Personalizovana vizuelizacija geo-informacija iz integrisanih izvora informacija zasnovana na semantici i web tehnologijama

The research subject of this PhD thesis is personalized vizuelization of geo-information originating from integrated geo-information sources, performed within Web-based Geographic Information Systems (Web GIS). The research presented in this PhD thesis includes the definition, design and implementation of Web GIS system architecture that enables personalized visualization of geospatial information based on contextual information. The presented architecture relies on the usage of GeoNis framework for interoperability of GIS applications. GeoNis platform uses a hybrid ontology approach for information integration purposes. By taking advantage of hybrid ontology approach, GeoNis platform provides an infrastructure that enables acquiring geospatial information from a large number of GIS systems, whereas GIS systems implement their interface components in the form of Web services developed according to geospatial information dissemination standards. The presented architecture enables efficient usage of GIS system’s interface components to provide customers with a personalized view over the integrated geo-information available within any of the GIS system integrated within GeoNis platform. The presented architecture of Web Geo-Information System for personalized visualization of geospatial information uses a textual description of user preferences as a baseline for selection of geospatial content from integrated geo-information sources for individual users. A description of user preferences is used to discover geo-information sources within GeoNis platform, whereby user preferences description becomes the basis for the development of user context, in terms of selected information and maps. To discover geo-information sources, described architecture takes advantage of semantic description of integrated geo-information sources, e.g. integrated GIS systems (application). As a semantic description of the integrated geospatial information sources, this process is capable of utilizing domain GeoNis ontology and local ontologies of integrated GIS systems. The model used for storing user’s contextual information within the presented Web GIS system is defined according to the OGC Web Map Context Document specification. The development of a Web GIS system according to the proposed architecture included the development of specification and implementation of a Web service that enables creating, storing and acquiring contextual documents developed for a particular user. Also, this PhD thesis included an implementation of a mechanism that allows the prediction of geospatial context of new Web GIS system user, in terms of the selection of geospatial information and maps for individual Web GIS system user. This mechanism is based on the use of metadata that had to be previously developed for each Web GI Service in the presented Web GIS system architecture. Due to the importance of a symbology used to visualize information in a GIS, an implementation of a Web GIS system for personalized visualization of integrated geospatial information included a development of a specification and implementation of the repository that enables creating, storing and acquiring symbology used to visualize geospatial information

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Resolving semantic conflicts through ontological layering

We examine the problem of semantic interoperability in modern software systems, which exhibit pervasiveness, a range of heterogeneities and in particular, semantic heterogeneity of data models which are built upon ubiquitous data repositories. We investigate whether we can build ontologies upon heterogeneous data repositories in order to resolve semantic conflicts in them, and achieve their semantic interoperability. We propose a layered software architecture, which accommodates in its core, ontological layering, resulting in a Generic ontology for Context aware, Interoperable and Data sharing (Go-CID) software applications. The software architecture supports retrievals from various data repositories and resolves semantic conflicts which arise from heterogeneities inherent in them. It allows extendibility of heterogeneous data repositories through ontological layering, whilst preserving the autonomy of their individual elements. Our specific ontological layering for interoperable data repositories is based on clearly defined reasoning mechanisms in order to perform ontology mappings. The reasoning mechanisms depend on the user‟s involvments in retrievals of and types of semantic conflicts, which we have to resolve after identifying semantically related data. Ontologies are described in terms of ontological concepts and their semantic roles that make the types of semantic conflicts explicit. We contextualise semantically related data through our own categorisation of semantic conflicts and their degrees of similarities. Our software architecture has been tested through a case study of retrievals of semantically related data across repositories in pervasive healthcare and deployed with Semantic Web technology. The extensions to the research results include the applicability of our ontological layering and reasoning mechanisms in various problem domains and in environments where we need to (i) establish if and when we have overlapping “semantics”, and (ii) infer/assert a correct set of “semantics” which can support any decision making in such domains