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Knowledge Discovery in Spatio-Temporal Data
Pešek, Martin ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with knowledge discovery in spatio-temporal data, which is currently a rapidly evolving area of research in information technology. First, it describes the general principles of knowledge discovery, then, after a brief introduction to mining in the temporal and spatial data, it focuses on the overview and description of existing methods for mining in spatio-temporal data. It focuses, in particular, on moving objects data in the form of trajectories with an emphasis on the methods for trajectory outlier detection. The next part of the thesis deals with the process of implementation of the trajectory outlier detection algorithm called TOP-EYE. In order to testing, validation and possibility of using this algorithm is designed and implemented an application for trajectory outlier detection. The algorithm is experimentally evaluated on two different data sets.
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Knowledge Discovery in Databases of Moving Objects
Chovanec, Vladimír ; Mlích, Jozef (referee) ; Chmelař, Petr (advisor)
The aim of this master's thesis is to get familiar with problems of data mining and classification. This thesis also continues with application SUNAR, which is upgraded in practical part with SVM classification of persons passing between cameras. In the conclusion, we discuss ways to improve classification and person recognition in application SUNAR.
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Data Mining in Data Stream
Sýkora, Petr ; Chmelař, Petr (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with the data mining in data stream which represents fast developing area of information technology. The text describes common principles of data mining, explains what data stream is and shows methods for its preprocessing and algorithms for following data mining. The special attention is given to the VFDT and the CVDT algorithm. The next mentioned are the spatiotemporal data and related data mining. The second part describes the design and implementation of the application for classification over spatiotemporal data stream represented by road traffic data and following prediction of spatiotemporal events (traffic-jams). The classification is performed by the VFDT and CVFDT algorithm. The application has been tested on the data set obtained by the simulation tool SUMO.
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Querying Spatio-Temporal Data of Moving Objects
Dvořáček, Ondřej ; Kolář, Dušan (referee) ; Zendulka, Jaroslav (advisor)
This master's thesis is devoted to the studies of possibilities, which can be used for representation of moving objects data and for querying such spatio-temporal data. It also shows results of the master's thesis created by Ing. Jaroslav Vališ, that should be used for the solution of this master's thesis. But based on the theoretical grounds defined at the beginning of this work was designed and implemented new database extension for saving and querying spatio-temporal data. Special usage of this extension is demonstrated in an example application. This application uses the database extension for the implementation of its own database functions that are domain specific. At the conclusion, there are presented ways of the farther development of this database extension and the results of this master's thesis are there set into the context of the following project, doctoral thesis "Moving objects database".
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Moving Objects Indexing
Vetešník, Jiří ; Chmelař, Petr (referee) ; Zendulka, Jaroslav (advisor)
This work is aimed for proposing acceptable indexing of moving objects. With the enlargement of mobile computing it is needed to manage large sets of spatiotemporal data. We introduce the problem of spatiotemporal data and basic general approaches of indexing these data. Further, we show support of spatial data in Oracle. The movement is typically represented as trajectory in two dimensional space with temporal component in third dimension. The thesis contains experiments performed in database Oracle on artificially generate data.
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Knowledge Discovery in Object Relational Databases
Chytka, Karel ; Vrážel, Dušan (referee) ; Chmelař, Petr (advisor)
The goal of this master's thesis is to acquaint with a problem of a knowledge discovery and objectrelational data classification. It summarizes problems which are connected with mining spatiotemporal data. There is described data mining kernel algorithm SVM. The second part solves classification method implementation. This method solves data mining in a Caretaker trajectory database. This thesis contains application's implementation for spatio-temporal data preprocessing, their organization in database and presentation too.
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