications, and the cloud in the iCPS context. A fault diagnosis case study illustrates how the reference architecture is applied to meet the functional and quality requirements for Big Data analytics in iCPS.

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Barrad, Sherif, Stéphane Gagnon, and Raul Valverde. "An Analytics Architecture for Procurement." International Journal of Information Technologies and Systems Approach 13, no. 2 (July 2020): 73–98. http://dx.doi.org/10.4018/ijitsa.2020070104.

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Procurement transformation and pure cost reduction are no longer a novelty in today's modern business world. Procurement, as a core business function, plays a key role given its ability to generate value for the firm. From maximizing supplier value to minimizing contract leakage, challenges seldomly lack in this department. In fact, both resource and skill shortages and technology limitations are typically “top-of-mind” for Procurement Executives. Many research articles around the concept of cost reduction however, limited literature has been published in the areas of Artificial Intelligence, analytics and Rules-Based Systems and their specific application in Procurement. This article proposes a new enterprise architecture, leveraging emerging technologies to guide procurement organizations in their digital transformation. Our intent is to discuss how analytics, business rules and complex event processing (CEP) can be explored and adapted to the world of procurement to help reduce costs. This article concludes by suggesting an approach to implement the proposed architecture.

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Choudhury, Dwaipayan, Aravind Sukumaran Rajam, Ananth Kalyanaraman, and Partha Pratim Pande. "High-Performance and Energy-Efficient 3D Manycore GPU Architecture for Accelerating Graph Analytics." ACM Journal on Emerging Technologies in Computing Systems 18, no. 1 (January 31, 2022): 1–19. http://dx.doi.org/10.1145/3482880.

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Recent advances in GPU-based manycore accelerators provide the opportunity to efficiently process large-scale graphs on chip. However, real world graphs have a diverse range of topology and connectivity patterns (e.g., degree distributions) that make the design of input-agnostic hardware architectures a challenge. Network-on-Chip (NoC)- based architectures provide a way to overcome this challenge as the architectural topology can be used to approximately model the expected traffic patterns that emerge from graph application workloads. In this paper, we first study the mix of long- and short-range traffic patterns generated on-chip using graph workloads, and subsequently use the findings to adapt the design of an optimal NoC-based architecture. In particular, by leveraging emerging three-dimensional (3D) integration technology, we propose design of a small-world NoC (SWNoC)- enabled manycore GPU architecture, where the placement of the links connecting the streaming multiprocessors (SM) and the memory controllers (MC) follow a power-law distribution. The proposed 3D manycore GPU architecture outperforms the traditional planar (2D) counterparts in both performance and energy consumption. Moreover, by adopting a joint performance-thermal optimization strategy, we address the thermal concerns in a 3D design without noticeably compromising the achievable performance. The 3D integration technology is also leveraged to incorporate Near Data Processing (NDP) to complement the performance benefits introduced by the SWNoC architecture. As graph applications are inherently memory intensive, off-chip data movement gives rise to latency and energy overheads in the presence of external DRAM. In conventional GPU architectures, as the main memory layer is not integrated with the logic, off-chip data movement negatively impacts overall performance and energy consumption. We demonstrate that NDP significantly reduces the overheads associated with such frequent and irregular memory accesses in graph-based applications. The proposed SWNoC-enabled NDP framework that integrates 3D memory (like Micron's HMC) with a massive number of GPU cores achieves 29.5% performance improvement and 30.03% less energy consumption on average compared to a conventional planar Mesh-based design with external DRAM.

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6

Srinivas, Shreya, Asif Qumer Gill, and Terry Roach. "Analytics-Enabled Adaptive Business Architecture Modeling." Complex Systems Informatics and Modeling Quarterly, no. 23 (July 31, 2020): 23–43. http://dx.doi.org/10.7250/csimq.2020-23.03.

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7

., Nagalakshmi D. R. "REMOTE ONLINE BIG DATA ANALYTICS ARCHITECTURE." International Journal of Research in Engineering and Technology 05, no. 16 (May 25, 2016): 99–101. http://dx.doi.org/10.15623/ijret.2016.0516021.

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8

Esteban-Maluenda, Ana, Laura Sánchez Carrasco, and Luis San Pablo Moreno. "ArchiText Mining: Applying Text Analytics to Research on Modern Architecture." Život umjetnosti, no. 105 (December 31, 2019): 158–79. http://dx.doi.org/10.31664/zu.2019.105.07.

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ArchiteXt Mining: Spanish Modern Architecture through Its Texts (1939–1975) is a research project funded by the Government of Spain through the 2015 Call for “Excellence Projects” of the Ministry of Economy and Competitiveness. This project aims to explore a new viewpoint and look into the special features of Spanish modern architecture. Despite the increasing success of using data analysis as a tool in a variety of disciplines, research on architectural theory has never made the most efficient use of these technologies. The Spanish and international circumstances of modern architecture development have been scrutinized through qualitative research, which has established a shared theoretical ground. It is now time to start a new in-depth research based on objective data. To address this challenge, we propose the application of text mining techniques to take advantage of the best data source in the field: architectural periodicals. The purpose is to create a powerful database hosted on a public website for the scientific community. Thus, this project fulfils several e-Research objectives: to facilitate the computerization of data research, to support every stageof data collection, and to manage big data analyses with thehelp of specific tools.

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Amare, Meseret Yihun, and Stanislava Simonova. "Learning analytics for higher education: proposal of big data ingestion architecture." SHS Web of Conferences 92 (2021): 02002. http://dx.doi.org/10.1051/shsconf/20219202002.

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Research background: Higher education institutions are generating multiple formats of data from diverse sources across the globe. The data ingestion layer is responsible for collecting data and transform for analysis. Learning analytics plays a vital role in providing decision-making support and selection of suitable timely intervention. The lack of tailored big-data ingestion architectures for academics led to several implementation challenges. Purpose of the article: The purpose of this article is to propose data ingestion architecture enabled for big data learning analytics. Methods: The study reviews existing literature to examine big-data ingestion tools and frameworks; and identify big-data ingestion challenges. An optimized framework for the real world learning analytics application was not yet in place at global higher educations. Consequently, the big-data ingestion pipeline is experiencing challenges of inefficient and complex data access, slow processing time, and security issues associated with transferring data to the system. The proposed data ingestion architecture is based on review of recent literature and adapts best international practices, guidelines, and techniques to meet the demand of current big-data ingestion issues. Findings & value added: This study identifies the current global challenges in implementing learning analytics projects. Review of recent big data ingestion techniques has been done based on defined metrics tuned for learning analytics purposes. The proposed data ingestion framework would increase the effectiveness of collecting, importing, processing and storing of learning data. Besides, the proposed architecture contributes to the construction of full-fledged big-data learning analytics ecosystem of higher educations.

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Kenda, Klemen, Nikolaos Mellios, Matej Senožetnik, and Petra Pergar. "Computer Architectures for Incremental Learning in Water Management." Sustainability 14, no. 5 (March 2, 2022): 2886. http://dx.doi.org/10.3390/su14052886.

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This paper presents an architecture and a platform for processing of water management data in real time. Stakeholders in the domain are faced with the challenge of handling large amounts of incoming sensor data from heterogeneous sources after the digitalization efforts within the sector. Our water management analytical platform (WMAP) is built upon the needs of domain experts (it provides capabilities for offline analysis) and is designed to solve real-world problems (it provides real-time data flow solutions and data-driven predictive analytics) for smart water management. WMAP is expected to contribute significantly to the water management domain, which has not yet acquired the competences to implement extensive data analysis and modeling capabilities in real-world scenarios. The proposed architecture extends existing big data architectures and presents an efficient way of dealing with data-driven modeling in the water management domain. The main improvement is in the speed (online analytics) layer of the architecture, where we introduce heterogeneous data fusion in a set of data streams that provide real-time data-driven modeling and prediction services. Using the proposed architecture, the results illustrate that models built with datasets with richer contextual information and multiple data sources are more accurate and thus more useful.

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11

Weber, C., and J. Königsberger. "Industrie 4.0: Aktuelle Entwicklungen für Analytics*/Industrie 4.0: Current Advances in Analytics - Part 1: Analytics and Data Management in Industrie 4.0 Reference Architectures." wt Werkstattstechnik online 107, no. 03 (2017): 113–17. http://dx.doi.org/10.37544/1436-4980-2017-03-9.

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Die Verarbeitung großer Datenmengen sowie die hohe Relevanz von Datenanalysen sind in den produzierenden Unternehmen mittlerweile angekommen. Bekannte Anwendungsbeispiele sind Digital Mock-Up in der Produktentwicklung oder Prozessoptimierung durch Predictive Maintenance. Die in letzter Zeit entwickelten Referenzarchitekturen in diesen breitgefächerten Themenfeldern betrachten dementsprechend verschiedene Aspekte in unterschiedlichen Ausprägungen. Dieser aus zwei Teilen bestehende Fachbeitrag rekapituliert und bewertet diese Entwicklungen, um Unternehmen bei der Umsetzung ihrer eigenen individuellen Architektur Hilfestellung zu geben. Im Teil 1 werden aktuelle Referenzarchitekturen mit ihren Architekturbausteinen im Bereich Industrie 4.0 vorgestellt. Im zweiten Teil (Ausgabe 6-2017 der wt Werkstattstechnik online) werden die Referenzarchitekturen unter dem Gesichtspunkt der Themenfelder Analytics sowie Datenmanagement untersucht und bewertet.   The processing of huge amounts of data as well as the importance of analytics on data have arrived in the manufacturing industry by now. Well-known usage examples are digital mock-ups in product engineering or process optimization through predictive maintenance. Recently developed reference architectures in these wide-ranging subject areas consider multiple aspects under different characteristics. This article recapitulates and evaluates these developments in two parts and aims to support companies in the implementation of their individual architecture. In this first part, current reference architectures for Industrie 4.0 are introduced. In the second part (to be pubished in issue 6-2017), these architectures are compared and assessed with regard to analytics and data management.

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12

Ozdal, Muhammet Mustafa, Serif Yesil, Taemin Kim, Andrey Ayupov, John Greth, Steven Burns, and Ozcan Ozturk. "Energy efficient architecture for graph analytics accelerators." ACM SIGARCH Computer Architecture News 44, no. 3 (October 12, 2016): 166–77. http://dx.doi.org/10.1145/3007787.3001155.

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13

Milosevic, Zoran, Weisi Chen, Andrew Berry, and Fethi A. Rabhi. "An open architecture for event-based analytics." International Journal of Data Science and Analytics 2, no. 1-2 (October 17, 2016): 13–27. http://dx.doi.org/10.1007/s41060-016-0029-7.

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14

Özgüven, Yavuz, Utku Gönener, and Süleyman Eken. "A Dockerized big data architecture for sports analytics." Computer Science and Information Systems, no. 00 (2022): 10. http://dx.doi.org/10.2298/csis220118010o.

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The big data revolution has had an impact on sports analytics as well. Many large corporations have begun to see the financial benefits of integrating sports analytics with big data. When we rely on central processing systems to aggregate and analyze large amounts of sport data from many sources, we compromise the accuracy and timeliness of the data. As a response to these issues, distributed systems come to the rescue, and the MapReduce paradigm holds promise for large scale data analytics. We describe a big data architecture based on Docker containers with Apache Spark in this paper. We evaluate the architecture on four data-intensive case studies in sport analytics including structured analysis, streaming, machine learning approaches, and graph-based analysis.

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15

Liu, Xiang Ju. "Research of Big Data Processing Platform." Applied Mechanics and Materials 484-485 (January 2014): 922–26. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.922.

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This paper introduces the operational characteristics of the era of big data and the current era of big data challenges, and exhaustive research and design of big data analytics platform based on cloud computing, including big data analytics platform architecture system, big data analytics platform software architecture , big data analytics platform network architecture big data analysis platform unified program features and so on. The paper also analyzes the cloud computing platform for big data analysis program unified competitive advantage and development of business telecom operators play a certain role in the future.

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16

Zhang, Decai, and Mass Hareeza Binti Ali. "A New Big Data and Logistic Regression-Based Approach for Small and Medium-Sized Enterprises." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 11 (November 30, 2022): 131–40. http://dx.doi.org/10.17762/ijritcc.v10i11.5800.

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Businesses are being asked to assess an expanding volume of actual semi-structured and unstructured statistics to address the obstacles of internationalization and deal more effectively with the uncertainties of international integration. Big Data (BD) analytics can therefore play a strategic role in promoting the international expansion of Small and Medium-Sized Enterprises (SMEs). The exact connection between BD Analytics and globalization has, however, only been sporadically examined in the existing literature. In this study, a quantitative analysis using a Logistic Regression (LR) concept revealed that the interaction effects between BD Analytics architecture and BD Analytics functionality are both helpful and significant but the connection between the management of BD Analytics architecture and the Degree of Internationalization (DI) is not required for internationalization development. This shows that increasing internationalization in SMEs requires more than BD Analytics governance alone. Instead, this study emphasizes the importance of building particular BD Analytics abilities and the availability of a beneficial interaction between management of BD Analytics architecture and BD Analytics abilities that could take advantage of the new information gained via BD Analytics in SME global expansion.

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Qureshi, Yasir Mahmood, William Andrew Simon, Marina Zapater, Katzalin Olcoz, and David Atienza. "Gem5-X." ACM Transactions on Architecture and Code Optimization 18, no. 4 (December 31, 2021): 1–27. http://dx.doi.org/10.1145/3461662.

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The increasing adoption of smart systems in our daily life has led to the development of new applications with varying performance and energy constraints, and suitable computing architectures need to be developed for these new applications. In this article, we present gem5-X, a system-level simulation framework, based on gem-5, for architectural exploration of heterogeneous many-core systems. To demonstrate the capabilities of gem5-X, real-time video analytics is used as a case-study. It is composed of two kernels, namely, video encoding and image classification using convolutional neural networks (CNNs). First, we explore through gem5-X the benefits of latest 3D high bandwidth memory (HBM2) in different architectural configurations. Then, using a two-step exploration methodology, we develop a new optimized clustered-heterogeneous architecture with HBM2 in gem5-X for video analytics application. In this proposed clustered-heterogeneous architecture, ARMv8 in-order cluster with in-cache computing engine executes the video encoding kernel, giving 20% performance and 54% energy benefits compared to baseline ARM in-order and Out-of-Order systems, respectively. Furthermore, thanks to gem5-X, we conclude that ARM Out-of-Order clusters with HBM2 are the best choice to run visual recognition using CNNs, as they outperform DDR4-based system by up to 30% both in terms of performance and energy savings.

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18

Smith, Jeffrey, and Manjeet Rege. "The Data Swarm." International Journal of Information Retrieval Research 6, no. 1 (January 2016): 52–64. http://dx.doi.org/10.4018/ijirr.2016010104.

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The traditional data warehouse model is no longer able to keep up with the evolution and changing requirements of the data analytic world. As we see the concept of a logical data warehouse gain momentum, there's a resulting need to drive a portion of the analytics closer to where the data is actually created and used. This paper uses the concept of swarm intelligence as a basis for simple, distributed analytics architecture to help address this need. It illustrates this with an example based on a chain of retail stores and demonstrates how this model could simplify the architecture and, at the same time, and increase data availability while decreasing cost.

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19

Chinsook, Kittipong, Withamon Khajonmote, Sununta Klintawon, Chaiyan Sakulthai, Wicha Leamsakul, and Thada Jantakoon. "Big Data in Higher Education for Student Behavior Analytics (Big Data-HE-SBA System Architecture)." Higher Education Studies 12, no. 1 (February 10, 2022): 105. http://dx.doi.org/10.5539/hes.v12n1p105.

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Big data is an important part of innovation that has recently attracted a lot of interest from academics and practitioners alike. Given the importance of the education industry, there is a growing trend to investigate the role of big data in this field. Much research has been undertaken to date in order to better understand the use of big data in many sectors for diverse reasons. Big data in higher education, however, still lacks a complete examination. Thus, the purposes of the research were (1) to design the system architecture of big data in higher education for student behavior analytics and (2) to evaluate the system architecture of big data in higher education for student behavior analytics. The research procedure was divided into two phases. The first phase is designing a system architecture for big data in higher education for student behavior analytics, and the second phase is the architecture evaluation by experts. Purposive sampling was used to select ten experts in big data and student behavior analytics. Data collection tools were the system and the assessment of an appropriate model with a five-level rating scale. The statistics used in the data analysis were means and standard deviation. The results showed that the system architecture of big data in higher education for student behavior analytics consists of four elements: a) Big Data Sources for Behavioral Analytics; b) Big Data Sources for Behavioral Analytics Sub-Domains; c) Big data capture and storage for behavioral analytics; and d) big data behavioral analysis. The experts' opinions on the system architecture were at the most appropriate level.

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20

K, Palanivel, and Manikandan J. "Business Analytics Architecture Stack to Modern Business Organizations." International Journal of Computer Sciences and Engineering 7, no. 8 (August 31, 2019): 275–87. http://dx.doi.org/10.26438/ijcse/v7i8.275287.

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Zubyk, Liudmyla, and Yaroslav Zubyk. "Architecture of modern platforms for big data analytics." Advanced Information Technology, no. 1 (1) (2021): 67–74. http://dx.doi.org/10.17721/ait.2021.1.09.

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Big data is one of modern tools that have impacted the world industry a lot of. It also plays an important role in determining the ways in which businesses and organizations formulate their strategies and policies. However, very limited academic researches has been conducted into forecasting based on big data due to the difficulties in capturing, collecting, handling, and modeling of unstructured data, which is normally characterized by it’s confidential. We define big data in the context of ecosystem for future forecasting in business decision-making. It can be difficult for a single organization to possess all of the necessary capabilities to derive strategic business value from their findings. That’s why different organizations will build, and operate their own analytics ecosystems or tap into existing ones. An analytics ecosystem comprising a symbiosis of data, applications, platforms, talent, partnerships, and third-party service providers lets organizations be more agile and adapt to changing demands. Organizations participating in analytics ecosystems can examine, learn from, and influence not only their own business processes, but those of their partners. Architectures of popular platforms for forecasting based on big data are presented in this issue.

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Palanivel, K. "Modern Network Analytics Architecture Stack to Enterprise Networks." International Journal for Research in Applied Science and Engineering Technology 7, no. 4 (April 30, 2019): 2634–51. http://dx.doi.org/10.22214/ijraset.2019.4480.

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23

Nikolakis, N., A. Marguglio, G. Veneziano, P. Greco, S. Panicucci, T. Cerquitelli, E. Macii, S. Andolina, and K. Alexopoulos. "A microservice architecture for predictive analytics in manufacturing." Procedia Manufacturing 51 (2020): 1091–97. http://dx.doi.org/10.1016/j.promfg.2020.10.153.

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Batni, Ramachendra P., and John F. Heck. "An analytics engine architecture for service provider deployments." Bell Labs Technical Journal 13, no. 3 (November 25, 2008): 129–41. http://dx.doi.org/10.1002/bltj.20329.

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25

Huertas Celdrán, Alberto, José A. Ruipérez-Valiente, Félix J. García Clemente, María Jesús Rodríguez-Triana, Shashi Kant Shankar, and Gregorio Martínez Pérez. "A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms." Sensors 20, no. 10 (May 21, 2020): 2923. http://dx.doi.org/10.3390/s20102923.

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The smart classrooms of the future will use different software, devices and wearables as an integral part of the learning process. These educational applications generate a large amount of data from different sources. The area of Multimodal Learning Analytics (MMLA) explores the affordances of processing these heterogeneous data to understand and improve both learning and the context where it occurs. However, a review of different MMLA studies highlighted that ad-hoc and rigid architectures cannot be scaled up to real contexts. In this work, we propose a novel MMLA architecture that builds on software-defined networks and network function virtualization principles. We exemplify how this architecture can solve some of the detected challenges to deploy, dismantle and reconfigure the MMLA applications in a scalable way. Additionally, through some experiments, we demonstrate the feasibility and performance of our architecture when different classroom devices are reconfigured with diverse learning tools. These findings and the proposed architecture can be useful for other researchers in the area of MMLA and educational technologies envisioning the future of smart classrooms. Future work should aim to deploy this architecture in real educational scenarios with MMLA applications.

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Babar, Muhammad, Muhammad Usman Tariq, Ahmed S. Almasoud, and Mohammad Dahman Alshehri. "Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics." Security and Communication Networks 2021 (November 28, 2021): 1–9. http://dx.doi.org/10.1155/2021/3320436.

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The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results.

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Tummers, J., A. Kassahun, and B. Tekinerdogan. "Reference architecture design for farm management information systems: a multi-case study approach." Precision Agriculture 22, no. 1 (June 1, 2020): 22–50. http://dx.doi.org/10.1007/s11119-020-09728-0.

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AbstractOne of the key elements of precision agriculture is the farm management information system (FMIS) that is responsible for data management, analytics and subsequent decision support. Various FMISs have been developed to support the management of farm businesses. A key artefact in the development of FMISs is the software architecture that defines the gross level structure of the system. The software architecture is important for understanding the system, analysing the design decisions and guiding the further development of the system based on the architecture. To assist in the design of the FMIS architecture, several reference architectures have been provided in the literature. Unfortunately, in practice, it is less trivial to derive the application architecture from these reference architectures. Two underlying reasons for this were identified. First of all, it appears that the proposed reference architectures do not specifically focus on FMIS but have a rather broad scope of the agricultural domain in general. Secondly, the proposed reference architectures do not seem to have followed the proper architecture documentation guidelines as defined in the software architecture community, lack precision, and thus impeding the design of the required application architectures. Presented in this article is a novel reference architecture that is dedicated to the specific FMIS domain, and which is documented using the software architecture documentation guidelines. In addition, the systematic approach for deriving application architectures from the proposed reference architecture is provided. To illustrate the approach, the results of multi-case study research are shown in which the presented reference architecture is used for deriving different FMIS application architectures.

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Hassan, Alaa Abdelraheem, and Tarig Mohammed Hassan. "Real-Time Big Data Analytics for Data Stream Challenges: An Overview." European Journal of Information Technologies and Computer Science 2, no. 4 (July 25, 2022): 1–6. http://dx.doi.org/10.24018/compute.2022.2.4.62.

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The conventional approach of evaluating massive data is inappropriate for real-time analysis; therefore, analysing big data in a data stream remains a critical issue for numerous applications. It is critical in real-time big data analytics to process data at the point where they are arriving at a quick reaction and good decision making, necessitating the development of a novel architecture that allows for real-time processing at high speed and low latency. Processing and anlayzing a data stream in real-time is critical for a variety of applications; however, handling a large amount of data from a variety of sources, such as sensor networks, web traffic, social media, video streams, and other sources, is a considerable difficulty. The main goal of this paper is to give an overview of the current architecture for real time big data analytics, real-time data stream processing methods available, including their system architectures Lambda, kappa, and delta large data stream processing.

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Khan, Muhammad Ashfaq, Md Rezaul Karim, and Yangwoo Kim. "A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network." Symmetry 10, no. 10 (October 11, 2018): 485. http://dx.doi.org/10.3390/sym10100485.

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Every day we experience unprecedented data growth from numerous sources, which contribute to big data in terms of volume, velocity, and variability. These datasets again impose great challenges to analytics framework and computational resources, making the overall analysis difficult for extracting meaningful information in a timely manner. Thus, to harness these kinds of challenges, developing an efficient big data analytics framework is an important research topic. Consequently, to address these challenges by exploiting non-linear relationships from very large and high-dimensional datasets, machine learning (ML) and deep learning (DL) algorithms are being used in analytics frameworks. Apache Spark has been in use as the fastest big data processing arsenal, which helps to solve iterative ML tasks, using distributed ML library called Spark MLlib. Considering real-world research problems, DL architectures such as Long Short-Term Memory (LSTM) is an effective approach to overcoming practical issues such as reduced accuracy, long-term sequence dependency, and vanishing and exploding gradient in conventional deep architectures. In this paper, we propose an efficient analytics framework, which is technically a progressive machine learning technique merged with Spark-based linear models, Multilayer Perceptron (MLP) and LSTM, using a two-stage cascade structure in order to enhance the predictive accuracy. Our proposed architecture enables us to organize big data analytics in a scalable and efficient way. To show the effectiveness of our framework, we applied the cascading structure to two different real-life datasets to solve a multiclass and a binary classification problem, respectively. Experimental results show that our analytical framework outperforms state-of-the-art approaches with a high-level of classification accuracy.

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Alexakis, Theodoros, Nikolaos Peppes, Konstantinos Demestichas, and Evgenia Adamopoulou. "A Distributed Big Data Analytics Architecture for Vehicle Sensor Data." Sensors 23, no. 1 (December 29, 2022): 357. http://dx.doi.org/10.3390/s23010357.

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The unceasingly increasing needs for data acquisition, storage and analysis in transportation systems have led to the adoption of new technologies and methods in order to provide efficient and reliable solutions. Both highways and vehicles, nowadays, host a vast variety of sensors collecting different types of highly fluctuating data such as speed, acceleration, direction, and so on. From the vast volume and variety of these data emerges the need for the employment of big data techniques and analytics in the context of state-of-the-art intelligent transportation systems (ITS). Moreover, the scalability needs of fleet and traffic management systems point to the direction of designing and deploying distributed architecture solutions that can be expanded in order to avoid technological and/or technical entrapments. Based on the needs and gaps detected in the literature as well as the available technologies for data gathering, storage and analysis for ITS, the aim of this study is to provide a distributed architecture platform to address these deficiencies. The architectural design of the system proposed, engages big data frameworks and tools (e.g., NoSQL Mongo DB, Apache Hadoop, etc.) as well as analytics tools (e.g., Apache Spark). The main contribution of this study is the introduction of a holistic platform that can be used for the needs of the ITS domain offering continuous collection, storage and data analysis capabilities. To achieve that, different modules of state-of-the-art methods and tools were utilized and combined in a unified platform that supports the entire cycle of data acquisition, storage and analysis in a single point. This leads to a complete solution for ITS applications which lifts the limitations imposed in legacy and current systems by the vast amounts of rapidly changing data, while offering a reliable system for acquisition, storage as well as timely analysis and reporting capabilities of these data.

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Ouafiq, El Mehdi, Mourad Raif, Abdellah Chehri, and Rachid Saadane. "Data Architecture and Big Data Analytics in Smart Cities." Procedia Computer Science 207 (2022): 4123–31. http://dx.doi.org/10.1016/j.procs.2022.09.475.

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Zhan, Yufeng, Peng Li, Kun Wang, Song Guo, and Yuanqing Xia. "Big Data Analytics by CrowdLearning: Architecture and Mechanism Design." IEEE Network 34, no. 3 (May 2020): 143–47. http://dx.doi.org/10.1109/mnet.001.1900286.

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Pateromichelakis, Emmanouil, Fabrizio Moggio, Christian Mannweiler, Paul Arnold, Mehrdad Shariat, Michael Einhaus, Qing Wei, Omer Bulakci, and Antonio De Domenico. "End-to-End Data Analytics Framework for 5G Architecture." IEEE Access 7 (2019): 40295–312. http://dx.doi.org/10.1109/access.2019.2902984.

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Nahri, Mohamed, Azedine Boulmakoul, Lamia Karim, and Ahmed Lbath. "IoV distributed architecture for real-time traffic data analytics." Procedia Computer Science 130 (2018): 480–87. http://dx.doi.org/10.1016/j.procs.2018.04.055.

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Ang, Kenneth Li-Minn, Feng Lu Ge, and Kah Phooi Seng. "Big Educational Data & Analytics: Survey, Architecture and Challenges." IEEE Access 8 (2020): 116392–414. http://dx.doi.org/10.1109/access.2020.2994561.

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Franciscus, Nigel, Xuguang Ren, and Bela Stantic. "Precomputing architecture for flexible and efficient big data analytics." Vietnam Journal of Computer Science 5, no. 2 (May 2018): 133–42. http://dx.doi.org/10.1007/s40595-018-0109-9.

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Weber, C., and J. Königsberger. "Industrie 4.0: Aktuelle Entwicklungen für Analytics*/Industrie 4.0: Current Advances in Analytics - Part 2: Comparison and Assessment of Industrie 4.0 Reference Architectures." wt Werkstattstechnik online 107, no. 06 (2017): 405–9. http://dx.doi.org/10.37544/1436-4980-2017-06-21.

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Die Verarbeitung großer Datenmengen sowie die Erkenntnis, dass Datenanalysen eine hohe Relevanz haben, sind in den produzierenden Unternehmen angekommen. Bekannte Anwendungsbeispiele sind Digital Mock-Up in der Produktentwicklung oder Prozessoptimierung durch Predictive Maintenance. Die in letzter Zeit entwickelten Referenzarchitekturen in diesen breitgefächerten Themenfeldern betrachten dementsprechend verschiedene Aspekte in unterschiedlichen Ausprägungen. Dieser aus zwei Teilen bestehende Beitrag rekapituliert und bewertet diese Entwicklungen, um Unternehmen bei der Umsetzung ihrer eigenen individuellen Architektur Hilfestellung zu geben. Im ersten Teil des Beitrags (Ausgabe 3-2017: wt Werkstattstechnik online) wurden aktuelle Referenzarchitekturen mit ihren Architekturbausteinen im Bereich Industrie 4.0 vorgestellt. In diesem zweiten Teil werden nun die Referenzarchitekturen unter dem Gesichtspunkt der Themenfelder Analytics sowie Datenmanagement untersucht und bewertet.   The processing of huge amounts of data as well as the importance of data analytics have arrived in the manufacturing industry by now. Well-known usage examples are digital mock-ups in product engineering or process optimization through predictive maintenance. Recently developed reference architectures in these wide-ranging subject areas consider multiple aspects under different characteristics. This two-part article recapitulates and evaluates these developments and aims to support companies in the implementation of their individual architecture. In the first part, published in 3-2017 wt online, current reference architectures for Industrie 4.0 were introduced. In this part, these architectures are compared and assessed with regard to analytics and data management.

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Marosi, Attila Csaba, Márk Emodi, Ákos Hajnal, Róbert Lovas, Tamás Kiss, Valerie Poser, Jibinraj Antony, et al. "Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing." Future Internet 14, no. 4 (April 6, 2022): 114. http://dx.doi.org/10.3390/fi14040114.

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The use of mature, reliable, and validated solutions can save significant time and cost when introducing new technologies to companies. Reference Architectures represent such best-practice techniques and have the potential to increase the speed and reliability of the development process in many application domains. One area where Reference Architectures are increasingly utilized is cloud-based systems. Exploiting the high-performance computing capability offered by clouds, while keeping sovereignty and governance of proprietary information assets can be challenging. This paper explores how Reference Architectures can be applied to overcome this challenge when developing cloud-based applications. The presented approach was developed within the DIGITbrain European project, which aims at supporting small and medium-sized enterprises (SMEs) and mid-caps in realizing smart business models called Manufacturing as a Service, via the efficient utilization of Digital Twins. In this paper, an overview of Reference Architecture concepts, as well as their classification, specialization, and particular application possibilities are presented. Various data management and potentially spatially detached data processing configurations are discussed, with special attention to machine learning techniques, which are of high interest within various sectors, including manufacturing. A framework that enables the deployment and orchestration of such overall data analytics Reference Architectures in clouds resources is also presented, followed by a demonstrative application example where the applicability of the introduced techniques and solutions are showcased in practice.

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Chen, Yung-Yao, Yu-Hsiu Lin, Chia-Ching Kung, Ming-Han Chung, and I.-Hsuan Yen. "Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes." Sensors 19, no. 9 (May 2, 2019): 2047. http://dx.doi.org/10.3390/s19092047.

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In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable.

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Babar, Muhammad, Mohammad Dahman Alshehri, Muhammad Usman Tariq, Fasee Ullah, Atif Khan, M. Irfan Uddin, and Ahmed S. Almasoud. "IoT-Enabled Big Data Analytics Architecture for Multimedia Data Communications." Wireless Communications and Mobile Computing 2021 (December 17, 2021): 1–9. http://dx.doi.org/10.1155/2021/5283309.

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The present spreading out of the Internet of Things (IoT) originated the realization of millions of IoT devices connected to the Internet. With the increase of allied devices, the gigantic multimedia big data (MMBD) vision is also gaining eminence and has been broadly acknowledged. MMBD management offers computation, exploration, storage, and control to resolve the QoS issues for multimedia data communications. However, it becomes challenging for multimedia systems to tackle the diverse multimedia-enabled IoT settings including healthcare, traffic videos, automation, society parking images, and surveillance that produce a massive amount of big multimedia data to be processed and analyzed efficiently. There are several challenges in the existing structural design of the IoT-enabled data management systems to handle MMBD including high-volume storage and processing of data, data heterogeneity due to various multimedia sources, and intelligent decision-making. In this article, an architecture is proposed to process and store MMBD efficiently in an IoT-enabled environment. The proposed architecture is a layered architecture integrated with a parallel and distributed module to accomplish big data analytics for multimedia data. A preprocessing module is also integrated with the proposed architecture to prepare the MMBD and speed up the processing mechanism. The proposed system is realized and experimentally tested using real-time multimedia big data sets from athentic sources that discloses the effectiveness of the proposed architecture.

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T, Abirami. "Telco Data Analytics using Open-Source Data Pipeline: Detailed Architecture and Technology Stack." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1717–25. http://dx.doi.org/10.22214/ijraset.2021.38644.

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Abstract: Open-source technology has influenced data analytics at each step from data storage to data analysis, and visualization. Open source for telco big data analytics enables sharp insights by enhancing problem discoverability and solution feasibility. This research paper talks about different technology stacks using open source for telco big data analytics that are used to deploy various tools including data collection, data storage, data processing, data analysis, and data visualization. This open source pipeline micro-services architecture built with modular technology stack and orchestrated by Kubernetes, can ingest data from multiple sources, process real-time data and provide business and network intelligence. Major idea of using open source technology in our architecture is to reduce cost and manage easily. Kubernetes is an industry adopted open source container orchestrator that offers fault-tolerance, application scaling, and load-balancing. The results can be displayed on the intuitive open source dashboard like Grafana for telecom operators. Our architecture is flexible and can be easily customized based on the telecommunication industry needs. Using the proposed architecture, the telecommunication sectors can get quick decision making with nearly 30% lower CapEX which is made possible using COTS hardware. Index Terms: Big data analytics, Data pipeline architecture, Open Source technologies, Real-time data processing, Faulttolerance, Load-balancing, Kubernetes, BDA, Open source dashboard

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Wang, Chen-Shu, and Jui-Yen Chang. "MISFP-Growth: Hadoop-Based Frequent Pattern Mining with Multiple Item Support." Applied Sciences 9, no. 10 (May 20, 2019): 2075. http://dx.doi.org/10.3390/app9102075.

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In practice, single item support cannot comprehensively address the complexity of items in large datasets. In this study, we propose a big data analytics framework (named Multiple Item Support Frequent Patterns, MISFP-growth algorithm) that uses Hadoop-based parallel computing to achieve high-efficiency mining of itemsets with multiple item supports (MIS). The proposed architecture consists of two phases. First, in the counting support phase, a Hadoop MapReduce architecture is employed to determine the support for each item. Next, in the analytics phase, sub-transaction blocks are generated according to MIS and the MISFP-growth algorithm identifies the frequency of patterns. To facilitate decision makers in setting MIS, we also propose the concept of classification of item (COI), which classifies items of higher homogeneity into the same class, by which the items inherit class support as their item support. Three experiments were implemented to validate the proposed Hadoop-based MISFP-growth algorithm. The experimental results show approximately 38% reduction in the execution time on parallel architectures. The proposed MISFP-growth algorithm can be implemented on the distributed computing framework. Furthermore, according to the experimental results, the enhanced performance of the proposed algorithm indicates that it could have big data analytics applications.

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Abolfazli, Saeid, and Maria R. Lee. "Mobile Data Analytics." IT Professional 19, no. 3 (2017): 14–16. http://dx.doi.org/10.1109/mitp.2017.38.

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Kumar Sangaiah, Arun, Ford Lumban Gaol, and Krishn K. Mishra. "Guest Editorial: Special Section on Big Data & Analytics Architecture." Intelligent Automation & Soft Computing 26, no. 3 (2020): 515–17. http://dx.doi.org/10.32604/iasc.2020.013928.

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Ruangvanich, Supparang, Prachyanun Nilsook, and Panita Wannapiroon. "System Architecture of Learning Analytics in Intelligent Virtual Learning Environment." International Journal of e-Education, e-Business, e-Management and e-Learning 9, no. 2 (2019): 90–99. http://dx.doi.org/10.17706/ijeeee.2019.9.2.90-99.

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Ruangvanich, Supparang, Prachyanun Nilsook, and Panita Wannapiroon. "System Architecture of Learning Analytics in Intelligent Virtual Learning Environment." International Journal of e-Education, e-Business, e-Management and e-Learning 10, no. 1 (2020): 33–42. http://dx.doi.org/10.17706/ijeeee.2020.10.1.33-42.

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Banasode, Praveen, and Sunita Padmannavar. "ENHANCED COMPUTATION ARCHITECTURE FOR BIG DATA ANALYTICS USING ENCRYPTION ALGORITHM." Indian Journal of Computer Science and Engineering 12, no. 5 (October 20, 2021): 1267–77. http://dx.doi.org/10.21817/indjcse/2021/v12i5/211205010.

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Hu, Chuang, Rui Lu, and Dan Wang. "FEVA: A Federated Video Analytics Architecture for Networked Smart Cameras." IEEE Network 35, no. 6 (November 2021): 163–70. http://dx.doi.org/10.1109/mnet.001.2100261.

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Zhu, Julie Yixuan, Bo Tang, and Victor O. K. Li. "A five-layer architecture for big data processing and analytics." International Journal of Big Data Intelligence 6, no. 1 (2019): 38. http://dx.doi.org/10.1504/ijbdi.2019.097399.

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Zhu, Julie Yixuan, Victor O. K. Li, and Bo Tang. "A five-layer architecture for big data processing and analytics." International Journal of Big Data Intelligence 6, no. 1 (2019): 38. http://dx.doi.org/10.1504/ijbdi.2019.10018535.

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