Multi-agent system approach for improved real-time visual summaries of geographical data streams

Abstract

Interdisciplinary efforts are needed to support visual exploration and analysis of spatio-temporal data streams from sensor networks, depending on the size and complexity of data that must be analysed before being displayed. For this, several emerging approaches have been proposed known as 'Visual Summaries' of datasets that will help users find what is most important and interesting to visualise in the mass of available information. In this paper, we present an approach for generating automatically the visual summaries in real time. For this, we have adopted chorem-based visual representations of territories issued from both geometric and semantic generalisation. Our approach relies on a multi-agent framework; an extraction knowledge agent is able to extract important spatiotemporal patterns of data streams coming from a sensor network as interesting regions, and a visualisation agent which displays those patterns as simplified maps. We validate this model with an example taken from the meteorology field.