For many companies today, it is highly valuable to collect and analyse data in order to support decision making and functions of various sorts. However, this kind of data-driven approach is seldomly applied to software testing and there is often a lack of verification that the testing performed is relevant to how the system under test is used. Therefore, the aim of this thesis is to investigate the possibility of introducing a data-driven approach to test automation by extracting user behaviour data and curating it to form input for testing. A prestudy was initially conducted in order to collect and assess different data sources for augmenting the testing. After suitable data sources were identified, the required data, including data about user activity in the system, was extracted. This data was then processed and three prototypes where built on top of this data. The first prototype augments the model-based testing by automatically creating models of the most common user behaviour by utilising data mining algorithms. The second prototype tests the most frequent occurring client actions. The last prototype visualises which features of the system are not covered by automated regression testing. The data extracted and analysed in this thesis facilitates the understanding of the behaviour of the users in the system under test. The three prototypes implemented with this data as their foundation can be used to assist other testing methods by visualising test coverage and executing regression tests.

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近年來影城結合商場、娛樂及餐飲，如雨後春筍般出現，建商售屋亦常以影城為吸睛廣告，對消費者而言，影城對周邊住宅價格影響是否存有關聯性係其購屋選擇關心條件之一，然而現今影城的類型大不相同，且觀察到影城大多座落於商圈內或是與百貨商場結合。因此，本研究將影城、商圈與住宅價格的相關程度做交叉分析，以初步了解其關聯程度，並應用集群分析以控制異質樣本，使得形成同質性的房屋屬性來看影城效果，再透過複迴歸模型分析，探討不同類型的影城、商圈與周邊住宅價格之影響關係。 透過複迴歸模型實證結果得知，影城對台北市周邊住宅價格是有影響的，以總價1,000萬元房屋，平均而言，正向的價差介於41萬元至310萬元之間，負向的價差則介於30萬元至271萬元之間，並且正向的價差高於負向的價差。進一步將影城依經營模式及服務方式分類進行實證，採連鎖經營模式的影城周邊房價價差約為40萬元，而提供複合式服務或僅提供單一式服務的影城，對房價的影響差別不大。該結果可提供消費者在評估購買房屋時之參考，亦可作為開發商與銷售業者在預售屋訂價策略及廣告遵循原則。 此外，由於消費型態的改變，消費者習慣將看電影結合其他休閒活動，這些由百貨公司以及電影院異業結盟的商圈更得消費者青睞，影城進駐與商圈發展之關係相輔相成，進而影響新北市周邊住宅價格，以總價1,000萬元房屋，平均來說，影城進駐商圈後能提升房價139萬元。 / These years combination-area of cinemas, malls, recreation and catering are springing up as well as being the eye-catching advertisement of building-contractors. In terms of house-buyers, the correlation between cinemas and the neighborhood housing price is one of the conditions they care. Nevertheless, with the variety of the cineplex-cinema currently and mostly locate in business districts and department stores, our study make a cross analysis between cineplex, cinema, business districts with residential price for their correlation. With cluster analysis to control the heterogeneous samples and to evaluate the price-effect of cineplex-cinemas under the homogeneous housing. Furthermore to multiple regression analyze cinema stationed-in commercial-district and their neighborhood housing price. Our study confirmed the cineplex-cinemas are influential to the Taipei City surrounding-area residential price. For the ten million house, averagely the positive-impact is between four hundred ten thousand to 3.1 million. Negative-impact is between three hundred thousand to 2.71 million. Besides, the housing price different positive effect is higher than the negative one. Further to verify type-mode cinemas: the price difference in neighborhood-area is four hundred thousand with franchise management type-mode. However, there is no price difference with complex and single-service type-mode. Housing-buyer can take the result as consideration during purchasing houses as well as real estate developers and salesman in pricing strategy and advertisement principles of pre-sale houses. Additionally, with the change of consumption patterns, consumers get use to watch movies together with other recreation. Thus commercial-districts combining with department stores and cinemas are more favored. The cinema stationed-in and commercial-district development are complemented each other, therefore to affect the neighborhood housing price in New Taipei City. For the ten million house, averagely, the positive price-different effect is one million and three hundred nighty thousand after the cinema stationed-in.

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The big data framework applied to smart meters offers an exception platform for data-driven forecasting and decision making to achieve sustainable energy efficiency. Buying-in consumer confidence through respecting occupants' energy consumption behavior and preferences towards improved participation in various energy programs is imperative but difficult to obtain. The key elements for understanding and predicting household energy consumption are activities occupants perform, appliances and the times that appliances are used, and inter-appliance dependencies. This information can be extracted from the context rich big data from smart meters, although this is challenging because: (1) it is not trivial to mine complex interdependencies between appliances from multiple concurrent data streams; (2) it is difficult to derive accurate relationships between interval based events, where multiple appliance usage persist; (3) continuous generation of the energy consumption data can trigger changes in appliance associations with time and appliances. To overcome these challenges, we propose an unsupervised progressive incremental data mining technique using frequent pattern mining (appliance-appliance associations) and cluster analysis (appliance-time associations) coupled with a Bayesian network based prediction model. The proposed technique addresses the need to analyze temporal energy consumption patterns at the appliance level, which directly reflect consumers' behaviors and provide a basis for generalizing household energy models. Extensive experiments were performed on the model with real-world datasets and strong associations were discovered. The accuracy of the proposed model for predicting multiple appliances usage outperformed support vector machine during every stage while attaining accuracy of 81.65\%, 85.90\%, 89.58\% for 25\%, 50\% and 75\% of the training dataset size respectively. Moreover, accuracy results of 81.89\%, 75.88\%, 79.23\%, 74.74\%, and 72.81\% were obtained for short-term (hours), and long-term (day, week, month, and season) energy consumption forecasts, respectively.

Nowadays quality of life is often discussed topic. In defining this term, there is considerable ambiguity and disunity, since there is no universally accepted definition, nor theoretically sophisticated model. However, despite this fact, the level of quality of life is currently one of the most discussed topic. Monitoring the quality of life by using a variety of indicators are engaged in several international organizations, one of them is the Development Programme of the United Nations. This organization annually publishes the Human Development Index, which divides the world´s countries into four groups according to their level of development: low, medium, high and very high development. The aim of this thesis is to analyze the quality of life in 125 countries by using cluster analysis, accurately the Ward's method. Quality of life in this thesis is evaluated based on 19 demographic and economic indicators, which include life expectancy, literacy rate, access to drinking water and infant mortality rate. The cluster analysis divided the country into individual clusters by their similarities. Six clusters were created by this analysis, which had been compared with the results of Human Development Index. The clusters very well reflect the division, which is commonly used in the characterization of developing and developed countries. Each of the six clusters can be very well described and characterized in terms of quality of life. It is also possible qualify those clusters as poorest developing, low developed, moderately developed, medium development, high and very high development countries. Based on the results it can be stated that this analysis is consistent with other indicators of quality of life and the resulting clusters are identical with the division of countries which is commonly used.

Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can be a disordered group of objects organized into several internally homogeneous classes or clusters. Discriminant analysis creates knowledge based on the jurisdiction of existing classes classification rule, which can be then used for classifying units with an unknown group membership. The aim of this thesis is a comparison of discriminant analysis and different methods of cluster analysis. To reflect the distances between objects within each cluster, squeared Euclidean and Mahalanobis distances are used. In total, there are 28 datasets analyzed in this thesis. In case of leaving correlated variables in the set and applying squared Euclidean distance, Ward´s method classified objects into clusters the most successfully (42,0 %). After changing metrics on the Mahalanobis distance, the most successful method has become the furthest neighbor method (37,5 %). After removing highly correlated variables and applying methods with Euclidean metric, Ward´s method was again the most successful in classification of objects (42,0%). From the result implies that cluster analysis is more precise when excluding correlated variables than when leaving them in a dataset. The average result of discriminant analysis for data with correlated variables and also without correlated variables is 88,7 %.

This dissertation thesis deals with similarity measures for nominal data in hierarchical clustering, which can cope with variables with more than two categories, and which aspire to replace the simple matching approach standardly used in this area. These similarity measures take into account additional characteristics of a dataset, such as frequency distribution of categories or number of categories of a given variable. The thesis recognizes three main aims. The first one is an examination and clustering performance evaluation of selected similarity measures for nominal data in hierarchical clustering of objects and variables. To achieve this goal, four experiments dealing both with the object and variable clustering were performed. They examine the clustering quality of the examined similarity measures for nominal data in comparison with the commonly used similarity measures using a binary transformation, and moreover, with several alternative methods for nominal data clustering. The comparison and evaluation are performed on real and generated datasets. Outputs of these experiments lead to knowledge, which similarity measures can generally be used, which ones perform well in a particular situation, and which ones are not recommended to use for an object or variable clustering. The second aim is to propose a theory-based similarity measure, evaluate its properties, and compare it with the other examined similarity measures. Based on this aim, two novel similarity measures, Variable Entropy and Variable Mutability are proposed; especially, the former one performs very well in datasets with a lower number of variables. The third aim of this thesis is to provide a convenient software implementation based on the examined similarity measures for nominal data, which covers the whole clustering process from a computation of a proximity matrix to evaluation of resulting clusters. This goal was also achieved by creating the nomclust package for the software R, which covers this issue, and which is freely available.

The main focus of this thesis is to find out what factors have the biggest influence on the price of electricity for household consumers in the European Union in the context of creating the internal electricity market in the EU. By means of the cluster analysis six EU Member States have been selected according to the following criteria: electricity consumption, electricity production, and the price of electricity. As a result of that Belgium, the Czech Republic, Estonia, Hungary, Malta and Sweden have been selected. Consequently, the regression analysis has been carried out to find out what factors influence the electricity prices in every country individually. The independent variables are mainly various sources of electricity production. It was found out those renewable resources most influent the electricity price for households in Belgium, Estonia, Hungary, and Sweden. Nevertheless, in the context of Europe 2020 strategy and the formation of the European energy market, one of the European Commission's objectives is to increase the share of renewable sources in the production of electricity in the EU Member States. Therefore, it will bring about higher prices of electricity, which goes against the Commission's effort to decrease the price of electricity for households through the liberalisation of electricity and the creation of the internal electricity market.

The main goal of this Master's Thesis is to form marketing recommendations for maternity hospitals. These marketing recommendations are in the form of marketing mixes, that are designed for individual revealed segments. The theoretical part of my Master's Thesis contains the reason why the maternity hospitals should implement marketing in the present day. Further goal of the theoretical part is to explain why I apply to the area of obstetrics the marketing of services. Further the theoretical part describes specifics of marketing of services, segmentation process, targeting and positioning. There is stated characteristics of the marketing mix in the area of services at the end of the theoretical part. The analytical part is based on written questionnaires inquiry. On the basis of results of questionnaires inquiry is carried out the process of segmentation using statistical programme IBM SPSS Statistics version 21.0. The result of segmentation process is discovering three market segments. These segments show similar characteristics. There is determined attractiveness of revealed segments in the chapter dealing with targeting. Marketing recommendations describe the value of providing services. Marketing recommendations are supplemented with social status of respondents.