The diploma thesis focuses on the assessment of the information system in the selected company. It analyzes the current state of the company and its equipment. Thesis includes proposals to minimize or eliminate identified deficiences and increase the level of work with information system, security and processes in company.

This diploma thesis focuses on problematics associated with design of relational database. It is divided into several parts where it deals with theoretical basis, analyses of current state and the design of own solution.

Organizations that during the past years become accustomed to run its services on its on-premise solution, have increasingly to decide if they will in these days continue to operate their IT services in this way, or take advantage of new opportunities for cloud-based solution. However, the transition to this new technology, which entrusts their data into the hands of external companies, accompanied by a fear of security of their sensitive data. The main objective of this thesis is to propose a reference model that will assist organizations in their selection process of cloud service providers with respect to data security. In conclusion the quality and usefulness of this reference model is validated by knowledge and information, which are obtained during interviews with IT specialists.

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In this thesis study, single target tracking algorithms including IMM-PDA and IMM-IPDA algorithms / Optimal approaches in multitarget tracking including IMM-JPDA, IMM-IJPDA and IMM-JIPDA algorithms and an example of Linear Multi-target approaches in multitarget tracking including IMM-LMIPDA algorithm have been studied and implemented in MATLAB for comparison. Simulations were carried out in various realistic test scenarios including single target tracking, tracking of multiple targets moving in convoy fashion, two targets merging in a junction, two targets merging-departing in junctions and multitarget tracking under isolated tracks situations. RMSE performance, track loss and computational load evaluations were done for these algorithms under the test scenarios dealing with these situations. Benchmarkings are presented relying on these outcomes.

Disputats, Handelshögskolan, Stockholm, 2003.

The main goal and contribution of this thesis are to create an artifact in the form of model that supports a recruitment of employees via social media networks and its implementation into practice. For this purpose has been used a depth analysis of current state of researched problematics leading to finding gaps that could be eventually solved while using mentioned model. The Author takes advantage of knowledge obtained from literature research and also from self-created research, particularly the analysis of content type investigated social media networks and specific data obtained manually and automatically; Comparative analysis of job advertisements; periodic questionnaire survey amongst HR staff. A significant part of the research has also been an investigation of already existing frames of social media networks recruitment. A depth analysis of current state determines that there is existence only of few models dealing with social media networks recruiting. Furthermore, these models lack candidate evaluation based on the social media networks behavior. Artifact as a model that supports a recruitment of employees via social media networks contains a suggestion of an automatic solution dealing with user´s data downloading and also a suggestion of subsequent analytical data processing and the creation of a predictive model for assessing the user´s behavior on the social media networks. Final evaluation of the effectiveness of the proposed model is done through the formal verification process and the case study. The case study verification has the suggested artifact been implemented in practice with a name Model PM that is using a recruiting application (Práce na míru) for an extraction of Facebook´s data. For user´s behavior predictor setting has been used a character test (MBTI). With the help of the cluster analysis and machine learning (Decision trees) has been created Stochastic predictive model that determines a character type of particular candidate (The accuracy of the prediction of the MBTI personality category is in the range between 68 % to 84 % in individual cases with confidence value between 43 % and 81 %). The case study verified a usefulness of model that supports a recruitment of employees via social media networks, and afterward, the mode has been implemented into the practice.

This master's degree thesis focuses on predictive analytics. This type of analysis uses historical data and predictive models to predict future phenomenon. The main goal of this thesis is to describe predictive analytics and its process from theoretical as well as practical point of view. Secondary goal is to implement project of predictive analytics in an important insurance company operating in the Czech market and to improve the current state of detection of fraudulent insurance claims. Thesis is divided into theoretical and practical part. The process of predictive analytics and selected types of predictive models are described in the theoretical part of the thesis. Practical part describes the implementation of predictive analytics in a company. First described are techniques of data organization used in datamart development. Predictive models are then implemented based on the data from the prepared datamart. Thesis includes examples and problems with their solutions. The main contribution of this thesis is the detailed description of the project implementation. The field of the predictive analytics is better understandable thanks to the level of detail. Another contribution of successfully implemented predictive analytics is the improvement of the detection of fraudulent insurance claims.

The main aim of this diploma thesis is to propose for changes in the company information system. Document content is focuse on analyse, implementation and using data warehouse and usage advance to optimaze methods for achievement added value to users. The first part of this thesis is focused on choice of suitable concept of data warehouse, implementation and possible problems. In the second part, I compare assets of this system with costs for development and administration.

A recent study using reinforcement learning with a Q-functions to quantify the impact of individual player actions in ice hockey has shown promising results. The model takes into account the context of the actions and captures internal dynamic features of the play which simple common metrics e.g., counting goals or assists, do not. It also performs look ahead which is important in a low scoring game like ice hockey. However, it does not capture the chemistry between the players i.e., how well the players play together which is important in a team sport like ice hockey. In this paper, we, therefore, extend this earlier work on individual player performance with new metrics on player pairs impact when on ice together. Our resulting top pairings are compared to NHL’s official statistics and extended analysis is performed that investigate the relationship with time on ice which provides insights that could be of relevance to coaches.