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big data analytics for Earth Sciences:the EarthServer approach

International Journal of Digital Earth 2016年第1期9卷 3-29页

作者： Peter Baumann Paolo Mazzetti Joachim Ungar Roberto Barbera Damiano Barboni Alan Beccati Lorenzo bigagli Enrico Boldrini Riccardo Bruno Antonio Calanducci Piero Campalani Oliver Clements Alex Dumitrua,Mike Grant Pasquale Herzig George Kakaletris John Laxton Panagiota Koltsida Kinga Lipskoch Alireza Rezaei Mahdiraji Simone Mantovani Vlad Merticariu Antonio Messina Dimitar Misev Stefano Natali Stefano Nativi Jelmer Oosthoek Marco Pappalardo James Passmore Angelo Pio Rossi Francesco Rundo Marcus Sen Vittorio Sorbera Don Sullivan Mario Torrisi Leonardo Trovato Maria Grazia Veratelli Sebastian Wagner Large-Scale Scientific Information Systems Jacobs UniversityBremenGermany Rasdaman GmbH BremenGermany CNR-IIA National Research Council of ItalyInstitute of Atmospheric Pollution ResearchFlorenceItaly EOX IT Services GmbH ViennaAustria Consorzio COMETA CataniaItaly Division of Catania Italian National Institute for Nuclear PhysicsCataniaItaly Department of Physics and Astronomy University of CataniaCataniaItaly MEEO S.r.l. FerraraItaly Plymouth Marine Laboratory PlymouthUK Fraunhofer IGD DarmstadtGermany Athena Research and Innovation Center in Information Communication&Knowledge Technologies AthensGreece British Geological Survey EdinburghUK Software Engineering Italia S.r.l. CataniaItaly British Geological Survey KeyworthUK NASA Ames Research Center Moffett FieldCAUSA

big data analytics is an emerging field since massive storage and computing capabilities have been made available by advanced *** and Environmental sciences are likely to benefit from big data analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and ***,Earth Science data and applications present specificities in terms of relevance of the geospatial information,wide heterogeneity of data models and formats,and complexity of ***,big Earth data analytics requires specifically tailored techniques and *** EarthServer big Earth data analytics engine offers a solution for coverage-type datasets,built around a high performance array database technology,and the adoption and enhancement of standards for service interaction(OGC WCS and WCPS).The EarthServer solution,led by the collection of requirements from scientific communities and international initiatives,provides a holistic approach that ranges from query languages and scalability up to mobile access and *** result is demonstrated and validated through the development of lighthouse applications in the Marine,Geology,Atmospheric,Planetary and Cryospheric science domains.

关键词： big data big data analytics array databases Earth Sciences interoperability standards

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big data analytics in turbulent contexts: towards organizational change for enhanced agility

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PRODUCTION PLANNING & CONTROL 2022年第2-3期33卷 105-122页

作者： Barlette, Yves Baillette, Pamela Montpellier Business Sch MBS MRM Res Ctr Montpellier France Univ Bordeaux IRGO Res Ctr Bordeaux France

This conceptual paper addresses the capabilities offered by big data analytics (BDA), critical in turbulent environments, in the context of the Industry 4.0 revolution. BDA enhances organizational agility, as it is highly valuable for the identification of opportunities and threats (i.e. the sensing process of agility);however, with the exception of aiding decision-making tools, seizing its benefits for reacting (i.e. responding) is more difficult without organizational adaptation. Hence, the changes required to exploit the full capabilities offered by BDA, particularly at the organizational level, are considerable. From a theoretical perspective, through a narrative literature review, this paper highlights several possibilities of organizational changes for enhancing a company's agility and leveraging BDA to achieve enhanced performance in turbulent contexts. The findings are then related to three organizational theories. This work ends in a conceptual model and several research paths for future studies. From a managerial perspective, this paper highlights, through several examples, the importance of top management's role in achieving the necessary organizational changes to enhance the companies' responding capabilities.

关键词： big data analytics agility Industry 4 0 organization turbulence

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big data analytics in Intelligent Transportation Systems: A Survey

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第1期20卷 383-398页

作者： Zhu, Li Yu, Fei Richard Wang, Yige Ning, Bin Tang, Tao Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

关键词： big data analytics intelligent transportation systems (ITS) machine learning transportation

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big data analytics enabled deep convolutional neural network for the diagnosis of cancer

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第2期66卷 905-931页

作者： Awotunde, Joseph Bamidele Panigrahi, Ranjit Shukla, Shubham Panda, Baidyanath Bhoi, Akash Kumar Univ Ilorin Fac Informat & Commun Sci Dept Comp Sci Ilorin 240003 Kwara State Nigeria Sikkim Manipal Univ Sikkim Manipal Inst Technol Dept Comp Applicat Majitar 737136 Sikkim India KIET Grp Inst Elect & Commun Engn Delhi NCR Ghaziabad 201206 India LTIMindtree 1 Amer Row3 Rd Floor Hartford CT 06103 USA Sikkim Manipal Univ Res Directorate Gangtok 737102 Sikkim India

Artificial intelligence (AI) has been shown to be a formidable instrument in managing big Healthcare data, and it has seen considerable success in bioinformatics. The advancement of big data in biological sciences has given rise to big data analytics (BDA) and artificial intelligence (AI). Because the AI methodologies used in bioinformatics are parallel and iterative, scalable big data management employing distributed and parallel technology is possible. The growth of bioinformatics has resulted in significant storage and administration issues;to share information, such large amounts of data must be handled efficiently. Computational developments in information technology have enabled analytical systems to cope with such data. Therefore, this study emphasizes the impact of big data and BDA in bioinformatics. A practical use of BDAs and AI in cancer classification was given, combining a unique Analysis of Variance (ANOVA) approach with Ant Colony Optimization (ACO) as a hybrid feature selection to pick significant genes while minimizing gene redundancy. Deep Convolutional Neural Networks (DCNN) were employed to classify the datasets. It is because microarray data are produced from gene expression data, and it frequently has a limited number of samples but a huge feature collection size. Using the same datasets, the suggested system outperformed earlier state-of-the-art approaches. The results of the proposed model on all the Leukemia, DLBCL, Colon, and SRDCT datasets revealed an average classification accuracy of 97.7%, 99.9%, 99.9% and 100%, respectively.

关键词： Bioinformatics big data analytics Artificial intelligence Deep convolutional neural network Ant colony optimization Analysis of variance Breast cancer

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big data analytics business value and firm performance: linking with environmental context

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020年第18期58卷 5456-5476页

作者： Vitari, Claudio Raguseo, Elisabetta Univ Toulon & Var Aix Marseille Univ CERGAM Aix En Provence France Politecn Torino Dept Management & Prod Engn Observ Online Platform Econ European Commiss Turin Italy

Previous studies, grounded on the resource based view, have already explored the relationship between the business value that big data analytics (BDA) can bring to firm performance. However, the role played by the environmental characteristics in which companies operate has not been investigated in the literature. We inform the theory, in that direction, via the integration of the contingency theory to the resource based view theory of the firm. This original and integrative model examines the moderating influence of environmental features on the relationship between BDA business value and firm performance. The combination of survey data and secondary financial data on a representative sample of medium and large companies makes possible the statistical validation of our research model. The results offer evidence that BDA business value leads to higher firm performance, namely financial performance, market performance and customer satisfaction. More original is the demonstration that this relationship is stronger in munificent environments, while the dynamism of the environment does not have any moderating effect on the performance of BDA solutions. It means that managers working for firms in markets with a growing demand are in the best position to profit from BDA.

关键词： resource based view contingency theory big data analytics customer satisfaction financial performance market performance munificence dynamism

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big data analytics Implications on Central Banking Green Technological Progress

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第5期23卷 2065-2087页

作者： Ahmed, Elsadig Musa Multimedia Univ Fac Business Melaka Malaysia

This paper examines big data analytics implications on the central banking financial system's technological progress. A digital technological progress framework and model is established to analyze the economy's aggregate supply via covering the monetary policy, big data analytics, pollutants emissions as independent variables and the economy's aggregate demand as a moderating variable in a modified extensive growth theory framework and model to compute the productivity indicators and the total factor productivity (TFP) as the central banking technological progress that combined the mentioned variables qualities contribution. Besides, data analytics positive and negative externalities that include data analytics shortcomings as unpriced undesirable output in the form of cybersecurity and pollutants' emissions among other proxies are internalized in the framework and the model to integrate the digital technology innovation with digital technology shortcomings and climate change. This revised extensive theory framework and model is a remarkable technique comprehensive of the technological progress matters and sustainable economic development and is considered one of the most important sustainable development and long-run economic growth proportions in the central banking financial system functions to manage the economy's aggregate supply and demand that unnoticed by previous studies.

关键词： Central bank aggregate supply and demand big data analytics machine learning artificial intelligence financial system technological progress externalities

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big data analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks

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IEEE ACCESS 2018年 6卷 32328-32338页

作者： Kibria, Mirza Golam Kien Nguyen Villardi, Gabriel Porto Zhao, Ou Ishizu, Kentaro Kojima, Fumihide Natl Inst Informat & Commun Technol Wireless Networks Res Ctr Wireless Syst Lab Yokosuka Kanagawa 2390847 Japan

The next-generation wireless networks are evolving into very complex systems because of the very diversified service requirements, heterogeneity in applications, devices, and networks. The network operators need to make the best use of the available resources, for example, power, spectrum, as well as infrastructures. Traditional networking approaches, i.e., reactive, centrally-managed, one-size-fits-all approaches, and conventional data analysis tools that have limited capability (space and time) are not competent anymore and cannot satisfy and serve that future complex networks regarding operation and optimization cost effectively. A novel paradigm of proactive, self-aware, self-adaptive, and predictive networking is much needed. The network operators have access to large amounts of data, especially from the network and the subscribers. Systematic exploitation of the big data dramatically helps in making the system smart, intelligent, and facilitates efficient as well as cost-effective operation and optimization. We envision data-driven next-generation wireless networks, where the network operators employ advanced data analytics, machine learning (ML), and artificial intelligence. We discuss the data sources and strong drivers for the adoption of the data analytics, and the role of ML, artificial intelligence in making the system intelligent regarding being self-aware, self-adaptive, proactive and prescriptive. A set of network design and optimization schemes are presented concerning data analytics. This paper concludes with a discussion of challenges and the benefits of adopting big data analytics, ML, and artificial intelligence in the next-generation communication systems.

关键词： big data analytics machine learning artificial intelligence next-generation wireless

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big data analytics and Computational Intelligence for Cyber-Physical Systems: Recent trends and state of the art applications

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 105卷 766-778页

作者： Iqbal, Rahat Doctor, Faiyaz More, Brian Mahmud, Shahid Yousuf, Usman Interact Coventry Ltd Coventry Univ Technol PkPuma Way Coventry CV1 2TT W Midlands England Coventry Univ Sch Comp Elect & Math Fac Engn Environm & Comp Priory St Coventry CV1 5FB W Midlands England Univ Essex Sch Comp Sci & Elect Engn Wivenhoe Pk Colchester CO4 3SQ Essex England Coventry Univ Enterprise Ltd Coventry Univ Technol PkPuma Way Coventry CV1 2TT W Midlands England

big data is fuelling the digital revolution in an increasingly knowledge driven and connected society by offering big data analytics and computational intelligence based solutions to reduce the complexity and cognitive burden on accessing and processing large volumes of data. In this paper, we discuss the importance of big data analytics and computational intelligence techniques applied to data produced from the myriad of pervasively connected machines and personalized devices offering embedded and distributed information processing capabilities. We provide a comprehensive survey of computational intelligence techniques appropriate for the effective processing and analysis of big data. We discuss a number of exemplar application areas that generate big data and can hence benefit from its effective processing. State of the art research and novel applications in health-care, intelligent transportation and social network sentiment analysis, are presented and discussed in the context of big data, Cyber-Physical Systems (CPS), and Computational Intelligence (Cl). We present a data modelling methodology, which introduces a novel biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). The HSTSM modelling approach incorporates a number of soft computing techniques such as: deep belief networks, auto-encoders, agglomerative hierarchical clustering and temporal sequence processing, in order to address the computational challenges arising from analysing and processing large volumes of diverse data to provide an effective big data analytics tool for diverse application areas. A conceptual cyber-physical architecture, which can accommodate and benefit from the proposed methodology, is further presented. (C) 2017 Elsevier B.V. All rights reserved.

关键词： big data big data analytics Cyber-Physical Systems Computational Intelligence Cl and CPS applications HSTSM

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big data analytics for wireless and wired network design: A survey

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COMPUTER NETWORKS 2018年第Feb.26期132卷 180-199页

作者： Hadi, Mohammed S. Lawey, Ahmed Q. El-Gorashi, Taisir E. H. Elmirghani, Jaafar M. H. Univ Leeds Sch Elect & Elect Engn Leeds W Yorkshire England

Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks' control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks. (C) 2018 Elsevier B.V. All rights reserved.

关键词： big data analytics Network design Self-optimization Self-configuration Self-healing network

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big data analytics with swarm intelligence

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INDUSTRIAL MANAGEMENT & data SYSTEMS 2016年第4期116卷 646-666页

作者： Cheng, Shi Zhang, Qingyu Qin, Quande Univ Nottingham Div Comp Sci Ningbo Zhejiang Peoples R China Shenzhen Univ Dept Management Sci Shenzhen Peoples R China Res Inst Business Analyt & Supply Chain Managemen Shenzhen Peoples R China Beijing Inst Technol Ctr Energy & Environm Policy Res Beijing Peoples R China

Purpose - The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. The paper aims to discuss these issues. Design/methodology/approach - In this paper, the potential application of SI in big data analytics is analyzed. The correspondence and association between big data analytics and SI techniques are discussed. As an example of the application of the SI algorithms in the big data processing, a commodity routing system in a port in China is introduced. Another example is the economic load dispatch problem in the planning of a modern power system. Findings - The characteristics of big data include volume, variety, velocity, veracity, and value. In the SI algorithms, these features can be, respectively, represented as large scale, high dimensions, dynamical, noise/surrogates, and fitness/objective problems, which have been effectively solved. Research limitations/implications - In current research, the example problem of the port is formulated but not solved yet given the ongoing nature of the project. The example could be understood as advanced IT or data processing technology, however, its underlying mechanism could be the SI algorithms. This paper is the first step in the research to utilize the SI algorithm to a big data analytics problem. The future research will compare the performance of the method and fit it in a dynamic real system. Originality/value - Based on the combination of SI and data mining techniques, the authors can have a bette

关键词： Evolutionary computation Optimization data mining big data Swarm intelligence big data analytics

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