The main goal of this thesis is to create a manual for planning application named Targetty appropriately so, for the academical purposes used at University of Economics, Prague and also to create a demo of this application for Vetrotech company. The goal is fulfilled through detailed analysis of the application and by describing its functionality via screenshots of the application and their description. The app demo is created on data model which was suggested for this purpose. This thesis can be divided into three main parts. The first part considers the issue of the enterprise planning by both the economical and the Business Intelligence points of view. The planning principles are demonstrated via examples and explained into detail. The second part is dedicated to analysis of the planning application called Targetty and in the last one a demo of application for Vetrotech company is created. This thesis proves that the planning application Targetty is a suitable application for small or medium companies. It also contains an application demo for Vetrotech company based on data model which may be used for creating a solution for any companies specializing in commercial industry.

This diploma thesis is focused on an implementation of a Business Intelligence (BI) in a raw materials trading company, which trades brown coal. At first, a Balanced Scorecard analysis is made in this trading company. Then results of this analysis are used for the implementation of the BI. The first chapter of this thesis describes goals, methods and structure of the thesis. The second chapter contains information about those theses, which have similar topics as my thesis. Next chapters are theoretical part of the thesis. The third chapter describes the Business Intelligence and the fourth chapter describes the Balanced Scorecard. The fifth chapter is a theoretical-practical chapter and describes the company and the brown coal market. Practical part starts on the chapter six, which describes the use of the Balanced Scorecard in the given company. Next chapter uses the outputs of the chapter six and in a chapter seven there is an implementation of the BI. The eighth chapter describes reports of the BI and the ninth chapter contains conclusion and an evaluation of achieving the goals set by this thesis. Main contribution of this thesis is in the demonstration of design and implementation of the BI in a raw materials trading company.

Le travail présenté dans cette thèse vise à proposer des approches pour construire et développer des entrepôts de données selon le modèle NoSQL orienté colonnes. L'intérêt porté aux modèles NoSQL est motivé d'une part, par l'avènement des données massives et d'autre part, par l'incapacité du modèle relationnel, habituellement utilisés pour implémenter les entrepôts de données, à permettre le passage à très grande échelle. En effet, les différentes modèles NoSQL sont devenus des standards dans le stockage et la gestion des données massives. Ils ont été conçus à l'origine pour construire des bases de données dont le modèle de stockage est le modèle « clé/valeur ». D'autres modèles sont alors apparus pour tenir compte de la variabilité des données : modèles orienté colonne, orienté document et orienté graphe. Pour développer des entrepôts de données massives, notre choix s'est porté sur le modèle NoSQL orienté colonnes car il apparaît comme étant le plus approprié aux traitements des requêtes décisionnelles qui sont définies en fonction d'un ensemble de colonnes (mesures et dimensions) issues de l'entrepôt. Cependant, le modèle NoSQL en colonnes ne propose pas d'opérateurs de type analyse en ligne (OLAP) afin d'exploiter les entrepôts de données.Nous présentons dans cette thèse des solutions innovantes sur la modélisation logique et physique des entrepôts de données NoSQL en colonnes. Nous avons proposé une approche de construction des cubes de données qui prend compte des spécificités de l'environnement du stockage orienté colonnes. Par ailleurs, afin d'exploiter les entrepôts de données en colonnes, nous avons défini des opérateurs d'agrégation permettant de créer des cubes OLAP. Nous avons proposé l'opérateur C-CUBE (Columnar-Cube) permettant de construire des cubes OLAP stockés en colonnes dans un environnement relationnel en utilisant la jointure invisible. MC-CUBE (MapReduce Columnar-Cube) pour construire des cubes OLAP stockés en colonnes dans un environnement distribué exploitant la jointure invisible et le paradigme MapReduce pour paralléliser les traitements. Et enfin, nous avons développé l'opérateur CN-CUBE (Columnar-NoSQL Cube) qui tient compte des faits et des dimensions qui sont groupés dans une même table lors de la génération de cubes à partir d'un entrepôt dénormalisé selon un certain modèle logique. Nous avons réalisé une étude de performance des modèles de données dimensionnels NoSQL et de nos opérateurs OLAP. Nous avons donc proposé un index de jointure en étoile adapté aux entrepôts de données NoSQL orientés colonnes, baptisé C-SJI (Columnar-Star Join Index). Pour évaluer nos propositions, nous avons défini un modèle de coût pour mesurer l'impact de l'apport de cet index. D'autre part, nous avons proposé un modèle logique baptisé FLM (Flat Logical Model) pour implémenter des entrepôts de données NoSQL orientés colonnes et de permettre une meilleure prise en charge par les SGBD NoSQL de cette famille.Pour valider nos différentes contributions, nous avons développé une plate-forme logicielle CG-CDW (Cube Generation for Columnar Data Warehouses) qui permet de générer des cubes OLAP à partir d'entrepôts de données en colonnes. Pour terminer et afin d'évaluer nos contributions, nous avons tout d'abord développé un banc d'essai décisionnel NoSQL en colonnes (CNSSB : Columnar NoSQL Star Schema Benchmark) basé sur le banc d'essai SSB (Star Schema Benchmark), puis, nous avons procédé à plusieurs tests qui ont permis de montrer l'efficacité des différents opérateurs d'agrégation que nous avons proposé. / The work presented in this thesis aims at proposing approaches to build data warehouses by using the columnar NoSQL model. The use of NoSQL models is motivated by the advent of big data and the inability of the relational model, usually used to implement data warehousing, to allow data scalability. Indeed, the NoSQL models are suitable for storing and managing massive data. They are designed to build databases whose storage model is the "key/value". Other models, then, appeared to account for the variability of the data: column oriented, document oriented and graph oriented. We have used the column NoSQL oriented model for building massive data warehouses because it is more suitable for decisional queries that are defined by a set of columns (measures and dimensions) from warehouse. However, the NoSQL model columns do not offer online analysis operators (OLAP) for exploiting the data warehouse.We present in this thesis new solutions for logical and physical modeling of columnar NoSQL data warehouses. We have proposed a new approach that allows building data cubes by taking the characteristics of the columnar environment into account. Thus, we have defined new cube operators which allow building columnar cubes. C-CUBE (Columnar-CUBE) for columnar relational data warehouses. MC-CUBE (MapReduce Columnar-CUBE) for columnar NoSQL data warehouses when measures and dimensions are stored in different tables. Finally, CN-CUBE (Columnar NoSQL-CUBE) when measures and dimensions are gathered in the same table according a new logical model that we proposed. We have studied the NoSQL dimensional data model performance and our OLAP operators, and we have proposed a new star join index C-SJI (Columnar-Star join index) suitable for columnar NoSQL data warehouses which store measures and dimensions separately. To evaluate our contribution, we have defined a cost model to measure the impact of the use of this index. Furthermore, we have proposed a logic model called FLM (Flat Logical Model) to represent a data cube NoSQL oriented columns and enable a better management by columnar NoSQL DBMS.To validate our contributions, we have developed a software framework CG-CDW (Cube Generation for Data Warehouses Columnar) to generate OLAP cubes from columnar data warehouses. Also, we have developed a columnar NoSQL decisional benchmark CNSSB (Columnar NoSQL Star Schema Benchmark) based on the SSB and finally, we conducted several tests that have shown the effectiveness of different aggregation operators that we proposed.

This Diploma thesis deals with the analysis and proposal of the pilot BI solution for the company Svoboda & Partner CZ s.r.o., engaged in the automechanic expertise. Fundamental objectives of this work focus particularly on the above mentioned pilot BI solutions and partial objectives deal with an analysis of the company and its needs, arising from business strategy and market analysis applications available to support the visualization of transaction reporting. These objectives have been achieved by analyzing the company's business model in chapter seven, further analysis of needs and problems within the company in chapter eight. Before the analysis, the diploma thesis deals with a survey of available applications that offer functionality of dynamically generated graphs to aid visualization of transaction reporting in chapter six and finally a major analysis and preparation of pilot BI and transaction reporting for this business which you can find from chapter 9 to chapter eleven. Contribution of this work for the company will be a complete analysis and delivery of all necessary documents in order to decide whether the above BI solutions to the company implement or not. In the case of a positive decision of the company, it is expected that thanks to BI solutions the ways of looking at corporate data can be more expanded, more precise and more operational management of the company, achieving better performance, higher profits and more efficient decision-making based on better data for decision making.

This thesis describes OLAP and data mining technologies and methods of their communication with users by using dynamic tables. Key theoretical and technical information is also included. Next part focuses on particular implementation of dynamic tables used in Vema portal solution. Last parts come close to analysis and implementation of the metadata editor which enables the metadata to be effectively designed.

This master's thesis, divided into two sections, compares in part one existing (non-)commercial systems for OLAP presentation using contingency table or graph. The main focus is put on a graph. Results received from my observations in part one are used for implementing a graphic subsystem within internet browser's environment. User friendly interface and good arrangement of displayed data are the most important tasks.