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big data architecture for connected vehicles: Feedback and application examples from an automotive group

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 134卷 374-387页

作者： Mostefaoui, Ahmed Merzoug, Mohammed Amine Haroun, Amir Nassar, Anthony Dessables, Francois Univ Bourgogne Franche Comte FEMTO ST Inst DISC Dept CNRS UMR 6174 F-90000 Belfort France Grp PSA Data & Connect Engn Dept F-78300 Poissy France Grp PSA Data & Connect Engn Dept F-90160 Bessoncourt France

Nowadays, using their onboard built-in sensors and communication devices, connected vehicles (CVs) can perform numerous measurements (speed, temperature, fuel consumption, etc.) and transmit them, in a real-time fashion, to dedicated infrastructure, usually via 4G/5G wireless communications. This raises many opportunities to develop new innovative telematics services, including, among others, driver safety, customer experience, location-based services and infotainment. Indeed, it is expected that there will be roughly 2 billion connected cars by the end of 2025 on the world's roadways, where each of which can produce up to 30 terabytes of data per day. Managing this big automotive data, in real and batch modes, imposes tight constraints on the underlying data management platform. To contribute to this research area, in this paper, we report on a real, in-production automotive big data platform;specifically, the one deployed by Groupe PSA (a French car manufacturer known also as Peugeot-Citroen). In particular, we present the technologies and open-source products used within the different components of this CV platform to gather, store, process, and leverage big automotive data. The proposed architecture is then assessed through realistic experiments, and the obtained results are reported and analyzed. Finally, we also provide examples of deployed automotive applications and reveal the implementation details of one of them (an eco-driving service). (c) 2022 Elsevier B.V. All rights reserved.

关键词： Connected vehicles V2I communication Automotive big data big data architecture Groupe PSA

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big data architecture Design of Smart Grid Based On big data Platform

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Procedia Computer Science 2023年 228卷 356-365页

作者： Liu Yang Tao Wang Bowen Sun Jiangtao Guo Hong Li Sate Grid XinJiang Information & Telecommunication Company Urumqi Xinjiang China

A novel big data-driven approach has been proposed to address the challenges of extensive data and user application difficulties in the smart grid. A smart grid big data architecture based on a big data platform has been designed. The random matrix theory is applied to establish a random matrix algorithm model, which samples data from the smart grid big database and undergoes training and learning to construct a data model for user requirements. Through calculations, the macroscopic data hidden in the smart grid big data is transformed into microscopic data for users' reference, enabling the identification of parameters that affect the normal operation of the smart grid at its core. The computed data can be displayed locally and also uploaded remotely to the SG186 marketing system for various user applications. Additionally, the data can be permanently stored in the cloud through wireless communication. By incorporating the random matrix theory algorithm into the smart grid big data architecture, the proposed solution not only enhances the intuitive visualization of smart grid big data but also provides technical references for future work.

关键词： Smart Grid big data architecture Random Matrix Theory Intuitive Visualization

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Phi: A Generic Microservices-Based big data architecture 18th

Phi: A Generic Microservices-Based Big Data Architecture

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18th European, Mediterranean, and Middle Eastern Conference on Information Systems (EMCIS)

作者： Maamouri, Amine Sfaxi, Lilia Robbana, Riadh Univ Tunis El Manar Fac Math Phys & Nat Sci Tunis Tunis Tunisia Univ Carthage Natl Inst Appl Sci & Technol Tunis Tunisia

ISBN: (纸本)9783030959470;9783030959463

We present in this paper Phi, a generic microservices-based big data architecture dedicated to complex multi-layered systems, that rallies multiple machine learning jobs, stream and batch processing. We show how to apply our architecture to an adaptive e-learning application that adjusts its recommendation to the emotions of the learner on the spot. We deploy our application on the cloud using AWS services, and perform some performance tests to show its feasibility in a realistic environment.

关键词： Microservices big data architecture Cloud-native applications DevOps practices Performance evaluation

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big data architecture for intelligent maintenance: a focus on query processing and machine learning algorithms

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JOURNAL OF big data 2020年第1期7卷 1-26页

作者： Lehmann, Claude Huber, Lilach Goren Horisberger, Thomas Scheiba, Georg Sima, Ana Claudia Stockinger, Kurt Zurich Univ Appl Sci Obere Kirchgasse 2 CH-8400 Winterthur Switzerland Bystron Laser AG Ind Str 21 CH-3362 Niederonz Switzerland

Exploiting available condition monitoring data of industrial machines for intelligent maintenance purposes has been attracting attention in various application fields. Machine learning algorithms for fault detection, diagnosis and prognosis are popular and easily accessible. However, our experience in working at the intersection of academia and industry showed that the major challenges of building an end-to-end system in a real-world industrial setting go beyond the design of machine learning algorithms. One of the major challenges is the design of an end-to-end data management solution that is able to efficiently store and process large amounts of heterogeneous data streams resulting from a variety of physical machines. In this paper we present the design of an end-to-end big data architecture that enables intelligent maintenance in a real-world industrial setting. In particular, we will discuss various physical design choices for optimizing high-dimensional queries, such as partitioning and Z-ordering, that serve as the basis for health analytics. Finally, we describe a concrete fault detection use case with two different health monitoring algorithms based on machine learning and classical statistics and discuss their advantages and disadvantages. The paper covers some of the most important aspects of the practical implementation of such an end-to-end solution and demonstrates the challenges and their mitigation for the specific application of laser cutting machines.

关键词： Prognostics and health management Intelligent maintenance big data architecture Heterogeneous data integration Stream processing Query processing Machine learning

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Examining the Trend of Research on big data architecture: Bibliometric Analysis using Scopus database

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Procedia Computer Science 2024年 234卷 172-179页

big data architecture refers to the infrastructure and processes used to store, manage and analyse large, complex data sets. This topic is widely considered to be challenging due to the ever-increasing volume of data and the need for scalable infrastructure to accommodate it. The aim of this study is to illustrate the current trends in big data architecture through a bibliometric analysis. A comprehensive analysis of big data architecture articles was conducted. Based on the keywords "big data architecture", 151 articles were compiled for further analysis. Between 2014 and 2022, the growth rate of big data architecture literature has increased which most of the studies are presented in English. It is believed that the big data could be extended to other area as there is multiple key words found to be interconnected with it. This study proven that big data architecture is one of the key future types of research which is one of the trending topics.

关键词： big data architecture Bibliometrics, Scopus database, Citation Analysis

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The δ big data architecture for mobility analytics 6

The δ big data architecture for mobility analytics

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6th IEEE International Conference on big data Computing Service and Applications (IEEE bigdataService)

作者： Vouros, George A. Glenis, Apostolos Doulkeridis, Christos Univ Piraeus Dept Digital Syst Piraeus Greece

ISBN: (纸本)9781728170220

Motivated by requirements in mobility analytics that require joint exploitation of streamed and voluminous archival data from diverse and heterogeneous data sources, this paper presents the delta (delta) architecture. Denoting "difference", delta emphasises on the different processing requirements from loosely-coupled components, which serve intertwined processing purposes, forming processing pipelines. The delta architecture, contributes principles for realizing systems, focusing on the requirements from components and pipelines, which are specified as constraints on performance indicators. The article presents a specific instantiation of the delta architecture to satisfy requirements for big data mobility analytics, exploiting real-world mobility data for performing realtime and batch analysis tasks.

关键词： big data architecture Mobility Analytics Software System

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A big data based architecture for collaborative networks: Supply chains mixed-network

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COMPUTER COMMUNICATIONS 2021年 175卷 102-111页

作者： Tamym, Lahcen Benyoucef, Lyes Moh, Ahmed Nait Sidi El Ouadghiri, Moulay Driss Moulay Ismail Univ IA Lab Meknes Morocco Aix Marseille Univ Univ Toulon LIS CNRS Marseille France Picardie Jules Verne Univ PJVU LTI Lab St Quentin en Yvelines France

Nowadays, the world knows a high-speed development and evolution of technologies, vulnerable economic environments, market changes, and personalised consumer trends. The issue and challenge related to enterprises networks design are more and more critical. These networks are often designed for short terms since their strategies must be competitive and better adapted to the environment, social and economical changes. As a solution, to design a flexible and robust network, it is necessary to deal with the trade-off between conflicting qualitative and quantitative criteria such as cost, quality, delivery time, and competition, etc. To this end, using big data (BD) as emerging technology will enhance the real performances of these kinds of networks. Moreover, even if the literature is rich with BD models and frameworks developed for a single supply chain network (SCN), there is a real need to scale and extend these BD models to networked supply chains (NSCs). To do so, this paper proposes a BD architecture to drive a mixed-network of SCs that collaborate in serial and parallel fashions. The collaboration is set up by sharing their resources, capabilities, competencies, and information to imitate a unique organisation. The objective is to increase internal value to their shareholders (where value is seen as wealth) and deliver better external value to the end-customer (where value represents customer satisfaction). Within a mixed-network of SCs, both values are formally calculated considering both serial and parallel networks configurations. Besides, some performance factors of the proposed BD architecture such as security, flexibility, robustness and resilience are discussed.

关键词： big data architecture Collaborative networks Enterprises network Supply chain network Flexibility Robustness

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A comprehensive evaluation system of teaching quality based on big data architecture

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INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2020年第2期30卷 176-189页

作者： Wang, Yanan Xian Fanyi Univ Dept Business Adm Xian 710105 Peoples R China

Aiming at the problems of long response time and high error rate in traditional teaching quality comprehensive evaluation system, a teaching quality comprehensive evaluation system based on big data structure is proposed. Firstly, different evaluation indicators are designed, the principle of teaching quality comprehensive evaluation is designed by using data dictionary, the requirement analysis of teaching quality comprehensive evaluation system is realised by using UML use case model, then the overall framework of the system is designed, and the comprehensive evaluation system of teaching quality is developed and studied by using *** related technology. The experimental results show that the response time of the designed system is shorter than that of the traditional teaching quality comprehensive evaluation system, and the fitting degree between the evaluation results and the actual situation is higher, which fully proves the superiority of the designed system.

关键词： big data architecture teaching quality comprehensive evaluation system design

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Edge Computing with big data Cloud architecture: A Case Study in Smart Building 8

Edge Computing with Big Data Cloud Architecture: A Case Stud...

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8th IEEE International Conference on big data (big data)

作者： Inibhunu, Catherine McGregor, Carolyn A. M. Ontario Tech Univ Oshawa ON Canada Univ Technol Sydney Ultimo NSW Australia

ISBN: (纸本)9781728162515

The growth of buildings embedded with technologies that can monitor the internal building environment with respect to energy consumption such as heating, ventilation, air conditioning, wind, motion as well occupancy have an immense potential. From energy management, occupancy administration, security maintenance as well as improving the health and quality of life for humans in indoor or outdoor spaces. These potentials can be realized by a clear understanding of the interplay between vast environmental conditions, humans and their health as well as the many smart products they interact with in their lives. This is a complex process that requires thorough testing and evaluation within smart buildings simulation environments where multiple buildings data can be generated and then effectively analyzed. This can be facilitated by a robust data management process that utilizes big data computing technologies to harness large volumes, variety and velocity of data that can be captured within smart buildings while maintain the security and privacy of data sources. In this paper we describe a smart building architecture that has been designed and developed for management of data from a smart building. In particular the architecture enables acquisition, processing and distribution of simulated environmental building data to multiple consumers and workflows for further processing and analysis locally and in a high performance cloud computing platform. The research premise is that such an architecture enables effective management of multiple data sources within climatic based simulated testing in smart buildings to further research.

关键词： Climatic Facilities Smart Buildings Simulations big data architecture IoT Cloud Frameworks Edge Computing

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A Case Study on API-Centric big data architecture 12th

A Case Study on API-Centric Big Data Architecture

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12th International Conference on Knowledge Management in Organizations (KMO) - Emerging Technology

作者： Kumaresan, Aravind Liberona, Dario Gnanamurthy, R. K. Univ Vaasa Vaasa Finland Univ Santiago Santiago Chile PPG Inst Technol Coimbatore Tamil Nadu India

ISBN: (纸本)9783319626987;9783319626970

The digital transformation trend is a significant key factor in driving innovations in today's world. As a result the leaders of organizations are constantly under pressure to introduce a cost effective, performance enhanced big data systems to stay competitive. Organizations must run mission critical systems in a stable and secure ways at the same time they have to respond at pace with an ever changing customer expectations. Thus, an ability to scale the business operations to meet the demands from web apps, mobile apps, operational systems and big data analytical tools etc., are crucial. Integration patterns are at the heart of connecting the various systems in organizations for decades but now the industry is focusing more on API-centric approach as an alternate to that. API-centric software enables the component to solve a well detailed problem. So, the other programs can be built upon the existing functionalities of an API. This case study demonstrates the various available approach options to develop an API-centric big data system. It also explains the opportunities and challenges in various approaches. Among the various approaches the case study mainly focuses on utilizing the power of API building software Apigee and Azure cloud technologies to build the API-centric big data trading system for an asset management firm based in London, UK.

关键词： big data architecture API Azure

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