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020 _a9783030480998
_9978-3-030-48099-8
024 7 _a10.1007/978-3-030-48099-8
_2doi
050 4 _aQA75.5-76.95
072 7 _aUNH
_2bicssc
072 7 _aUND
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072 7 _aCOM030000
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072 7 _aUNH
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072 7 _aUND
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082 0 4 _a025.04
_223
245 1 0 _aBig Data in Emergency Management: Exploitation Techniques for Social and Mobile Data
_h[electronic resource] /
_cedited by Rajendra Akerkar.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXVIII, 183 p. 97 illus., 79 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1. Introduction to Emergency Management -- 2. Big Data -- 3. Learning Algorithms for Emergency Management -- 4. Knowledge Graphs and Natural-Language Processing -- 5. Social Media Mining for Disaster Management and Community Resilience -- 6. Big Data-Driven Citywide Human Mobility Modeling for Emergency Management -- 7. Smartphone based Emergency Communication -- 8. Emergency Information Visualisation. .
520 _aThis contributed volume discusses essential topics and the fundamentals for Big Data Emergency Management and primarily focusses on the application of Big Data for Emergency Management. It walks the reader through the state of the art, in different facets of the big disaster data field. This includes many elements that are important for these technologies to have real-world impact. This book brings together different computational techniques from: machine learning, communication network analysis, natural language processing, knowledge graphs, data mining, and information visualization, aiming at methods that are typically used for processing big emergency data. This book also provides authoritative insights and highlights valuable lessons by distinguished authors, who are leaders in this field. Emergencies are severe, large-scale, non-routine events that disrupt the normal functioning of a community or a society, causing widespread and overwhelming losses and impacts. Emergency Management is the process of planning and taking actions to minimize the social and physical impact of emergencies and reduces the community’s vulnerability to the consequences of emergencies. Information exchange before, during and after the disaster periods can greatly reduce the losses caused by the emergency. This allows people to make better use of the available resources, such as relief materials and medical supplies. It also provides a channel through which reports on casualties and losses in each affected area, can be delivered expeditiously. Big Data-Driven Emergency Management refers to applying advanced data collection and analysis technologies to achieve more effective and responsive decision-making during emergencies. Researchers, engineers and computer scientists working in Big Data Emergency Management, who need to deal with large and complex sets of data will want to purchase this book. Advanced-level students interested in data-driven emergency/crisis/disaster management will also want to purchase this book as a study guide.
650 0 _aInformation storage and retrieval systems.
650 0 _aNatural disasters.
650 0 _aComputer networks .
650 0 _aApplication software.
650 0 _aArtificial intelligence.
650 1 4 _aInformation Storage and Retrieval.
650 2 4 _aNatural Hazards.
650 2 4 _aComputer Communication Networks.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aArtificial Intelligence.
700 1 _aAkerkar, Rajendra.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030480981
776 0 8 _iPrinted edition:
_z9783030481001
776 0 8 _iPrinted edition:
_z9783030481018
856 4 0 _uhttps://doi.org/10.1007/978-3-030-48099-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c175701
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