Event attributed Spatial Entity Knowledge (EASE) based Spatio-Temporal reasoning to infer geographic processes

Abstract

Remote Sensing of resource in a geographic space at regular temporal intervals has paved way for the evolution of geo-spatial information processing. Knowledge engineering of facts acquired through this technology primarily aims at qualitative results to support human in solving complex tasks that cannot be solved through quantitative relational query processing methods with Database Management Systems (DBMS). This necessitates the need for automated inference mechanism to be built over relational databases. Automated reasoning, a systematic process of formal symbolic representation to codify the acquired facts enables the system to infer new knowledge which can further update the facts. A formal representation of Event Attributed Spatial Entity (EASE) Knowledge base is proposed using the theory of Allen's Interval calculus and Randel's RCC-8. The objective of the proposed knowledge base is to formalize spatial entities in a geographic region whose temporal attributes are events occurring in an interval, at time instant and over successive intervals to qualitatively answer the event-based queries on prediction of spatial process. The significance of this formal approach is shown using query evaluation on real datasets. The working of proposed knowledge base is explained with illustrative results. Towards the end of this work, the direction for enhancement of EASE to explore its use is discussed.