BigGIS: a continuous refinement approach to master heterogeneity and uncertainty in spatio-temporal big data (vision paper)

摘要: Geographic information systems (GIS) are important for decision support based on spatial data. Due to technical and economical progress an ever increasing number of data sources available leading a rapidly growing fast unreliable amount that can be beneficial (1) in the approximation multivariate causal predictions future values as well (2) robust proactive decision-making processes. However, today's GIS not designed such big demands require new methodologies effectively model uncertainty generate meaningful knowledge. As consequence, we introduce BigGIS, predictive prescriptive spatio-temporal analytics platform, symbiotically combines analytics, semantic web technologies visual methodologies. We present novel continuous refinement show challenges intermediate result collaborative research project into analysis design enabled GIS.