Rise big spatial data (20 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2016
Serie: Buch 55 von 74 - Lecture Notes in Geoinformation and Cartography
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Rise of Big Spatial Data
Ivan, Igor (EDT); Singleton, Alex (EDT); Horák, Jirí (EDT); Inspektor, Toma s? (EDT)
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Rise of Big Spatial Data
Ivan, Igor (EDT); Singleton, Alex (EDT); Horák, Jirí (EDT); Inspektor, Toma s? (EDT)
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Rise of Big Spatial Data
Ivan, Igor (EDT); Singleton, Alex (EDT); Horák, Jirí (EDT); Inspektor, Toma s? (EDT)
Sprache: Englisch
Verlag: Springer, 2016
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Taschenbuch. Zustand: Neu. The Rise of Big Spatial Data | Igor Ivan (u. a.) | Taschenbuch | Lecture Notes in Geoinformation and Cartography | xxvii | Englisch | 2018 | Springer | EAN 9783319832166 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]…com | Anbieter: preigu.
Rise of Big Spatial Data
Ivan, Igor (EDT); Singleton, Alex (EDT); Horák, Jirí (EDT); Inspektor, Toma s? (EDT)
Sprache: Englisch
Verlag: Springer, 2016
Serie: Buch 55 von 74 - Lecture Notes in Geoinformation and Cartography
- Hardcover
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This edited volume gathers the proceedings of the Symposium GIS Ostrava 2016, the Rise of Big Spatial Data, held at the Technical University of Ostrava, Czech Republic, March 16-18, 2016.Combining theoretical papers and applications by authors from a…round the globe, it summarises the latest research findings in the area of big spatial data and key problems related to its utilisation.Welcome to dawn of the big data era: though it's in sight, it isn't quite here yet. Big spatial data is characterised by three main features: volume beyond the limit of usual geo-processing, velocity higher than that available using conventional processes, and variety, combining more diverse geodata sources than usual. The popular term denotes a situation in which one or more of these key properties reaches a point at which traditional methods for geodata collection, storage, processing, control, analysis, modelling, validation and visualisation fail to provide effective solutions. >Entering the era of big spatial data calls for finding solutions that address all 'small data' issues that soon create 'big data' troubles. Resilience for big spatial data means solving the heterogeneity of spatial data sources (in topics, purpose, completeness, guarantee, licensing, coverage etc.), large volumes (from gigabytes to terabytes and more), undue complexity of geo-applications and systems (i.e. combination of standalone applications with web services, mobile platforms and sensor networks), neglected automation of geodata preparation (i.e. harmonisation, fusion), insufficient control of geodata collection and distribution processes (i.e. scarcity and poor quality of metadata and metadata systems), limited analytical tool capacity (i.e. domination of traditional causal-driven analysis), low visual system performance, inefficient knowledge-discovery techniques (for transformation of vast amounts of information into tiny and essential outputs) and much more. These trends are accelerating as sensors become more ubiquitous around the world.
The Rise of Big Spatial Data
Ivan, Igor (Editor)/ Singleton, Alex (Editor)/ Horák, Jirí (Editor)/ Inspektor, Toma s? (Editor)
Sprache: Englisch
Verlag: Springer Verlag, 2016
Serie: Buch 55 von 74 - Lecture Notes in Geoinformation and Cartography
- Hardcover
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Hardcover. Zustand: Brand New. 438 pages. 9.25x6.25x1.25 inches. In Stock.
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents papers summarizing the latest research findings in the area of big spatial dataOffers a multidisciplinary focus with papers analysing key problems related to big spatial data utilisationProvides a combination o…f theoretical papers .
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Gebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents papers summarizing the latest research findings in the area of big spatial dataOffers a multidisciplinary focus with papers analysing key problems related to big spatial data utilisationProvides a com…bination of theoretical papers .
Sprache: Englisch
Verlag: Springer International Publishing Okt 2016, 2016
Serie: Buch 55 von 74 - Lecture Notes in Geoinformation and Cartography
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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This edited volume gathers the proceedings of the Symposium GIS Ostrava 2016, the Rise of Big Spatial Data, held at the Technical University of Ostrava, Czech Republic, March 16-18, 2016.Combining theoretical papers and applications by autho…rs from around the globe, it summarises the latest research findings in the area of big spatial data and key problems related to its utilisation.Welcome to dawn of the big data era: though it's in sight, it isn't quite here yet. Big spatial data is characterised by three main features: volume beyond the limit of usual geo-processing, velocity higher than that available using conventional processes, and variety, combining more diverse geodata sources than usual. The popular term denotes a situation in which one or more of these key properties reaches a point at which traditional methods for geodata collection, storage, processing, control, analysis, modelling, validation and visualisation fail to provide effective solutions. >Entering the era of big spatial data calls for finding solutions that address all 'small data' issues that soon create 'big data' troubles. Resilience for big spatial data means solving the heterogeneity of spatial data sources (in topics, purpose, completeness, guarantee, licensing, coverage etc.), large volumes (from gigabytes to terabytes and more), undue complexity of geo-applications and systems (i.e. combination of standalone applications with web services, mobile platforms and sensor networks), neglected automation of geodata preparation (i.e. harmonisation, fusion), insufficient control of geodata collection and distribution processes (i.e. scarcity and poor quality of metadata and metadata systems), limited analytical tool capacity (i.e. domination of traditional causal-driven analysis), low visual system performance, inefficient knowledge-discovery techniques (for transformation of vast amounts of information into tiny and essential outputs) and much more. These trends are accelerating as sensors become more ubiquitous around the world. 436 pp. Englisch.
Sprache: Englisch
Verlag: Springer International Publishing Jun 2018, 2018
Serie: Buch 55 von 74 - Lecture Notes in Geoinformation and Cartography
- Softcover
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This edited volume gathers the proceedings of the Symposium GIS Ostrava 2016, the Rise of Big Spatial Data, held at the Technical University of Ostrava, Czech Republic, March 16-18, 2016.Combining theoretical papers and applications b…y authors from around the globe, it summarises the latest research findings in the area of big spatial data and key problems related to its utilisation.Welcome to dawn of the big data era: though it's in sight, it isn't quite here yet. Big spatial data is characterised by three main features: volume beyond the limit of usual geo-processing, velocity higher than that available using conventional processes, and variety, combining more diverse geodata sources than usual. The popular term denotes a situation in which one or more of these key properties reaches a point at which traditional methods for geodata collection, storage, processing, control, analysis, modelling, validation and visualisation fail to provide effective solutions. >Entering the era of big spatial data calls for finding solutions that address all 'small data' issues that soon create 'big data' troubles. Resilience for big spatial data means solving the heterogeneity of spatial data sources (in topics, purpose, completeness, guarantee, licensing, coverage etc.), large volumes (from gigabytes to terabytes and more), undue complexity of geo-applications and systems (i.e. combination of standalone applications with web services, mobile platforms and sensor networks), neglected automation of geodata preparation (i.e. harmonisation, fusion), insufficient control of geodata collection and distribution processes (i.e. scarcity and poor quality of metadata and metadata systems), limited analytical tool capacity (i.e. domination of traditional causal-driven analysis), low visual system performance, inefficient knowledge-discovery techniques (for transformation of vast amounts of information into tiny and essential outputs) and much more. These trends are accelerating as sensors become more ubiquitous around the world. 436 pp. Englisch.
Sprache: Englisch
Verlag: Springer, Palgrave Macmillan Jun 2018, 2018
Serie: Buch 55 von 74 - Lecture Notes in Geoinformation and Cartography
- Softcover
- Print-on-Demand
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This edited volume gathers the proceedings of the Symposium GIS Ostrava 2016, the Rise of Big Spatial Data, held at the Technical University of Ostrava, Czech Republic, March 16-18, 2016. Combining theoretical papers and applications by a…uthors from around the globe, it summarises the latest research findings in the area of big spatial data and key problems related to its utilisation.Welcome to dawn of the big data era: though it's in sight, it isn't quite here yet. Big spatial data is characterised by three main features: volume beyond the limit of usual geo-processing, velocity higher than that available using conventional processes, and variety, combining more diverse geodata sources than usual. The popular term denotes a situation in which one or more of these key properties reaches a point at which traditional methods for geodata collection, storage, processing, control, analysis, modelling, validation and visualisation fail to provide effective solutions.>Entering the era of big spatial data calls for finding solutions that address all 'small data' issues that soon create 'big data' troubles. Resilience for big spatial data means solving the heterogeneity of spatial data sources (in topics, purpose, completeness, guarantee, licensing, coverage etc.), large volumes (from gigabytes to terabytes and more), undue complexity of geo-applications and systems (i.e. combination of standalone applications with web services, mobile platforms and sensor networks), neglected automation of geodata preparation (i.e. harmonisation, fusion), insufficient control of geodata collection and distribution processes (i.e. scarcity and poor quality of metadata and metadata systems), limited analytical tool capacity (i.e. domination of traditional causal-driven analysis), low visual system performance, inefficient knowledge-discovery techniques (for transformation of vast amounts of information into tiny and essential outputs) and much more. These trends are accelerating as sensors become more ubiquitous around the world.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 436 pp. Englisch.
Sprache: Englisch
Verlag: Palgrave Macmillan, Springer Okt 2016, 2016
Serie: Buch 55 von 74 - Lecture Notes in Geoinformation and Cartography
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Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This edited volume gathers the proceedings of the Symposium GIS Ostrava 2016, the Rise of Big Spatial Data, held at the Technical University of Ostrava, Czech Republic, March 16-18, 2016. Combining theoretical papers and applications by authors…from around the globe, it summarises the latest research findings in the area of big spatial data and key problems related to its utilisation.Welcome to dawn of the big data era: though it's in sight, it isn't quite here yet. Big spatial data is characterised by three main features: volume beyond the limit of usual geo-processing, velocity higher than that available using conventional processes, and variety, combining more diverse geodata sources than usual. The popular term denotes a situation in which one or more of these key properties reaches a point at which traditional methods for geodata collection, storage, processing, control, analysis, modelling, validation and visualisation fail to provide effective solutions.>Entering the era of big spatial data calls for finding solutions that address all 'small data' issues that soon create 'big data' troubles. Resilience for big spatial data means solving the heterogeneity of spatial data sources (in topics, purpose, completeness, guarantee, licensing, coverage etc.), large volumes (from gigabytes to terabytes and more), undue complexity of geo-applications and systems (i.e. combination of standalone applications with web services, mobile platforms and sensor networks), neglected automation of geodata preparation (i.e. harmonisation, fusion), insufficient control of geodata collection and distribution processes (i.e. scarcity and poor quality of metadata and metadata systems), limited analytical tool capacity (i.e. domination of traditional causal-driven analysis), low visual system performance, inefficient knowledge-discovery techniques (for transformation of vast amounts of information into tiny and essential outputs) and much more. These trends are accelerating as sensors become more ubiquitous around the world.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 436 pp. Englisch.
Sprache: Englisch
Verlag: Palgrave Macmillan, Springer, 2016
Serie: Buch 55 von 74 - Lecture Notes in Geoinformation and Cartography
- Hardcover
- Print-on-Demand
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Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This edited volume gathers the proceedings of the Symposium GIS Ostrava 2016, the Rise of Big Spatial Data, held at the Technical University of Ostrava, Czech Republic, March 16-18, 2016.Combining theoretical papers and applications by authors fr…om around the globe, it summarises the latest research findings in the area of big spatial data and key problems related to its utilisation.Welcome to dawn of the big data era: though it's in sight, it isn't quite here yet. Big spatial data is characterised by three main features: volume beyond the limit of usual geo-processing, velocity higher than that available using conventional processes, and variety, combining more diverse geodata sources than usual. The popular term denotes a situation in which one or more of these key properties reaches a point at which traditional methods for geodata collection, storage, processing, control, analysis, modelling, validation and visualisation fail to provide effective solutions. >Entering the era of big spatial data calls for finding solutions that address all 'small data' issues that soon create 'big data' troubles. Resilience for big spatial data means solving the heterogeneity of spatial data sources (in topics, purpose, completeness, guarantee, licensing, coverage etc.), large volumes (from gigabytes to terabytes and more), undue complexity of geo-applications and systems (i.e. combination of standalone applications with web services, mobile platforms and sensor networks), neglected automation of geodata preparation (i.e. harmonisation, fusion), insufficient control of geodata collection and distribution processes (i.e. scarcity and poor quality of metadata and metadata systems), limited analytical tool capacity (i.e. domination of traditional causal-driven analysis), low visual system performance, inefficient knowledge-discovery techniques (for transformation of vast amounts of information into tiny and essential outputs) and much more. These trends are accelerating as sensors become more ubiquitous around the world.
