Description
This 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.
Author: Rajendra Akerkar
Publisher: Springer
Published: 09/15/2020
Pages: 183
Binding Type: Hardcover
Weight: 1.02lbs
Size: 9.21h x 6.14w x 0.50d
ISBN13: 9783030480981
ISBN10: 3030480984
BISAC Categories:
- Computers | System Administration | Storage & Retrieval
- Nature | Natural Disasters
- Computers | Networking | Hardware
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.
Author: Rajendra Akerkar
Publisher: Springer
Published: 09/15/2020
Pages: 183
Binding Type: Hardcover
Weight: 1.02lbs
Size: 9.21h x 6.14w x 0.50d
ISBN13: 9783030480981
ISBN10: 3030480984
BISAC Categories:
- Computers | System Administration | Storage & Retrieval
- Nature | Natural Disasters
- Computers | Networking | Hardware
About the Author
Rajendra Akerkar holds master's in applied mathematics and doctorate in computer science. He is the professor and head of Big Data Research Group at Vestlansforsking (Western Norway Research Institute), Norway. His research and teaching experience includes over 28 years in the Academia spanning universities in Asia, Europe and North America. His research focuses on application of big data methods to real world challenges, particularly transport, public security, energy and utilities. He is actively involved in several international ICT initiatives for more than 22 years.