Title :
Analytics-Enabled Disaster Preparedness and Emergency Management
Author :
Espana, Karen ; Trogo, Rhia ; Faeldon, James ; Santiago, Juanito ; Sabido, Delfin Jay
Author_Institution :
IBM Philippines, Inc., Quezon City, Philippines
Abstract :
The strongest land falling cyclone, typhoon Haiyan, left in its wake a great need for the government to have a means to monitor damages and manage relief, recovery and rehabilitation efforts. The Intelligent Operations Center for Emergency Management (IOC-EM) was built to address the needs of the Philippine government in the response, recovery and rehabilitation of the affected areas not only of a typhoon such as Haiyan but other calamities as well. These calamities and emergencies may include earthquakes, volcano eruptions and landslides. This paper presents the IOC-EM that integrates and illustrates the data needed by decision makers to make timely and fact-based decisions in the advent of a natural calamity. The data is managed and presented through the use of descriptive analytics. The IOC-EM data warehouse stores historical data that can be used for building more complex predictive and prescriptive analytics models. In this paper, we present the design of the IOC-EM and its data warehouse.
Keywords :
data analysis; data warehouses; disasters; emergency management; emergency services; IOC-EM data warehouse; Intelligent Operations Center for Emergency Management; Philippine government; Typhoon Haiyan; analytics-enabled disaster preparedness; calamities; damage monitoring; data management; decision making; descriptive analytics; earthquakes; historical data storage; land falling cyclone; landslides; natural calamity; predictive analytics model; prescriptive analytics model; recovery efforts; rehabilitation efforts; relief efforts management; timely fact-based decisions; volcano eruptions; Data warehouses; Disaster management; Emergency services; Government; Meteorology; Predictive models; Tropical cyclones; Analytics; Data Warehouse; Disaster Preparedness; Emergency Management; Situational Intelligence;
Conference_Titel :
Data Mining Workshop (ICDMW), 2014 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4799-4275-6
DOI :
10.1109/ICDMW.2014.168