Global societies are facing increasing various challenges and opportunities due to rapidly shifting global environment. To be prepared and coped with the future well, many nations and institutions are actively doing horizon scanning and foresight activities based on data. This forum will have several presentations about data-driven foresight activities, processes, and use cases as well as development of data-driven horizon scanning technologies and tools and their application to foresight. We hope this forum would be a chance to share experience and know-how among participants.
1.Risk Assessment and Horizon Scanning – A Systematic Approach to Foresight, Lock Pin CHEW, Director at NSCC, NSCS, Singapore, Jenard NG, Assistant Director at RPO, NSCC, NSCS, Singapore
Abstract: The Risk Assessment and Horizon Scanning (RAHS) Programme Office (RPO) is a unit created within the National Security Coordination Secretariat (NSCS) dedicated towards identifying weak signals and emergent threats. In this presentation, the speakers will explain how RPO conducts horizon scanning, sensemaking, scenario generation and risk assessment. They will share about the RAHS System, a computer programme developed to support analysts in foresight. They will share examples of how the work of the RPO has contributed to capability development and contingency planning to mitigate national security risks.
2.Big data analytics in horizon scan: opportunities and challenges in the Korean public sector, Young-Joo LEE, Ph.D., Principal Researcher at Future Strategy Center, NIA, Korea
Abstract: Coming of the age of big data stimulates both the public and private sector to utilize big data for problem-solving and decision making. However, applying big data analytics for future related area such as prediction, foresight, and future strategy development, is in the early stage particularly in the public sector. The current presentation deals with the applied case of data-driven horizon scanning conducted by the Future Strategy Center at National Information Society Agency (NIA) in South Korea. First, we discuss the conceptual distinction between traditional foresight and data-driven foresight in terms of the methodology, data, and field of applications. Next, we introduce how we discovered the possibilities of big data analytics for foresight and how we established a framework and methodology. Finally, we continue with the review of current status and several challenges we face to increase the utilization of data-driven horizon scan in the policy area. The implications and future directions concludes the presentation.
Keywords: data-driven horizon scanning; data-driven foresight; big data analytics
3.Transportation BIGDATA Supporting Disaster Network, Jun LEE, Ph.D., Chief Director at KOTI, Korea
Abstract: With the introduction of ITS (Intelligent Transportation System), the road infrastructures have the information collection and control system radically. In this study, I will introduce the View-T; transport Big Data Platform developed by Korea Transport Research Institute (KOTI), and show the cases used in the field of firefighting. In particular, It will present a business model that can be introduced in Korea and suggest a future plan for utilizing the BigData.
Keywords: intelligent transportation system; transport big data platform; business model
4.Extracting disaster-related information from news data, Do-Woo KIM, Ph.D., Researcher at NDMI, Korea
Abstract: This study develops a technology that systematically extracts disaster related information from Korean news. News is data with the following five advantages in terms of disaster management. First, it contains information on comprehensive risks. Second, there is a story about the disaster situation. Third, data for quite a long time can be secured. Fourth, they are produced in real time in common worldwide, and are easily shared over the Internet. To extract disaster information from news, we applied event extraction technology to disaster domain. First, an ontology for disaster related terminology was constructed, and news data sets for 27 disaster types were build based on the ontology. Second, we aggregated all of the predicate patterns in the classified news texts, and artificially identified the 2930 predicate pattern describing situations related to disaster. Based on the selected predicate, the historical events from minor accidents to major disasters were scanned, and specific contextual information such as the victim, cause, place, and time of the event were extracted. This technology has enabled us to efficiently access information on specific disaster situations as well as the long-term disaster outbreak of 27 years (1991 to present).
Keywords: disaster-related information; ontology; predicate patterns; contextual information
5.Event-based surveillance and risk assessment on possible imported infectious diseases in Korea, Chaeshin CHU, Ph.D., Deputy Scientific Director at Division of Risk Assessment and International Cooperation, KCDC, Korea
Abstract: Recent increase in international trade and travel makes the world more susceptible to a public health emergency caused by emerging infectious diseases. The 2009 influenza A/H1N1 pandemic around the world and the MERS-CoV outbreak in Korea showed the imported infectious disease can make a huge impact not only public health but also entire economy. After MERS-CoV outbreak, the Republic of Korea has reformed the National Disease Control System, and the Korea Centers for Disease Control and Prevention (KCDC) have a new division to conduct risk assessment on possible public health emergency. KCDC collects information from the websites of international organizations, ministries of health, along with health-related websites including ProMED, CIDRAP, and GPHIN. The collected information through daily event-based surveillance go through daily risk assessment and the information is shared with relevant ministries/agencies, local government and Korea Medical Association on a daily, weekly and monthly basis.
Keywords: national disease control system; risk assessment; public health emergency; event-based surveillance
6.Development of proactive detection technology for emerging disease prediction, Insung AHN, Ph.D., Principal Researcher at KISTI and Principal Investigator at KRICT, Korea
Abstract: As the 2009 influenza virus pandemic influenza pandemic and the 2015 Middle East Respiratory Syndrome suffered from socio-economic disruption due to domestic influx, there was growing interest in how to anticipate the upcoming new infectious diseases is. In this presentation, I will introduce the disease simulation technologies that are rapidly developing in the United States and some advanced countries, and newly launched national project named “Convergent solution for emerging virus infection” conducted by 9 governmental research institutes in Korea. KISTI is responsible for the development of disease-spreading prevention technology in this project. Proactive detection of disease was done using machine learning techniques, and the main data sources were disease information from neighboring countries or major exchange countries in real time news data form. Another important point in predicting the disease pandemic is the occurrence of mutations in existing viruses. In this presentation, I’d like to share not only the occurrence of the disease, but also some results of the machine learning study using the disease variation data.
7.What should horizon scanning provide for analysts?, Seungwoo LEE, Ph.D., Principal Researcher at KISTI, Korea, Nam Hee CHOI, Ph.D., Professor at KNUT, Korea
Abstract: Horizon scanning is a systematic process of collecting evidences for finding out weak signals and could be viewed as a tool of supporting analysts’ foresight activities. Analysts usually spend too much times in searching and reading evidences of weak signals, comparing to times spending in analyzing driving factors and their relationships, establishing possible scenarios, and making a strategy corresponding to each scenarios. What should horizon scanning provide for analysts, especially to save times spending in searching evidences of weak signals? Current horizon scanning mostly focuses on extracting and visualizing keywords (or entities) and their collocation but it is time to more advance the intelligence capability of horizon scanning. To explore what should be provided (mined) for analysts, we followed analysts’s work of driving factors and their relationships in a case domain of infectious disease. We also applied system dynamics to simulate a case of inflow and spread of infectious disease from overseas and find out which factors are more critical in preventing it. Through this presentation, we’d like to emphasize on necessity of more intelligent horizon scanning.
Keywords: horizon scanning; analysts; weak signal searching; keyword collocation; intelligence; system dynamics
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