Granular knowledge discovery framework for fire and rescue reporting system

Issue title: Granular Knowledge Discovery

Guest editors: Dominik Ślȩzak, Andrzej Skowron, Yukio Ohsawa and Junzo Watada

Article type: Research Article

Authors: Kreński, Karol^{a; *} | Krasuski, Adam^a | Szczuka, Marcin^b | Ƚazowy, Stanisƚaw^a

Affiliations: [a] The Main School of Fire Service, Sƚowackiego, Warsaw, Poland | [b] Institute of Mathematics, The University of Warsaw, Warsaw, Poland

Correspondence: [*] Corresponding author: Karol Kreński, The Main School of Fire Service, Sƚowackiego 52/54, 01-629 Warsaw, Poland. E-mail: [email protected]

Abstract: We present a proposition for an architecture of a platform for fire & rescue Incident Data Reporting System (IDRS). We propose to extend the current, simple functionality of such systems with knowledge that is a result of using semantic background of the analyzed phenomena. We discuss how to build and tune practically meaningful models of processes by means of granules, approximating their states and instances. We discuss how the incorporation of granular paradigm in the process of modelling and knowledge discovery from IDRS data may enrich the final outcome. of data preparation and transformation. We justify the need for proposed approach by presenting several case studies related to existing IDRS. We also discuss the complexity of available data sources that can be utilized to make the proposed approach more useful.

Keywords: Fire service, granular computing, decision support, reporting system

DOI: 10.3233/IDT-140231

Journal: Intelligent Decision Technologies, vol. 9, no. 4, pp. 379-389, 2015