The main purpose of panel studies is to analyze changes in values of studied variables over time. In micro panel research, a large number of elements are periodically observed within the relatively short time period of just a few years. Moreover, the number of repeated measurements is small. This dissertation deals with contemporary approaches to the regression and the clustering analysis of micro panel data. One of the approaches to the micro panel analysis is to use multivariate statistical models originally designed for crosssectional data and modify them in order to take into account the within-subject correlation. The thesis summarizes available tools for the regression analysis of micro panel data. The known and currently used linear mixed effects models for a normally distributed dependent variable are recapitulated. Besides that, new approaches for analysis of a response variable with other than normal distribution are presented. These approaches include the generalized marginal linear model, the generalized linear mixed effects model and the Bayesian modelling approach. In addition to describing the aforementioned models, the paper also includes a brief overview of their implementation in the R software. The difficulty with the regression models adjusted for micro panel data is the ambiguity of their parameters estimation. This thesis proposes a way to improve the estimations through the cluster analysis. For this reason, the thesis also contains a description of methods of the cluster analysis of micro panel data. Because supply of the methods is limited, the main goal of this paper is to devise its own two-step approach for clustering micro panel data. In the first step, the panel data are transformed into a static form using a set of proposed characteristics of dynamics. These characteristics represent different features of time course of the observed variables. In the second step, the elements are clustered by conventional spatial clustering techniques (agglomerative clustering and the C-means partitioning). The clustering is based on a dissimilarity matrix of the values of clustering variables calculated in the first step. Another goal of this paper is to find out whether the suggested procedure leads to an improvement in quality of the regression models for this type of data. By means of a simulation study, the procedure drafted herein is compared to the procedure applied in the kml package of the R software, as well as to the clustering characteristics proposed by Urso (2004). The simulation study demonstrated better results of the proposed combination of clustering variables as compared to the other combinations currently used. A corresponding script written in the R-language represents another benefit of this paper. It is available on the attached CD and it can be used for analyses of readers own micro panel data.

The objective of this master thesis is to design and develop a real-time storage system for geographic data from driving school trips. The system provides tools for analysis and evaluation of practice trips. This system is an extension of the DoAutoskoly.cz project which is described in the text. The next part contains an introduction to geographical data, spatial data and available databases with spatial extensions. The understanding to spatial databases is very important for the system design, an explanation of a solution for a database layer and implementation of major parts. Solution for a graphical view of the results and possible extensions of the system are described in the last part of this thesis.

Tématem této disertační práce je klasifikace daty s nevyváženými daty. Jedná se o oblast strojového, jejímž cílem je řešit problémy, které plynou z toho, že jedna ze tříd je v datech zastoupena výrazně méně než třída druhá. Minoritní třída má často větší význam a tradiční metody upřednostňující majoritní třídu nedosahují dobrých výsledků na třídě minoritní. Dvě aplikační domény motivovaly výzkum a vedly na identifikaci dvou specifických, dosud neřešených problémů.  V první z nich vedlo omezení kladené na minimální požadovanou přesnost na minoritní třídě v počítačové bezpečnosti na formulaci úlohy klasifikace s omezením. Navrhl jsem metodu, která kombinuje upravenou verzi logistické regrese a stochastické algoritmy, které vždy vylepšily výsledky logistické regrese.Druhou je doména analýzy učení (Learning Analytics), která motivovala definici problému predikce splnění cíle, jenž má specifikovaný termín splnění. Byl představen koncept sebe-učení (Self-Learning), kdy trénování modelu probíhá díky jedincům, kteří tento cíl splní předčasně. Díky malému počtu jedinců splňujících úlohu na začátku je problém silně nevyvážený, ale nevyváženost klesá směrem k termínu splnění. Na problému identifikace rizikových studentů distanční univerzity bylo ukázáno, že (1) takový koncept dává lepší výsledky než specifikovaná základna (baseline), (2) a že metody pro vypořádání se s nevyvážeností, které neberou v potaz informaci o doméně, nevedly k velkým zlepšením. Evaluace ukázala, že metody založené na znalosti domény v rozšířené verzi pro Self-Learning vylepšily klasifikaci více než běžné metody pro vypořádání se s nevyvážeností a že znalost příčiny nevyváženosti může vést k lepším výsledkům.

This thesis is focused on problem data reconciliation of measurements. The objective of this thesis was reconciled measured value from electric drum dryer to suit exactly to the mathematical model of drying. For solution was used nonlinear data reconciliation with constrained nonlinear optimization. The entire calculation is processed in programme MATLAB and outputs are graphs of reconciled values of measurement on dryer such as inlet and outlet temperature and humidity, differential pressure of exhaust moisture air, weight of laundry, atmospheric pressure and electric supply. Achieved solution can by characterized by an amount of evaporated water. Weight of wet and dry laundry are 27,7 kg a 17,7 kg. The calculated amount of evaporated water from measurements was almost 18,8 kg. With reconciled measurements it was 9,7 kg. Goals of the thesis were found more realistic values.

The object of master‘s thesis is design and creation of e-commerce system database migration tool from the ZenCart platform to the PrestaShop platform. Both system databases will be described and analysed and based on gained information the migration tool will be created according customers‘ requirements and then final data migration from original to the new database will be executed.

Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunately, these algorithms are not suitable for data stream processing. In frequent-pattern mining from data streams, it is important to manage sets of items and also their history. There are several reasons for this; it is not just the history of frequent items, but also the history of potentially frequent sets that can become frequent later. This requires more memory and computational power. This thesis describes two algorithms: Lossy Counting and FP-stream. An effective implementation of these algorithms in C# is an integral part of this thesis. In addition, the two algorithms have been compared.

This master's thesis deals with data mining techniques and classification of the data into specified categories. The goal of this thesis is to implement a web portal for administration and classification of data from distributed sources. To achieve the goal, it is necessary to test different methods and find the most appropriate one for web articles classification. From the results obtained, there will be developed an automated application for downloading and classification of data from different sources, which would ultimately be able to substitute a user, who would process all the tasks manually.

The subject of this master's thesis is extension of existing data mining system. System will be extended by the module for the time series data mining. This thesis consists of common introduction to data mining issues and continues with time series analysis. Thesis then also contains some of the current tasks and algorithms used in time series data mining, follows by the concept of the implementation and description of the choosen mining method. Possible future system's improvments are disscused at the end of the paper.

Decades of research in quantitative genetics have found substantial heritability for personality traits as well as for mental disorders which formed the basis of the ongoing molecular genetic studies that aim to identify genetic variations that actually contribute to the manifestation of complex traits. With regard to psychological traits, genetic variation impacting neurotransmitter function have been of particular interest. Additionally, the role of environmental factors including gene × environment interactions has been further investigated and the impor-tance of developmental aspects has been stressed. Furthermore, endophenotypes which link complex traits with their respective biological underpinnings and thus bridge the gap between gene and behaviour have begun to be included in research efforts. In accordance with this approach, this thesis aims to further examine the influence of genetic variation impacting serotonergic and dopaminergic functioning on endophenotypes of anxiety-related behaviour. To this end, two well established paradigms – the acoustic startle reflex and the cortisol stress response – were employed. Both show considerable interindividual variation which has been found in quantitative genetic studies to be at least partly based on genetic factors. In addition, the neural circuits underlying these endophenotypes are relatively well understood and thus reveal references for the detection of associated genetic influences. The results of this thesis associate the overall startle magnitude in two independent samples of young adults with a polymorphism in the promoter region of the serotonin transporter (5-HTT) gene (5-HTTLPR): Carriers of the short (S) allele which results in a reduced gene ex-pression showed a stronger startle magnitude which is in line with numerous findings linking the S allele to increased measures of negative emotionality. In addition to 5-HTTLPR, the effects of past stressful life events on the startle response were investigated: Participants who had recently experienced at least one stressful life event exhibited stronger startle responses and reduced habituation of the startle reflex although there was no 5-HTTLPR × environment inter-action effect. A third study revealed independent and joint effects of 5-HTTLPR and a poly-morphism in the dopamine receptor 4 gene (DRD4) in the same sample of young adults with regard to the cortisol stress response with carriers of the DRD4 7R allele which has been associ-ated with higher scores in sensation seeking, showing reduced cortisol responses. In addition, a 5-HTTLPR × DRD4 interaction effect emerged: 5-HTTLPR long (L) allele carriers showed the lowest cortisol response but only when they possessed at least one copy of the DRD4 7R allele. Moreover, in a fourth study a life span approach was taken and the influence of a further important serotonergic polymorphism which impacts the functioning of tryptophan hydroxylase 2 (TPH2), the rate limiting enzyme in the biosynthesis of serotonin, on interindividual differences in the startle response was investigated in three different age samples: children, young adults and older adults. There was a sex × TPH2 genotype interaction effect in a sample of young adults on the overall startle response while there was no effect of TPH2 in children or older adults. The last study of this thesis presents findings regarding the influence of two dopaminergic polymorphisms in genes encoding the enzyme catechol-O-methyltransferase (COMT) and the dopamine transporter (DAT), respectively, which both terminate dopamine signalling and are thus important regulators of dopaminergic neurotransmission, on the startle reflex in older adults. COMT met/met homozygotes showed the strongest and val/val homozygotes displayed the smallest startle magnitude which is in line with findings linking the COMT met allele to increased scores of anxiety related traits and disorders. Regarding DAT, participants homozygous for the 10R allele, which had previously associated with attention-deficit hyperactivity disorder, showed a stronger overall startle response. In sum, this thesis comprises data on interindividual differences in an electrophysiological and a hormonal endophenotype across the life span and their association with serotonergic and dopaminergic function based on genetic variation. One major finding is the clear evidence for the influence of serotonergic polymorphisms on the startle response in young adults while in contrast in older adults genetic variation in the dopaminergic system exerted considerable influence. These differences might be due to developmental processes in the different stages of life although cohort effects and effects of different recruitment strategies can also not be ruled out. Furthermore, there were significant differences regarding the genetic influence on the acoustic startle reflex and cortisol stress response in one and the same sample which might be due to methodological differences of the two paradigms as well as differences in their underlying neuronal circuits. In conclusion, this thesis supports the acoustic startle reflex and the cortisol stress response as valuable endophenotypes and thus indicators for underlying neurobiological circuits although some methodological issues remain. It also highlights the importance of taking developmental factors and changes over the course of life into account. Finally, this thesis emphasizes the necessity to include reliably and validly assessed past experienced events in molecular genetic studies in order to understand the interplay between genetic and environmental factors in shaping (endo)-phenotypes.

Databases increase by new data continually. A process called Knowledge Discovery in Databases has been defined for analyzing these data and new complex systems has been developed for its support. Developing of one of this systems is described in this thesis. Main goal is to analyse the actual state of implementation of this system which is based on the Java NetBeans Platform and the Oracle database system and to extend it by data preprocessing algorithms and the source data analysis. Implementation of data preprocessing components and changes in kernel of this system are described in detail in this thesis.