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A multi-source spatio-temporal data cube for large-scale geospatial analysis / Fan Gao in International journal of geographical information science IJGIS, vol 36 n° 9 (September 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 9 (September 2022) . - pp 1853 - 1884 Titre : A multi-source spatio-temporal data cube for large-scale geospatial analysis Type de document : Article/Communication Auteurs : Fan Gao, Auteur ; Peng Yue, Auteur ; Zhipeng Cao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1853 - 1884 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] cube espace-temps [Termes IGN] cyberinfrastructure [Termes IGN] données spatiotemporelles [Termes IGN] Géocube [Termes IGN] hypercube [Termes IGN] informatique en nuage [Termes IGN] intelligence artificielle [Termes IGN] observation de la Terre Résumé : (auteur) Data management and analysis are challenging with big Earth observation (EO) data. Expanding upon the rising promises of data cubes for analysis-ready big EO data, we propose a new geospatial infrastructure layered over a data cube to facilitate big EO data management and analysis. Compared to previous work on data cubes, the proposed infrastructure, GeoCube, extends the capacity of data cubes to multi-source big vector and raster data. GeoCube is developed in terms of three major efforts: formalize cube dimensions for multi-source geospatial data, process geospatial data query along these dimensions, and organize cube data for high-performance geoprocessing. This strategy improves EO data cube management and keeps connections with the business intelligence cube, which provides supplementary information for EO data cube processing. The paper highlights the major efforts and key research contributions to online analytical processing for dimension formalization, distributed cube objects for tiles, and artificial intelligence enabled prediction of computational intensity for data cube processing. Case studies with data from Landsat, Gaofen, and OpenStreetMap demonstrate the capabilities and applicability of the proposed infrastructure. Numéro de notice : A2022-643 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2087222 Date de publication en ligne : 14/06/2022 En ligne : https://doi.org/10.1080/13658816.2022.2087222 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101458 [article]

Big data computing for geospatial applications / Zhenlong Li (2020)  

Public Titre : Big data computing for geospatial applications Type de document : Monographie Auteurs : Zhenlong Li, Éditeur scientifique ; Wenwu Tang, Éditeur scientifique ; Qunying Huang, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 222 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03943-245-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse géovisuelle [Termes IGN] analyse spatio-temporelle [Termes IGN] cyberinfrastructure [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées [Termes IGN] données massives [Termes IGN] informatique en nuage [Termes IGN] métadonnées [Termes IGN] représentation géographique [Termes IGN] réseau sémantique Résumé : (éditeur) The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. Note de contenu : 1- Introduction to Big Data computing for geospatial applications 2- MapReduce-based D-ELT framework to address the challenges of geospatial Big Data 3- High-performance overlay analysis of massive geographic polygons that considers shape complexity in a cloud environment 4- Parallel cellular automata Markov model for land use change prediction over MapReduce framework 5- Terrain analysis in Google Earth Engine: A method adapted for high-gerformance global-scale analysis 6- Integrating geovisual analytics with machine learning for human mobility pattern discovery 7- Social media Big Data mining and spatio-temporal analysis on public emotions for disaster mitigation 8- A novel method of missing road generation in city blocks based on big mobile navigation trajectory data 9- A task-oriented knowledge base for geospatial problem-solving 10- Geographic knowledge graph (GeoKG): A formalized geographic knowledge representation 11- Advanced cyberinfrastructure to enable search of big climate datasets in THREDDS Numéro de notice : 28389 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03943-245-5 En ligne : https://doi.org/10.3390/books978-3-03943-245-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98688

Security risk management for the Internet of things: Technologies and techniques for IoT security, privacy and data protection / John Soldatos (2020)  

Public Titre : Security risk management for the Internet of things: Technologies and techniques for IoT security, privacy and data protection Type de document : Monographie Auteurs : John Soldatos, Éditeur scientifique Editeur : Boston, Delft : Now publishers Année de publication : 2020 Importance : 250 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-68083-682-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Société de l'information [Termes IGN] attaque informatique [Termes IGN] cyberinfrastructure [Termes IGN] données massives [Termes IGN] données numériques [Termes IGN] internet des objets [Termes IGN] sécurité informatique Résumé : (éditeur) In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot. Note de contenu : 1- Introduction 2- Security Data Modelling for Configurable Risk Assessment as a Service in IoT Systems 3- Data-driven IoT Security Using Deep Learning Techniques 4- Privacy Awareness, Risk Assessment, and Control Measures in IoT Platforms: BRAIN-IoT Approach 5- IoT Network Risk Assessment and Mitigation: The SerIoT Approach 6- Chariot-integrated Approach to Safety, Privacy, and Security – CHARIOT IPSE 7- Pattern-driven Security, Privacy, Dependability and Interoperability in IoT 8- Enabling Continuous Privacy Risk Management in IoT Systems 9- Data Protection Compliance Requirements for the Internet of Things 10- Cybersecurity Certification in IoT Environments 11- Firmware Software Analysis at Source Code and Binary Levels 12- End-to-End Security for IoT 13- Blockchain Ledger Solution Affirming Physical, Operational, and Functional Changes in an IoT System 14- Leveraging Interledger Technologies in IoT Security Risk Management Numéro de notice : 25979 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Monographie DOI : 10.1561/9781680836837 En ligne : http://dx.doi.org/10.1561/9781680836837 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96707

Artificial intelligence and the Internet of things / Mercedes Bunz (2018)  

Public Titre : Artificial intelligence and the Internet of things Type de document : Monographie Auteurs : Mercedes Bunz, Auteur ; Laima Janciute, Auteur Editeur : Londres [Royaume-Uni] : University of Westminster Press Année de publication : 2018 Collection : CAMRI Policy Briefs num. 2 Importance : 31 p. ISBN/ISSN/EAN : 978-1-911534-82-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] aide à la décision [Termes IGN] cyberinfrastructure [Termes IGN] éthique [Termes IGN] Infrastructure de données [Termes IGN] internet des objets [Termes IGN] politique publique [Termes IGN] traitement automatique de données [Termes IGN] traitement du langage naturel Résumé : (éditeur) "Through algorithms and artificial intelligence (AI), objects and digital services now demonstrate new skills they did not have before, right up to replacing human activity through pre-programming or by making their own decisions. As part of the internet of things, AI applications are already widely used today, for example in language processing, image recognition and the tracking and processing of data. This policy brief illustrates the potential negative and positive impacts of AI and reviews related policy strategies adopted by the UK, US, EU, as well as Canada and China. Based on an ethical approach that considers the role of AI from a democratic perspective and considering the public interest, the authors make policy recommendations that help to strengthen the positive impact of AI and to mitigate its negative consequences." Note de contenu : 1- What's the issue? 2- Research evidence 3- Review of policy options 4- Policy recommandations Numéro de notice : 25993 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Monographie DOI : 10.16997/book25 En ligne : https://www.uwestminsterpress.co.uk/site/books/m/10.16997/book25 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96769

PolarGlobe : A web-wide virtual globe system for visualizing multidimensional, time-varying, big climate data / Wenwen Li in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 7-8 (July - August 2017) . - pp 1562 - 1582 Titre : PolarGlobe : A web-wide virtual globe system for visualizing multidimensional, time-varying, big climate data Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Sizhe Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1562 - 1582 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Arctique [Termes IGN] changement climatique [Termes IGN] cyberinfrastructure [Termes IGN] données massives [Termes IGN] données multidimensionnelles [Termes IGN] expérience scientifique [Termes IGN] géovisualisation [Termes IGN] globe virtuel [Termes IGN] image multitemporelle [Termes IGN] prototype [Termes IGN] rendu (géovisualisation) [Termes IGN] webGL [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) The increasing research interest in global climate change and the rise of the public awareness have generated a significant demand for new tools to support effective visualization of big climate data in a cyber environment such that anyone from any location with an Internet connection and a web browser can easily view and comprehend the data. In response to the demand, this paper introduces a new web-based platform for visualizing multidimensional, time-varying climate data on a virtual globe. The web-based platform is built upon a virtual globe system Cesium, which is open-source, highly extendable and capable of being easily integrated into a web environment. The emerging WebGL technique is adapted to support interactive rendering of 3D graphics with hardware graphics acceleration. To address the challenges of transmitting and visualizing voluminous, complex climate data over the Internet to support real-time visualization, we develop a stream encoding and transmission strategy based on video-compression techniques. This strategy allows dynamic provision of scientific data in different precisions to balance the needs for scientific analysis and visualization cost. Approaches to represent, encode and decode processed data are also introduced in detail to show the operational workflow. Finally, we conduct several experiments to demonstrate the performance of the proposed strategy under different network conditions. A prototype, PolarGlobe, has been developed to visualize climate data in the Arctic regions from multiple angles. Numéro de notice : A2017-312 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1306863 En ligne : http://dx.doi.org/10.1080/13658816.2017.1306863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85366 [article]

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