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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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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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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.
|
|
|
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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