alex singleton (159 risultati)

Hardcover. Condizione: Used; Good. Dispatched, from the UK, within 48 hours of ordering. This book is in good condition but will show signs of previous ownership. Please expect some creasing to the spine and/or minor damage to the cover. Inscription on the first page, typically just a name but may include a dedication or a brief personal message.

Condizione: Good. Ships from the UK. Former library book; may include library markings. Used book that is in clean, average condition without any missing pages.

Condizione: Very Good. Most items will be dispatched the same or the next working day. A copy that has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Condizione: New. Alex Singleton (illustratore). In.

Condizione: Very Good. 1st Edition. Ships from the UK. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects.

Paperback. Condizione: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New. Creative, Alex (illustratore). Print on Demand.

Gebundene Ausgabe. Condizione: Sehr gut. Gebraucht - Sehr gut - ungelesen,als Mängelexemplar gekennzeichnet, mit leichten Mängeln an Schnitt oder Umschlag durch Lager- oder Transportschaden -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 435 pp. Englisch.

Softcover. Condizione: Near Fine. First Edition. Near Fine blue and black paperback with french flaps. Very slight reading wear. 319 pages, unmarked. Molly Detwiler, Thomas Rees, Jaime Britton, Shazelle Goulet, Melanie Farley, Alex Vikmanis, Sara Consolati, Joshua Breitbart, David Addona, Michelle Krivanek, Erin Morrill, Lisa Gordon, Seth Singleton, Brittany Ham, Wayne White, Christine Meade, Emma Rose Miller, Brenda M. Orozco, Emily Jern-Miller, Molly Prentiss, Sara Fontaine, Stephanie Suggs, Junior Clemons, Julie littman, sarah L. Webb, Madeline Zinn, Dustin Acton ; GWSP C8C; 9 X 7 X 1 inches; 319 pages.

Condizione: New. Print on Demand.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New. In.

Taschenbuch. Condizione: 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 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.