A flexible architecture for the pre-processing of solar satellite image time series data - the SETL architecture

Abstract

Satellite image time series (SITS) is a challenging domain for knowledge discovery database due to their characteristics: each image has several sunspots and each sunspot is associated with sensor data composed of the radiation level and the sunspot classifications. Each image has time parameters and sunspots' coordinates, spatiotemporal data. Several challenges of SITS domain are faced during the extract, transform, and load (ETL) process. In this paper, we proposed an architecture called SITS's extract, transform, and load (SETL) that extracts the visual characteristics of each sunspot and associates it with sunspot's sensor data considering the spatiotemporal relations. SETL brings flexibility and extensibility to working with challenging domains such as SITS because it integrates textual, visual and spatiotemporal characteristics at sunspot-record level. Furthermore, we obtained acceptable performance results according to a domain expert and increased the possibility of using different data mining algorithms comparing to the art state.