[pt] A produção de indicadores sociais e sua disponibilização na Web é uma importante iniciativa de democratização e transparência que os governos em todo mundo vêm realizando nas últimas duas décadas. No Brasil diversas instituições governamentais ou ligadas ao governo publicam indicadores relevantes para acompanhamento do desempenho do governo nas áreas de saúde, educação, meio ambiente entre outras. O acesso, a consulta e a correlação destes dados demanda grande esforço, principalmente, em um cenário que envolve diferentes organizações. Assim, o desenvolvimento de ferramentas com foco na integração e disponibilização das informações de tais bases, torna-se um esforço relevante. Outro aspecto que se destaca no caso particular do Brasil é a dificuldade em se identificar dados estatísticos dentre outros tipos de dados armazenados no mesmo banco de dados. Esta dissertação propõe um arcabouço de software que cobre a identificação das bases de dados estatísticas no banco de dados de origem e o enriquecimento de seus metadados utilizando ontologias padronizadas pelo W3C, como base para o processo de triplificação. / [en] The production of social indicators and their availability on the Web is an important initiative for the democratization and transparency that governments have been doing in the last two decades. In Brazil, several government or government-linked institutions publish relevant indicators to help assess the government performance in the areas of health, education, environment and others. The access, query and correlation of these data demand substantial effort, especially in a scenario involving different organizations. Thus, the development of tools, with a focus on the integration and availability of information stored in such bases, becomes a significant effort. Another aspect that requires attention, in the case of Brazil, is the difficulty in identifying statistical databases among others type of data that share the same database. This dissertation proposes a software framework which covers the identification of statistical data in the database of origin and the enrichment of their metadata using W3C standardized ontologies, as a basis for the triplification process.

Data warehouses are being used in many applications since quite a long time. Traditionally, new data in these warehouses is loaded through offline bulk updates which implies that latest data is not always available for analysis. This, however, is not acceptable in many modern applications (such as intelligent building, smart grid etc.) that require the latest data for decision making. These modern applications necessitate real-time fast atomic integration of incoming facts in data warehouse. Moreover, the data defining the analysis dimensions, stored in dimension tables of these warehouses, also needs to be updated in real-time, in case of any change. In this thesis, such real-time data warehouses are defined as dynamic data warehouses. We propose a data model for these dynamic data warehouses and present the concept of Hierarchical Hybrid Multidimensional Data Space (HHMDS) which constitutes of both ordered and non-ordered hierarchical dimensions. The axes of the data space are non-ordered which help their dynamic evolution without any need of reordering. We define a data grouping structure, called Minimum Bounding Space (MBS), that helps efficient data partitioning of data in the space. Various operators, relations and metrics are defined which are used for the optimization of these data partitions and the analogies among classical OLAP concepts and the HHMDS are defined. We propose efficient algorithms to store summarized or detailed data, in form of MBS, in a tree structure called DyTree. Algorithms for OLAP queries over the DyTree are also detailed. The nodes of DyTree, holding MBS with associated aggregated measure values, represent materialized sections of cuboids and tree as a whole is a partially materialized and indexed data cube which is maintained using online atomic incremental updates. We propose a methodology to experimentally evaluate partial data cubing techniques and a prototype implementing this methodology is developed. The prototype lets us experimentally evaluate and simulate the structure and performance of the DyTree against other solutions. An extensive study is conducted using this prototype which shows that the DyTree is an efficient and effective partial data cubing solution for a dynamic data warehousing environment.

Duomenų kubo konstravimas yra laikui ir kompiuteriniams resursams imlus procesas. Nepaisant to, šis darbas turi būti atliktas norint pasinaudoti greitų užklausų iš ypatingai didelių OLAP kubų teikiamais privalumais . Telekomunikacijų bendrovės surenka didelius duomenų kiekius apie įvykius telekomunikaciniuose tinkluose. Kiekviena duomenų porcija aprašo daug informacijos (pavyzdžiui: paslaugos tipą, iniciatorių, gavėją, pradžios laiką, trukmę, perduotų duomenų kiekį, skambučio kryptį, kainą, tinklo sąsajos adresą ir t.t.). Mobiliojo ryšio rinkoje yra įprasta apdovanoti kiekvieną abonentą tam tikru prizu (pinigais, nuolaidomis ar nauju mobiliuoju telefonu) mainais į 24 mėnesių sutartį naudotis konkretaus operatoriaus paslaugomis. Taigi kas 24 mėnesius abonentas turi galimybę pakeisti paslaugos teikėją. Tam, kad ryšio operatorius išlaikytų savo klientus, už sutarties pratęsimą taip pat turi pasiūlyti dovaną. Kad būtų galima tai atlikti nepatiriant finansinių nuostolių – mobiliojo ryšio operatorius privalo žinoti kiekvieno abonento naudojimosi paslaugomis statistiką. Šiame dokumente aprašoma pora būtų kaip pakeisti duomenų pirminį vaizdą (struktūrą ir sudėtį) siekiant pagreitinti duomenų kubų konstravimo procesą. Vienas šių metodų – duomenų agregavimas iki didžiausio, vis dar tinkamo analizei, lygio. Kitas metodas – tai lėtai kintančių kubo dimensijų sintezavimas taip sumažinant kubo dydį ir pagreitinant jo kūrimą. / Data cube pre computing is time and computer resources consuming task. In spite of this it needs to be done in order to construct an OLAP cube to take advantage of fast querying in data sets enormous in its sizes. Telecommunication industries collect huge amount of data about events in its networks. Every data portion holds a lot of information (i.e. service type, originator, receiver, time for start, duration, data volume, calling direction, cost, network interface address, etc.). In mobile telecommunication industries it is common to award each customer / subscriber by some prize (money, cell phone, discount to services and so on) in return of 24 month obligation to use one’s services. So, every 24 months subscriber gains ability to choose another telecommunication network. In order to maintain stable amount of subscribers’ service provider must offer something in return. In order to do that correctly, without financial loses, one must know exact usage statistics of each subscriber. This paper covers couple tips to arrange data in data warehouses (data marts) in order to achieve greater data cube pre processing speed. One of these methods covers partial data aggregation to highest degree, still sufficient to answer specific queries. Another method shows the ability to synthesize data cube dimensions in order to lower data volumes, that data cube pre calculation could take less time.

This master’s thesis focuses on monitoring and profiling multidimensional database and reports. The project was created in MS SSIS to this purpose and it contains 4 ETL packages. The thesis is divided into three main sections. First section consists of theoretical background needed to accomplish the goal. In second section there is analysis of the company for which is the solution created and in the third part of the thesis there is described the proposal and it’s solution. Proposal of the solution and the solution itself was created in MS Visual Studio 2015, MS SQL Management Studio and Power BI.

This thesis focuses on the area of detection of potential insurance frauds by using Business Intelligence (BI) and its practical application to real data of compulsory and accident insurance. It describes the basic concepts of insurance business, the individual layers of BI architecture, and a detailed description of the implementation process from data transformation through the use of advanced analytical methods to the presentation of acquired information.

No description available.

The purpose of this study was to explore the usage of data cubes in the context of the Global Earth Observation System of Systems (GEOSS). This study investigated what added benefit could be provided to users of the GEOSS platform by utilizing the capabilities of data cubes. Data cubes in earth observation is a concept for how data should be handled and provided by a data server. It includes aspects such as flexible extraction of subsets and processing capabilities. In this study it was found that the most frequent use case for data cubes was time analysis. One of the main services provided by the GEOSS portal was the discovery and inspection of datasets. In the study a timeline interface was constructed to facilitate the exploration and inspection of datasets with a temporal dimension. The datasets were provided by a data cube, and made use of the data cubes capabilities in retrieving subsets of data along any arbitrary axis. A usability evaluation was conducted on the timeline interface to gain insight into the users requirements and user satisfaction. The results showed that the design worked well in many regards, ranking high in user satisfaction. On a number of points the study highlighted areas of improvement. Providing insight into important design limitations and challenges together with suggestions on how these could be approached in different ways. / Syftet med studien var att undersöka hur Data Cubes kunde komma att användas inom ramarna för Global Earth Observation System of Systems (GEOSS). Vilka fördelar som kunde dras ifrån att utnyttja den potential som data cubes besitter och använda dem i GEOSS plattformen undersöktes i studien. Data cubes för earth observation är ett koncept om hur data ska hanteras och tillhandahållas av datatjänster. Det ämnar bland annat flexibel extrahering av datapartitioner och dataprocesseringsförmågor. I denna studie iakttogs det att det mest frekvent förekommande användningsområdet för data cubes var analys av tid. Ett huvudsyfte med GEOSS portalen var att tillhandahålla användaren med verktyg för att utforska och inspektera dataset. I denna studie tillverkades ett användargränssnitt med en tidslinje för att ge användaren tillgång till att även utforska och inspektera dataset med en tidsdimension. Datasetet tillhandahålls från en data cube och utnyttjar data cubes färdighet i att förse utvalda partitioner av datasetet som kan extraheras längs valfri axel. En användarstudie har gjorts på användargränssnittet för att utvärdera till vilken grad användarna var nöjda och hur det uppfyllde deras krav, för att samla värdefulla insikter. Resultatet visar på att designen presterar väl på flera punkter, den rankar högt i användartillfredsställelse. Med studien klargör även framtida förbättringsmöjligheter och gav insikter om viktiga designbegränsningar och utmaningar. I rapporten diskuteras det hur dessa kan hanteras på olika sätt.

Data mining is the extraction of interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Association rule mining is a data mining method that seeks to discover associations among transactions encoded within a database. Data mining on spatio-temporal data takes into consideration the dynamics of spatially extended systems for which large amounts of spatial data exist, given that all real world spatial data exists in some temporal context. We need fuzzy sets in mining association rules from spatio-temporal databases since fuzzy sets handle the numerical data better by softening the sharp boundaries of data which models the uncertainty embedded in the meaning of data. In this thesis, fuzzy association rule mining is performed on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed for fuzzy spatio-temporal data cube construction. Besides the performance criteria interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined to be the metrics for the comparison of association rule mining techniques. Fuzzy association rule mining using spatio-temporal data cubes and Apriori algorithm performed within the scope of this thesis are compared using these metrics. Real meteorological data (precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed using data cube and Apriori algorithm in order to generate the fuzzy association rules.

L'analyse en ligne OLAP (Online Analytical Processing) est une des technologies les plus importantes dans les entrepôts de données, elle permet l'analyse multidimensionnelle de données. Cela correspond à un outil d'analyse puissant, tout en étant flexible en terme d'utilisation pour naviguer dans les données, plus ou moins en profondeur. OLAP a été le sujet de différentes améliorations et extensions, avec sans cesse de nouveaux problèmes en lien avec le domaine et les données, par exemple le multimedia, les données spatiales, les données séquentielles, etc. A l'origine, OLAP a été introduit pour analyser des données structurées que l'on peut qualifier de classiques. Cependant, l'émergence des réseaux d'information induit alors un nouveau domaine intéressant qu'il convient d'explorer. Extraire des connaissances à partir de larges réseaux constitue une tâche complexe et non évidente. Ainsi, l'analyse OLAP peut être une bonne alternative pour observer les données avec certains points de vue. Différents types de réseaux d'information peuvent aider les utilisateurs dans différentes activités, en fonction de différents domaines. Ici, nous focalisons notre attention sur les réseaux d'informations bibliographiques construits à partir des bases de données bibliographiques. Ces données permettent d'analyser non seulement la production scientifique, mais également les collaborations entre auteurs. Il existe différents travaux qui proposent d'avoir recours aux technologies OLAP pour les réseaux d'information, nommé ``graph OLAP". Beaucoup de techniques se basent sur ce qu'on peut appeler cube de graphes. Dans cette thèse, nous proposons une nouvelle approche de “graph OLAP” que nous appelons “Graphes enrichis par des Cubes” (GreC). Notre proposition consiste à enrichir les graphes avec des cubes plutôt que de construire des cubes de graphes. En effet, les noeuds et/ou les arêtes du réseau considéré sont décrits par des cubes de données. Cela permet des analyses intéressantes pour l'utilisateur qui peut naviguer au sein d'un graphe enrichi de cubes selon différents niveaux d'analyse, avec des opérateurs dédiés. En outre, notons quatre principaux aspects dans GreC. Premièrement, GreC considère la structure du réseau afin de permettre des opérations OLAP topologiques, et pas seulement des opérations OLAP classiques et informationnelles. Deuxièmement, GreC propose une vision globale du graphe avec des informations multidimensionnelles. Troisièmement, le problème de dimension à évolution lente est pris en charge dans le cadre de l'exploration du réseau. Quatrièmement, et dernièrement, GreC permet l'analyse de données avec une évolution du réseau parce que notre approche permet d'observer la dynamique à travers la dimension temporelle qui peut être présente dans les cubes pour la description des noeuds et/ou arêtes. Pour évaluer GreC, nous avons implémenté notre approche et mené une étude expérimentale sur des jeux de données réelles pour montrer l'intérêt de notre approche. L'approche GreC comprend différents algorithmes. Nous avons validé de manière expérimentale la pertinence de nos algorithmes et montrons leurs performances. / Online Analytical Processing (OLAP) is one of the most important technologies in data warehouse systems, which enables multidimensional analysis of data. It represents a very powerful and flexible analysis tool to manage within the data deeply by operating computation. OLAP has been the subject of improvements and extensions across the board with every new problem concerning domain and data; for instance, multimedia, spatial data, sequence data and etc. Basically, OLAP was introduced to analyze classical structured data. However, information networks are yet another interesting domain. Extracting knowledge inside large networks is a complex task and too big to be comprehensive. Therefore, OLAP analysis could be a good idea to look at a more compressed view. Many kinds of information networks can help users with various activities according to different domains. In this scenario, we further consider bibliographic networks formed on the bibliographic databases. This data allows analyzing not only the productions but also the collaborations between authors. There are research works and proposals that try to use OLAP technologies for information networks and it is called Graph OLAP. Many Graph OLAP techniques are based on a cube of graphs.In this thesis, we propose a new approach for Graph OLAP that is graphs enriched by cubes (GreC). In a different and complementary way, our proposal consists in enriching graphs with cubes. Indeed, the nodes or/and edges of the considered network are described by a cube. It allows interesting analyzes for the user who can navigate within a graph enriched by cubes according to different granularity levels, with dedicated operators. In addition, there are four main aspects in GreC. First, GreC takes into account the structure of network in order to do topological OLAP operations and not only classical or informational OLAP operations. Second, GreC has a global view of a network considered with multidimensional information. Third, the slowly changing dimension problem is taken into account in order to explore a network. Lastly, GreC allows data analysis for the evolution of a network because our approach allows observing the evolution through the time dimensions in the cubes.To evaluate GreC, we implemented our approach and performed an experimental study on a real bibliographic dataset to show the interest of our proposal. GreC approach includes different algorithms. Therefore, we also validated the relevance and the performances of our algorithms experimentally.

Diploma thesis presents an evaluation of the current state of the company system, identification of critical areas and areas suitable for improvement. Based on the theoretical knowledge and analysis results, commercial Business Intelligence software is designed to enhance the quality and efficiency of the company's decision-support system and the introduction of an advanced Quality Culture system. The thesis reveals critical locations in the corporate environment and opens up space to design improvements to the system.