A Novel Platform for Efficient Utilization and Integration of Time Series Data for Various Applications
Jaewon Moon, Seungwoo Kum, Seungtaek Oh · 2023
In the industry, various time-series data are being generated, but it is challenging to handle them consistently due to the diverse purposes of data collection and the distinct characteristics of each dataset. Time-series data should be able to be combined and utilized for different purposes beyond their initial storage objectives. However, the development of platforms that search for and utilize a large number of time-series data lags behind other data formats. In this paper, we propose a time-series data management system that stores and manages time-series data for different purposes, facilitates the discovery of relevant time-series data, and enables their combination and processing based on specific objectives. The proposed management platform will assist in effectively finding and utilizing various incomplete and problematic real-time series data. Furthermore, future research is planned to be conducted, leveraging the prepared time-series data in the platform to test various learning and analysis techniques and uncover their underlying meanings.