Development of a spatial decision support system requires integration of various spatial data sets coming from different information systems of possibly more than one organization. The spatial decision support system development for spatial planning and environmental protection is discussed and spatial data integration is described. The heterogeneity of information systems from which spatial data come is reflected through their purpose. Utility and real estate cadaster systems imply services to citizens as part of spatial data infrastructure in the Federation of Bosnia and Herzegovina. The spatial planning information system is intended for registration of environmental changes and spatial decision making. Utility cadaster information system development is described along with important aspects on how to satisfy both functional cadastral services and spatial planning experts needs to analyze information related to land use and network supply systems. Particular attention is given to spatial data transformation for utility cadaster database development in accordance with a prescribed data model.

This paper describes an approach introducing location intelligence using open-source software components as the solution for planning and construction of the airport infrastructure. As a case study, the spatial information system of the International Airport in Sarajevo is selected. Due to the frequent construction work on new terminals and the increase of existing airport capacities, as one of the measures for more efficient management of airport infrastructures, the development team has suggested to airport management to introduce location intelligence, meaning to upgrade the existing information system with a functional WebGIS solution. This solution is based on OpenGeo architecture that includes a set of spatial data management technologies used to create an online internet map and build a location intelligence infrastructure.

Organizations can improve efficiency of process execution through a correct resource allocation, as well as increase income, improve client satisfaction, and so on. This work presents a novel approach for solving problems of resource allocation in business processes which combines process mining, statistical techniques, and metaheuristic algorithms for optimization. In order to get more reliable results of the simulation, in this paper, we use process mining analysis and statistical techniques for building a simulation model. For finding optimal human resource allocation in business processes, we use the improved differential evolution algorithm with population adaptation. Because of the use of a stochastic simulation model, noise appears in the output of the model. The differential evolution algorithm is modified in order to include uncertainty in the fitness function. In the end, validation of the model was done on three different data sets in order to demonstrate the generality of the approach, and the comparison with the standard approach from the literature was done. The results have shown that this novel approach gives solutions which are better than the existing model from literature.