A tool for visual analytics of multidimensional data

V magistrskem delu predstavimo orodje za vizualno analitiko večdimenzionalnih podatkov. V analizi sorodnega dela predstavimo tehnike vizualizacije masovnih podatkov in tradicionalne ter napredne tehnike odkrivanja znanja. Na tej osnovi podrobneje opišemo razvito orodje in s primeri uporabe demonstriramo njegovo učinkovitost. Z rezultati pa tudi potrdimo pravilnost delovanja implementiranih funkcionalnosti.This Master\u27s thesis introduces a tool for visual analytics of multidimensional data. During the analysis of the related work, technique for big data visualization and traditional as well as advanced knowledge discovery methods are presented. On this basis, the developed tool is described details, while its efficiency is demonstrated with several use-cases. The correctness of the developed methods is proved with the results

AN ALGORITHM FOR FLOODING AREA PREDICTION USING LiDAR DATA

Z napredovanjem računalniške tehnologije se zadnje čase pojavlja čim več različnih fizikalnih simulacij, med katere spadajo tudi simulacije dinamike tekočin. Enačbe Navier-Strokes opisujejo takšno gibanje tekočin v 3D prostoru, katerih diskretizacija in reševanje na CPU porabi veliko časa. Zato bi si želeli lažjo in hitrejšo metodo za tovrstne simulacije. V diplomskem delu smo predstavili implementacijo reševalnika enačb plitve vode, ki opisujejo gibanje tekočin v 2D prostoru. Reševalnik smo uporabili za predvidevanje poplavnih območij nad 2.5D mrežo zgrajeno iz 3D oblaka točk. Ta množica točk običajno predstavlja realna površja, ki so bila posneta s tehnologijo LiDAR. Tako je v razvitem orodju mogoče predvidet ogrožene predele, ki bodo poplavljeni v realnem času.With the recent advances of computer technology there are many different physical simulations, including simulations of the fluid dynamics. Navier-Strokes equations describe such fluid motion in 3D space. Solving Navier-Strokes equations on CPU is a time consuming task. Because of that simplified method is necessary for the CPU simulation. In this diploma thesis an implementation of the solver for shallow water equations (SWE) is presented. The solver for predicting flooded areas on 2.5D grid constructed from the point cloud is used. These points usually represent real surfaces that have been scanned with LiDAR technology. Withourimplementedapplication, theareasthatwillbefloodedfromanearbywatersources can be predicted in a real-time

A Complete Environmental Intelligence System for LiDAR-Based Vegetation Management in Power-Line Corridors

This paper presents the first complete approach to achieving environmental intelligence support in the management of vegetation within electrical power transmission corridors. Contrary to the related studies that focused on the mapping of power lines, together with encroaching vegetation risk assessment, we realised predictive analytics with vegetation growth simulation. This was achieved by following the JDL/DFIG data fusion model for complementary feature extraction from Light Detection and Ranging (LiDAR) derived data products and auxiliary thematic maps that feed an ensemble regression model. The results indicate that improved vegetation growth prediction accuracy is obtained by segmenting training samples according to their contextual similarities that relate to their ecological niches. Furthermore, efficient situation assessment was then performed using a rasterised parametrically defined funnel-shaped volumetric filter. In this way, RMSE≈1 m was measured when considering tree growth simulation, while a 0.37 m error was estimated in encroaching vegetation detection, demonstrating significant improvements over the field observations

STALITA: Innovative Platform for Bank Transactions Analysis

Acts of fraud have become much more prevalent in the financial industry with the rise of technology and the continued economic growth in modern society. Fraudsters are evolving their approaches continuously to exploit the vulnerabilities of the current prevention measures in place, many of whom are targeting the financial sector. To overcome and investigate financial frauds, this paper presents STALITA, which is an innovative platform for the analysis of bank transactions. STALITA enables graph-based data analysis using a powerful Neo4j graph database and the Cypher query language. Additionally, a diversity of other supporting tools, such as support for heterogeneous data sources, force-based graph visualisation, pivot tables, and time charts, enable in-depth investigation of the available data. In the Results section, we present the usability of the platform through real-world case scenarios