Publications / 2019 Proceedings of the 36th ISARC, Banff, Alberta, Canada

Chatbot System for Data Management: A Case Study of Disaster-related Data

James Yichu Chen, Meng-Han Tsai, Cheng-Hsuan Yang, Hao-Yung Chan and Shih-Chung Kang
Pages 306-309 (2019 Proceedings of the 36th ISARC, Banff, Alberta, Canada)
Abstract:

This research aims to explore the effectiveness of chatbot system for highly-complex data management. With the growing popularity of mobile devices, the conversational information technology (IT) service, such as chatbot, has permeated into our daily life. Conversation-based systems are now widely utilized for helping the user to access the data they need or control other devices more intuitively. Although lots of systems have been developed for personal assisting, such as Siri, Alexa, and so on, or customer services, seldom of them are used for data management. Therefore, this research will focus on exploring the application of chatbot system for the management of complex data. We used the disaster-related data and a chatbot system developed in the previous work as a case study. A one-year field test was conducted for figuring out the feasibility and effectiveness of chatbot system for data management. After the field test, we found that the number of users of the developed system was doubled within six months. The usage of the system increased approximately 2.5 times with six months. We also found that the users now rely on the chatbot system for their daily tasks. The results reveal that the chatbot system may be a promising direction for highly-complex data management due to its intuitive data accessing process.

Keywords: Data Management; Conversational System; Chatbot