At the beginning, the project describes basics and utilization of data warehousing and OLAP techniques and operations used within the data warehouses. Then follows a description of one of the commercial OLAP client - based on the features of this product the requirement analysis of the freeware OLAP cube client displayer is desribed - choosing the functionality to be implemented in the client. Using the requirement analysis the structural design of the application (including UML diagrams) is made. The best solution from compared libraries, frameworks and development environments is chosen for the design. Next chapter is about implementation and tools and frameworks used in implemetation. At the end the thesis clasifies the reached results and options for further improvement.

The thesis generally describes matters of data warehouses and knowledge discovery in databases. Then it focuses on the meteorological databases and their problems. The practical part of thesis describes design methods for data mining project, NOAA Global Surface Summary of the Day (GSOD), which is then implemented in two different ways using the Pentaho tools. Finally, an evaluation and comparison of these two approaches.

In the recent past, different multidimensional data models were introduced to model OLAP (‘Online Analytical Processing’) scenarios. Design problems arise, when the modeled OLAP scenarios become very large and the dimensionality increases, which greatly decreases the support for an efficient ad-hoc data analysis process. Therefore, we extend the classical multidimensional model by grouping functionally dependent attributes within single dimensions, yielding in real orthogonal dimensions, which are easy to create and to maintain on schema design level. During the multidimensional data analysis phase, this technique yields in nested data cubes reflecting an intuitive two-step navigation process: classification-oriented ‘drill-down’/ ‘roll-up’ and description-oriented‘split’/ ‘merge’ operators on data cubes. Thus, the proposed Nested Multidimensional Data Model provides great modeling flexibility during the schema design phase and application-oriented restrictiveness during the data analysis phase.

Materialized summary tables and cached query results are frequently used for the optimization of aggregate queries in a data warehouse. Query rewriting techniques are incorporated into database systems to use those materialized views and thus avoid accessing the possibly huge raw data. A rewriting is only possible if the query is derivable from these views. Several approaches can be found in the literature to check the derivability and find query rewritings. However, most algorithms either find rewritings only in very restricted cases or in complex cases which rarely occur in data warehouse environments. The specific application scenario of a data warehouse with its multidimensional perspective allows the consideration of much more semantic information, e.g. structural dependencies within the dimension hierarchies and different characteristics of measures. The motivation of this article is to use this information to present simple conditions for derivability in a large number of relevant cases which go beyond previous approaches.

The concept of multidimensional modeling has proven extremely successful in the area of Online Analytical Processing (OLAP) as one of many applications running on top of a data warehouse installation. Although many different modeling techniques expressed in extended multidimensional data models were proposed in the recent past, we feel that many hot issues are not properly reflected. In this paper we address ten common problems reaching from defects within dimensional structures over multidimensional structures to new analytical requirements and more.

A common optimization technique in data warehouse environments is the use of materialized aggregates. Aggregate processing becomes complex, if partitions of aggregates or queries are materialized and reused later. Most problematic are the implication problems regarding the restriction predicates. We show that in the presence of hierarchies in a multidimensional environment an efficient algorithm can be given to construct - or to derive - an aggregate from one or more overlapping materialized aggregate partitions (set-derivability).

在全球化趨勢與Internet帶動速度競爭的影響下，現今的企業經常採取將旗下部門分散佈署於各地，或者和位於不同地區的公司進行合併結盟的策略，藉以提昇其競爭力與市場反應能力。由於地理位置分散的結果，這類企業當中通常存在著許多不同的資料倉儲系統；為了充分支援管理決策的需求，這些不同的資料倉儲當中的資料必須能夠進行交換與整合，因此需要有一套開放且獨立的資料交換標準，俾能經由Internet在不同的資料倉儲間交換多維度資料。然而目前所知的跨資料倉儲之資料交換解決方案多侷限於逐列資料轉換或是以純文字檔案格式進行資料轉移的方式，這些方式除缺乏效率外亦不夠系統化。在本篇研究中，將探討多維度資料交換的議題，並發展一個以XML為基礎的多維度資料交換模式。本研究並提出一個基於學名結構的方法，以此方法發展一套單一的標準交換格式，並促成分散各地的資料倉儲間形成多對多的系統化映對模式。以本研究所發展之多維度資料模式與XML資料模式間的轉換模式為基礎，並輔以本研究所提出之多維度中介資料管理功能，可形成在網路上通用且以XML為基礎的多維度資料交換過程，並能兼顧效率與品質。本研究並開發一套雛型系統，以XML為基礎來實作多維度資料交換，藉資證明此多維度資料交換模式之可行性，並顯示經由中介資料之輔助可促使多維度資料交換過程更加系統化且更富效率。 / Motivated by the globalization trend and Internet speed competition, enterprise nowadays often divides into many departments or organizations or even merges with other companies that located in different regions to bring up the competency and reaction ability. As a result, there are a number of data warehouse systems in a geographically-distributed enterprise. To meet the distributed decision-making requirements, the data in different data warehouses is addressed to enable data exchange and integration. Therefore, an open, vendor-independent, and efficient data exchange standard to transfer data between data warehouses over the Internet is an important issue. However, current solutions for cross-warehouse data exchange employ only approaches either based on records or transferring plain-text files, which are neither adequate nor efficient. In this research, issues on multidimensional data exchange are studied and an XML-based Multidimensional Data Exchange Model is developed. In addition, a generic-construct-based approach is proposed to enable many-to-many systematic mapping between distributed data warehouses, introducing a consistent and unique standard exchange format. Based on the transformation model we develop between multidimensional data model and XML data model, and enhanced by the multidimensional metadata management function proposed in this research, a general-purpose XML-based multidimensional data exchange process over web is facilitated efficiently and improved in quality. Moreover, we develop an XML-based prototype system to exchange multidimensional data, which shows that the proposed multidimensional data exchange model is feasible, and the multidimensional data exchange process is more systematic and efficient using metadata.

[pt] Um banco de dados estatístico consiste de um conjunto de observações feitas em pontos de um espaço lógico, e, muitas vezes, são organizados como cubos de dados. A definição adequada de cubos de dados, em especial de suas dimensões, ajuda a processar as suas observações e, mais importante, ajuda a combinar observações de cubos de dados diferentes. Neste contexto, os princípios de dados interligados podem ser proveitosamente aplicados à definição de cubos de dados, oferecendo uma estratégia para fornecer a semântica das dimensões, incluindo seus valores. Este trabalho introduz uma arquitetura de mediação para auxiliar no consumo de cubos de dados, expostos como triplas RDF e armazenados em bancos de dados relacionais. Os cubos de dados são descritos em um catálogo usando vocabulários padronizados e são acessados por métodos HTTP usando os princípios de REST. Portanto, este trabalho busca tirar proveito tanto dos princípios de dados interligados quanto dos princípios de REST para descrever e consumir os cubos de dados interligados de forma simples e eficiente. / [en] A statistical data set comprises a collection of observations made at some points across a logical space and is often organized as what is called a data cube. The proper definition of the data cubes, especially of their dimensions, helps to process the observations and, more importantly, helps to combine observations from different data cubes. In this context, the Linked Data Principles can be profitably applied to the definition of data cubes, in the sense that the principles offer a strategy to provide the missing semantics of the dimensions, including their values. This work introduces a mediation architecture to help consume linked data cubes, exposed as RDF triples, but stored in relational databases. The data cubes are described in a catalogue using standardized vocabularies and are accessed by HTTP methods using REST principles. Therefore, this work aims at taking advantage of both Linked Data and REST principles in order to describe and consume linked data cubes in a simple but efficient way.

De nombreuses applications décisionnelles reposent sur des entrepôts de données. Ces entrepôts permettent le stockage de données multidimensionnelles historisées qui sont ensuite analysées grâce à des outils OLAP. Traditionnellement, les nouvelles données dans ces entrepôts sont chargées grâce à des processus d’alimentation réalisant des insertions en bloc, déclenchés périodiquement lorsque l’entrepôt est hors-ligne. Une telle stratégie implique que d’une part les données de l’entrepôt ne sont pas toujours à jour, et que d’autre part le système de décisionnel n’est pas continuellement disponible. Or cette latence n’est pas acceptable dans certaines applications modernes, tels que la surveillance de bâtiments instrumentés dits "intelligents", la gestion des risques environnementaux etc., qui exigent des données les plus récentes possible pour la prise de décision. Ces applications temps réel requièrent l’intégration rapide et atomique des nouveaux faits dans l’entrepôt de données. De plus, ce type d’applications opérant dans des environnements fortement évolutifs, les données définissant les dimensions d’analyse elles-mêmes doivent fréquemment être mises à jour. Dans cette thèse, de tels entrepôts de données sont qualifiés d’entrepôts de données dynamiques. Nous proposons un modèle de données pour ces entrepôts dynamiques et définissons un espace hiérarchique de données appelé Hierarchical Hybrid Multidimensional Data Space (HHMDS). Un HHMDS est constitué indifféremment de dimensions ordonnées et/ou non ordonnées. Les axes de l’espace de données sont non-ordonnés afin de favoriser leur évolution dynamique. Nous définissons une structure de regroupement de données, appelé Minimum Bounding Space (MBS), qui réalise le partitionnement efficace des données dans l’espace. Des opérateurs, relations et métriques sont définis pour permettre l’optimisation de ces partitions. Nous proposons des algorithmes pour stocker efficacement des données agrégées ou détaillées, sous forme de MBS, dans une structure d’arbre appelée le DyTree. Les algorithmes pour requêter le DyTree sont également fournis. Les nœuds du DyTree, contenant les MBS associés à leurs mesures agrégées, représentent des sections matérialisées de cuboïdes, et l’arbre lui-même est un hypercube partiellement matérialisé maintenu en ligne à l’aide des mises à jour incrémentielles. Nous proposons une méthodologie pour évaluer expérimentalement cette technique de matérialisation partielle ainsi qu’un prototype. Le prototype nous permet d’évaluer la structure et la performance du DyTree par rapport aux autres solutions existantes. L’étude expérimentale montre que le DyTree est une solution efficace pour la matérialisation partielle d’un cube de données dans un environnement dynamique. / 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.

Manual data exploration in data cubes and searching for potentially interesting and useful information starts to be time-consuming and ineffective from certain volume of the data. In my thesis, I designed, implemented and tested a system, automating the data cube exploration and offering potentially interesting views on OLAP data to the end user. The system is based on integration of two data analytics methods - OLAP analysis data visualisation and data mining, represented by GUHA association rules mining. Another contribution of my work is a research of possibilities how to solve differences between OLAP analysis and association rule mining. Implemented solutions of the differences include data discretization, dimensions commensurability, design of automatic data mining task algorithm based on the data structure and mapping definition between mined association rules and corresponding OLAP visualisation. The system was tested with real retail sales data and with EU structural funds data. The experiments proved that complementary usage of the association rule mining together with OLAP analysis identifies relationships in the data with higher success rate than the isolated use of both techniques.