Spatiotemporal Data Warehousing for Event Tracking Applications

Abstract

In this paper, we propose a multidimensional spatiotemporal modeling framework of data warehouse creation for tracing dynamic events in contemporary applications, like crowd contact tracing for Covid-19 prevention. Such a framework offers a natural and consistent solution for slowly changing dimension management. It provides a progressive evolution from traditional static data management to modern dynamic data analysis with spatiotemporal tracking capabilities for IoT applications. Based on such a framework, en-tity-centered resource integration and related business intelligence applications can be rig-orously developed, managed and properly tracked. © 2022 Institute of Information Science. All rights reserved.