Dissertations / Theses on the topic 'Data warehousing'

Over the last decade, we have witnessed the emergence of networks in a wide spectrum of application domains, ranging from social and information networks to biological and transportation networks. Graphs provide a solid theoretical foundation for modeling complex networks, and revealing valuable insights from both the network structure and the data embedded within its entities. As the business and social environments are getting increasingly complex and interconnected, graphs became a widespread abstraction at the core of the information infrastructure supporting those environments. Modern information systems consist of a large number of sophisticated and interacting business entities that naturally form graphs. In particular, integrating graphs into data warehouse systems received a lot of interest from both academia and industry. Indeed, data warehouses are the central enterprise's information repository, and are critical for proper decision support and future planning. Graph warehousing is emerging as the field that extends current information systems with graph management and analytics capabilities. Many approaches were proposed to address the graph data warehousing challenge. These efforts laid the foundation for multidimensional modeling and analysis of graphs. However, most of the proposed approaches partially tackle the graph warehousing problem by being restricted to simple abstractions such as homogeneous graphs or ignoring important topics such as multidimensional integrity constraints and dimension hierarchies. In this dissertation, we conduct a systematic study of the graph data warehousing topic, and address the key challenges of database and multidimensional modeling of graphs. We first propose GRAD, a new graph database model specifically tuned for warehousing and OLAP analytics. GRAD aims to provide analysts with a set of simple, well-defined, and adaptable conceptual components to support rich semantics and perform complex analysis on graphs. Then, we define the multidimensional concepts for heterogeneous attributed graphs and highlight the new types of measures that could be derived. We project this multidimensional model on property graphs and explore how to extract the candidate multidimensional concepts and build graph cubes. Then, we extend the multidimensional model by integrating GRAD and show how graph modeling based on GRAD facilitates multidimensional modeling, and enables supporting dimension hierarchies and building new types of OLAP cubes on graphs. Afterwards, we present TopoGraph, a graph data warehousing framework that extends current graph warehousing models with new types of cubes and queries combining graph-oriented and OLAP querying. TopoGraph goes beyond traditional OLAP cubes, which process value-based grouping of tables, by considering in addition the topological properties of the graph elements. And it goes beyond current graph warehousing models by proposing new types of graph cubes. These cubes embed a rich repertoire of measures that could be represented with numerical values, with entire graphs, or as a combination of them. Finally, we propose an architecture of the graph data warehouse and describe its main building blocks and the remaining gaps. The various components of the graph warehousing framework can be effectively leveraged as a foundation for designing and building industry-grade graph data warehouses. We believe that our research in this thesis brings us a step closer towards a better understanding of graph warehousing. Yet, the models and framework we proposed are the tip of the iceberg. The marriage of graph and warehousing technologies will bring many exciting research opportunities, which we briefly discuss at the end of the thesis.
Durant l’última dècada, hem estat testimonis de l’aparició de xarxes en un ampli espectre de dominis d’aplicació, que van de les xarxes socials i d’informació a xarxes biològiques i de transport. Els grafs proporcionen un fonament teòric sòlid per a modelar xarxes complexes i revelen informació valuosa tant de l'estructura de la xarxa com de les dades integrades a les seves entitats. A mesura que els entorns empresarials i socials són cada cop més complexos i interconnectats, els grafs es van convertir en una abstracció generalitzada en el nucli de la infraestructura d'informació que dona suport a aquests entorns. Els sistemes d'informació moderns consisteixen en un gran nombre d'entitats empresarials i la seva interacció, que formen grafs de forma natural. En particular, la integració de grafs en sistemes de magatzem de dades va rebre molt d’interès tant de l’àmbit acadèmic com de la indústria. De fet, els magatzems de dades són el repositori central d'informació de l'empresa i són fonamentals per a un suport adequat a la presa de decisions i una planificació futura. Els magatzems de dades en graf (graph data warehousing) és un camp emergent que estén els sistemes d’informació tradicionals amb capacitats d’administració i d’anàlisi de dades en format grafs. Fins ara, s'han proposat molts enfocaments per afrontar el repte de l'emmagatzematge de dades en graf. Aquests esforços van posar els fonaments pel modelatge i l'anàlisi de grafs d'una perspectiva multidimensional. Tanmateix, la majoria dels plantejaments proposats aborden parcialment el problema de l'emmagatzematge de grafs restringint-se a abstraccions simples com ara grafs homogenis o ignorant temes importants com ara restriccions d’integritat multidimensionals i jerarquies de dimensió. En aquesta tesi realitzem un estudi sistemàtic del tema d'emmagatzematge de dades en graf i tractem els reptes clau de la base de dades i el modelatge multidimensional de grafs. Primer proposem GRAD, un nou model de base de dades de grafs específicament ajustat per a emmagatzematge i analítica OLAP. GRAD pretén proporcionar als analistes un conjunt de components conceptuals simples, ben definits i adaptables per donar suport a elements semàntics complexos i realitzar anàlisis complexos sobre grafs. A continuació, definim els conceptes multidimensionals per a grafs heterogenis amb atributs i ressaltem els nous tipus de mesures que es poden derivar. Projectem aquest model multidimensional en property graphs i explorem com extreure conceptes multidimensionals candidats i construir cubs de grafs. A continuació, ampliem el model multidimensional integrant GRAD i mostrem com el modelatge de grafs basat en GRAD facilita el modelatge multidimensional i permet suportar jerarquies de dimensions i crear nous tipus de cubs OLAP en grafs. Després, presentem TopoGraph, un marc d’emmagatzematge de dades en graf que amplia els models d’emmagatzematge de grafs actuals amb nous tipus de cubs i consultes que combinen la consulta orientada a grafs i OLAP. TopoGraph va més enllà dels cubs tradicionals OLAP, que processen l'agrupació de taules basada en el valor, considerant a més les propietats topològiques dels grafs. I va més enllà dels models d’emmagatzematge en graf actuals proposant nous tipus de cubs de grafs. Aquests cubs incorporen un ric repertori de mesures que es podrien representar amb valors numèrics, amb grafs sencers o com a combinació d’aquests. Finalment, proposem una arquitectura per al magatzem de dades en graf i descrivim els blocs de construcció principals i els buits restants. Els diversos components del marc d'emmagatzematge de grafs es poden aprofitar eficaçment com a base per dissenyar i construir magatzems de dades de grafs a nivell industrial. Creiem que la nostra recerca en aquesta tesi ens apropa un pas més cap a una millor comprensió de graph warehousing.

Understanding and managing change is a strategic objective for many organisations to successfully compete in a market place; as a result, organisations are leveraging their data asset and implementing data warehouses to gain business intelligence necessary to improve their businesses. Data warehouses are expensive initiatives, one-half to two-thirds of most data warehousing efforts end in failure. In the absence of well-formalised design methodology in the industry and in the context of the debate on data architecture in data warehousing, this thesis examines why multidimensional and relational data models define the data architecture landscape in the industry. The study develops a number of propositions from the literature and empirical data to understand the factors impacting the choice of logical data model in data warehousing. Using a comparative case study method as the mean of collecting empirical data from the case organisations, the research proposes a conceptual model for logical data model adoption. The model provides a framework that guides decision making for adopting a logical data model for a data warehouse. The research conceptual model identifies the characteristics of business requirements and decision pathways for multidimensional and relational data warehouses. The conceptual model adds value by identifying the business requirements which a multidimensional and relational logical data model is empirically applicable.

Dissertation (Ph.D.)--Worcester Polytechnic Institute.
Keywords: View Matching; View Maintenance; Materialized View; Data Warehouse; Information Integration. Includes bibliographical references. (p.206-215)

Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, September 2003.
Thesis advisor(s): Thomas J. Housel, Glenn R. Cook. Includes bibliographical references (p. 81-82). Also available online.

Data Warehouses are increasing their data volume at an accelerated rate; high disk space consumption; slow query response time and complex database administration are common problems in these environments. The lack of a proper data model and an adequate architecture specifically targeted towards these environments are the root causes of these problems. Inefficient management of stored data includes duplicate values at column level and poor management of data sparsity which derives from a low data density, and affects the final size of Data Warehouses. It has been demonstrated that the Relational Model and Relational technology are not the best techniques for managing duplicates and data sparsity. The novelty of this research is to compare some data models considering their data density and their data sparsity management to optimise Data Warehouse environments. The Binary-Relational, the Associative/Triple Store and the Transrelational models have been investigated and based on the research results a novel Alternative Data Warehouse Reference architectural configuration has been defined. For the Transrelational model, no database implementation existed. Therefore it was necessary to develop an instantiation of it’s storage mechanism, and as far as could be determined this is the first public domain instantiation available of the storage mechanism for the Transrelational model.

Thesis (M.S. in Information Technology Management) Naval Postgraduate School, Sept. 1997.
Thesis advisors, Daniel R. Dolk, George W. Thomas, and Kathryn Kocher. Includes bibliographical references (p. 203-206). Also available online.

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Nos dias atuais, uma das ferramentas mais utilizadas de Business Intelligence (BI) para o suporte à decisão da alta gerência de grandes companhias é o Data Warehouse (DW). O DW é um banco de dados que armazena seus dados de uma forma especial para que se otimizem as consultas orientadas ao negócio, além dos dados terem como características a não volatilidade, serem históricos e integrados. O ambiente em que o DW está inserido é o Data Warehousing que contempla não só o DW mais outros componentes que o ajudam a desempenhar a sua atividade fim. O aumento da quantidade de usuários utilizando esse ambiente, o crescimento exponencial do tamanho do DW, além da necessidade de otimizar as consultas e atender localmente os interesses da diretoria dos departamentos ou filiais específicas, fez com que pesquisadores da área de banco de dados buscassem soluções para obter a distribuição dos dados e consultas de uma forma transparente e segura em um ambiente de data warehousing. Atualmente, existem vários trabalhos correlatos nessa linha de pesquisa, porém nenhum demonstra na prática o resultado efetivo de uma arquitetura que contemple essas vantagens. Esse trabalho toma como base a arquitetura do sistema WebD²W (Web Distributed Data Warehousing) proposta por Cristina Ciferri para efetivar essa distribuição. Assim, foram desenvolvidos o componente de distribuição, utilizando o conceito de grafos de derivação para o desenvolvimento de algoritmos de fragmentação horizontal e mista, e o componente de consulta do ambiente distribuído, estendendo o servidor OLAP Mondrian para atender às necessidades impostas por essa nova arquitetura. Finalmente, um DW de uma rede de locadoras de DVD foi gerado para ser utilizado como estudo de caso para mostrar a aplicabilidade e eficiência desses componentes

Multidimensional analysis, trends analysis, summaries and drill-downs as data warehousing methods of choice provided rich, valuable and detailed perspective of cancer threats in terms of virtually any dimension covered by data. These allowed to model the risk of cancer including age, race, sex and survival chances among others, to spot most dangerous and incident cancers, revealed how little survival chances and treatment efficiency increased over last 30 years and how little early diagnosis was improved, presented trends and changes in them and changes in cancer risk related to place of residence and emphasized the importance of risk mitigation by screening and healthy lifestyle. These methods also turned out to be easy, requiring less computer science related knowledge as one could expect. With little support from IT staff, oncology domain professionals can easily benefit from vast data sets and analytical power applied to it. Data mining algorithms evaluated over melanoma of the skin data managed to extract what's already known in the domain. Therefore, when used by oncology professionals over less generic data one can expect data mining to have the potential of extending experts' knowledge. Neural networks, decision trees and clusters showed higher prediction accuracy than Naive Bayes classifiers and association rules but it is advised to merge results from many algorithms. Findings by particular algorithms are often disjoint and when combined, allow to reveal more despite varying predictive performance. Analysis of caCORE system and systemic integration experiment proved that building a large-scale oncological data system integrating distributed data is extremely complex. Integrating with it requires a lot of effort to understand its structures, prepare data mappings and implement integration procedures. Strict cooperation of IT and oncology professionals is mandatory. Suggestions were made to simplify the generic caCORE data model (ontology) or split it into smaller parts and expose as much integration functionality as web interfaces or encapsulated classes to decrease the complexity of the process. Tweaked like that, caCORE would be fully feasible and could be considered as the future of application of data warehousing and data mining techniques in oncology, providing distributed and common-model compliant dataset and leveraging the power of research community.
The thesis evaluates: application of data warehousing and mining analysis to SEERStat surveillance and epidemiology oncological database and aspects of future development of integrated and extensible data systems for oncology domain basing on integration experiment with caCORE project. In the thesis following is presented: results of the analysis of cancer diseases data with conclusions and advice, potential of this specific analytical application and conclusions as well as guidelines about how future, more powerful oncological analytical systems could be built.
dominiksm@o2.pl

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals.

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.
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Este trabalho descreve os conceitos básicos do ambiente do Data Warehouse, abordando em especial o processo de migração de dados. São expostas algumas técnicas e tecnologias mais recentes existentes no mercado com esta finalidade. A partir de um estudo inicial sobre os conceitos de Data Warehouse, delimitou-se o trabalho em função do processo de migração dos dados. Com este propósito, foram estudadas quatro abordagens e elaborada uma análise comparativa na tentativa de determinar qual delas é a mais adequada ao processo. Em um processo de migração de dados é importante garantir também a qualidade dos dados, em decorrência disto, o trabalho contém a descrição de uma abordagem que trata de como é realizado o processo para a qualidade de dados em Data Warehouse. São citadas também algumas ferramentas existentes no mercado que possam possivelmente atender aos processos de migração de dados para o Data Warehouse e qualidade de dados.

In most research, appropriate collections of data play a significant role in aiding decision-making processes. This is more critical if the data is being accessed across organisational barriers. Further, for the data to be mined and analysed efficiently, to aid decision-making processes, it must be harnessed in a suitably-structured fashion. There is, for example, a need to perform diverse data analyses and interpretation of structured (non-personal) HIV/AIDS patient-data from various quarters in South Africa. Although this data does exist, to some extent, it is autonomously owned and stored in disparate data storages, and not readily available to all interested parties. In order to put this data to meaningful use, it is imperative to integrate and store this data in a manner in which it can be better utilized by all those involved in the ontological field. This implies integration of (and hence, interoperability), and appropriate accessibility to, the information systems of the autonomous organizations providing data and data-processing. This is a typical problem-scenario for a Virtual Inter-Organisational Information System (VIOIS), proposed in this study. The VIOIS envisaged is a hypothetical, secure, Wireless Grid-enabled Data Warehouse (WGDW) that enables IOIS interaction, such as the storage and processing of HIV/AIDS patient-data to be utilized for HIV/AIDS-specific research. The proposed WDGW offers a methodical approach for arriving at such a collaborative (HIV/AIDS research) integrated system. The proposed WDGW is virtual community that consists mainly of data-providers, service-providers and information-consumers. The WGDW-basis resulted from systematic literaturesurvey that covered a variety of technologies and standards that support datastorage, data-management, computation and connectivity between virtual community members in Grid computing contexts. A Grid computing paradigm is proposed for data-storage, data management and computation in the WGDW. Informational or analytical processing will be enabled through data warehousing while connectivity will be attained wirelessly (for addressing the paucity of connectivity infrastructure in rural parts of developing countries, like South Africa).

Resumo: Data warehouse é um repositorio de dados coletados de fontes de dados distribuídas, autônomas e heterogêneas. A tecnologia data warehousing tem sido utilizada em Sistemas de Suporte à Decisão (DSS - Decision Support Systems) para auxiliar nos processos decisorios e identificar tendências de mercado. O data warehouse armazena uma ou mais visões materializadas dos ciados das fontes. A qualidade do processo de tomada de decisão em um DSS depende da correta propagação das atualizações ocorridas nas fontes de dados para as visões materializadas no data warehouse. Disso depende a manutenção da consistência dos dados que é em geral irai processo complexo. Nos últimos anos, algoritmos de manutenção incrementai de visões materializadas em data warehouse têm se destacado como uma importante abordagem para o problema. Um estudo comparativo desses algoritmos foi realizado e como conseqüência desse estudo um novo algoritmo, denominado SVM {Algorithm for Scheduling Warehouse View Maintenance), é aqui proposto. Esse algoritmo combina os aspectos positivos dos algoritmos estudados. Sua principal vantagem é definir intervalos de tempo para propagar as atualizações das fontes no data warehouse. Os principais aspectos de implementação do SVM são discutidos e um estudo de caso, composto de diferentes situações que mostram seu funcionamento, é apresentado.

Over the last decade, we have witnessed the emergence of networks in a wide spectrum of application domains, ranging from social and information networks to biological and transportation networks.Graphs provide a solid theoretical foundation for modeling complex networks and revealing valuable insights from both the network structure and the data embedded within its entities.As the business and social environments are getting increasingly complex and interconnected, graphs became a widespread abstraction at the core of the information infrastructure supporting those environments. Modern information systems consist of a large number of sophisticated and interacting business entities that naturally form graphs. In particular, integrating graphs into data warehouse systems received a lot of interest from both academia and industry. Indeed, data warehouses are the central enterprise's information repository and are critical for proper decision support and future planning. Graph warehousing is emerging as the field that extends current information systems with graph management and analytics capabilities. Many approaches were proposed to address the graph data warehousing challenge. These efforts laid the foundation for multidimensional modeling and analysis of graphs. However, most of the proposed approaches partially tackle the graph warehousing problem by being restricted to simple abstractions such as homogeneous graphs or ignoring important topics such as multidimensional integrity constraints and dimension hierarchies.In this dissertation, we conduct a systematic study of the graph data warehousing topic and address the key challenges of database and multidimensional modeling of graphs.We first propose GRAD, a new graph database model tailored for graph warehousing and OLAP analytics. GRAD aims to provide analysts with a set of simple, well-defined, and adaptable conceptual components to support rich semantics and perform complex analysis on graphs.Then, we define the multidimensional concepts for heterogeneous attributed graphs and highlight the new types of measures that could be derived. We project this multidimensional model on property graphs and explore how to extract the candidate multidimensional concepts and build graph cubes. Then, we extend the multidimensional model by integrating GRAD and show how GRAD facilitates multidimensional graph modeling, and enables supporting dimension hierarchies and building new types of OLAP cubes on graphs.Afterward, we present TopoGraph, a graph data warehousing framework that extends current graph warehousing models with new types of cubes and queries combining graph-oriented and OLAP querying. TopoGraph goes beyond traditional OLAP cubes, which process value-based grouping of tables, by considering also the topological properties of the graph elements. And it goes beyond current graph warehousing models by proposing new types of graph cubes. These cubes embed a rich repertoire of measures that could be represented with numerical values, with entire graphs, or as a combination of them.Finally, we propose an architecture of the graph data warehouse and describe its main building blocks and the remaining gaps. The various components of the graph warehousing framework can be effectively leveraged as a foundation for designing and building industry-grade graph data warehouses.We believe that our research in this thesis brings us a step closer towards a better understanding of graph warehousing. Yet, the models and framework we proposed are the tip of the iceberg. The marriage of graph and warehousing technologies will bring many exciting research opportunities, which we briefly discuss at the end of the thesis.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished

Data warehousing (DW) in the last decade has become the technology of choice for building data management infrastructures to provide organizations the decision-making capabilities needed to effectively carry out its activities. Despite its phenomenal growth and importance to organizations the rate of DW implementation success has been less than stellar. Many DW implementation projects fail due to technical or organizational reasons. There has been limited research on organizational factors and their role in DW implementations. It is important to understand the role and impact of both technical but organizational factors in DW implementations and their relative importance to implementation performance. A research model was developed to test the significance of technical and organizational factors in the three phases of implementation with DW implementation performance. The independent variables were technical (data, technology, and expertise) and organizational (management, goals, users, organization). The dependent variable was performance (content, accuracy, format, ease of use, and timeliness). The data collection method was a Web based survey of DW implementers and DW users sampled (26) from a population of 108 identified DW implementations. Regression was used as the multivariate statistical technique to analyze the data. The results show that organization factors are significantly related to performance. Also, that some variables in the post-implementation phase have a significant relationship with performance. Based on the results of the tests the model was revised to reflect the relative impact of technical and organizational factors on DW performance. Results suggest that in some cases organizational factors have a significant relationship with DW implementation performance. The implications and interpretation of these results provide researchers and practitioners' insights and a new perspective in the area of DW implementations.

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Este trabalho objetiva o desenvolvimento de Bibliotecas Digitais de Learning Objects (LO-DLs), usando técnicas de Data Warehousing (DWing) e Data Mining (DMing). Através da abordagem de Data Warehousing pode-se correlacionar os passos principais desta técnica, que são Extração, Transformação, Carga e OLAP, com os principais serviços de Bibliotecas Tradicionais, que são Aquisição, Classificação por Assunto, Catalogação e Consulta/Análise, de forma que eles sejam processados automaticamente. Técnicas de Data Mining são incorporadas a alguns desses processos automatizando o desenvolvimento da biblioteca. Além de integrar múltiplas fontes de LOs, que estão armazenadas em diferentes SGBDs (Sistemas de Gerência de Banco de Dados) e catalogadas através de diferentes padrões de metadados, esta abordagem contribui para prover o usuário de uma maneira mais sofisticada de consulta ao acervo, mais abrangente que as usuais opções por título, autor e assunto, já que OLAP propicia acesso multidiimensional. Além disso, também contribui para melhorar a qualidade da biblioteca, uma vez que as técnicas OLAP e de Data Mining são usadas para analisar os LOs e os acessos aos mesmos. Uma atualização automática da biblioteca acontece quando há mudança no perfil do usuário.
This work aims at the development of Learning Objects Digital Libraries (LO-DLs), using Data Warehousing (DWing) and Data Mining (DMining) techniques. By using the Data Warehousing approach, we will be able to correlate the main steps of this technique, which area Extraction, Transformation, Loading and OLAP, with the main services of a Traditional Library which are Acquisition, Subject Classification, Cataloging, and Searching, so that they will work in an automatic way. Data Mining techniques are incorporated in some of these processes automating the process of the development of the library. Besides integrating multiple LOs sources, which are stored in diverse DBMSs (Data Base Management Systems) and catalogued in different metadata languages, this approach contributes to providing the user with a sophisticated query to the library that is more comprehensive than the usual author, subject or title options, since OLAP allows multidimensional access. Furthermore it also contributes to the improvement of the library, since OLAP and data mining techniques are used to analyze LOs data and the access to them. An automatic refresh of the library is made when users´ profile changes.

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
Includes bibliographical references (leaves 76-77).
by Yao Ma.
S.B.and M.Eng.

This licentiate thesis presents the use of participatory approach to developing a data warehouse for data mining in health care. Uganda is one of the countries that faced the largest brunt of the HIV/AIDS epidemic at its inception in the early 1980s with reports of close to a million deaths. Government and nongovernmental interventions over the years saw massive reductions in HIV prevalence rates over the years. This reduction in HIV prevalence rates led to great praises by the international community and a call for other countries to model Uganda’s approach to battling the epidemic. In the last decade the reduction in HIV prevalence rates have stagnated and in some cases increased. This has lead to a call for reexamination of the HIV/AIDS fight with an emphasis on collective efforts of all approaches. One of these collective efforts is the introduction of antiretroviral therapy (ART) for those already infected with the virus. Antiretroviral therapy has numerous challenges in Uganda not least of which is the cost of the therapy especially on a developing country with limited resources. It is estimated that of the close to 1 million infected in Uganda only 300,000 are on antiretroviral therapy (UNAIDS, 2009). Additional challenges of the therapy includes following through a treatment regimen that is prescribed. Given the costs of the therapy and the limited number of people able to access the therapy it is imperative that this effort be as effective as possible. This research hinges on using data mining techniques with monitoring HIV patient’s therapy, most specifically their adherence to ART medication. This is crucial given that failure to adhere to therapy means treatment failure, virus mutation and huge losses in terms of costs incurred in administering the therapy to the patients. A system was developed to monitor patient adherence to therapy, by using a participatory approach of gathering system specification and testing to ensure acceptance of the system by the stakeholders. Due to the cost implications of over the shelf software the development of the system was implemented using open source software with limited license costs. These can be implemented in resource constrained settings in Uganda and elsewhere to assist in monitoring patients in HIV therapy. A algorithm that is used to analyze the patient data warehouses for information on and quickly assists therapists in identifying potential risks such as non-adherence and treatment failure. Open source dimensional modeling tools power architect and DB designer were used to model the data warehouse using open source MYSQL database. The thesis is organized in three parts with the first part presenting the background information, the problem, justification, objectives of the research and a justification for the use of participatory methodology. The second part presents the papers, on which this research is based and the final part contains the summary discussions, conclusions and areas for future research. The research is sponsored by SIDA under the collaboration between Makerere University and Blekinge Institute of Technology (BTH) in Sweden.

Current database management systems use Graphic Processing Units (GPUs) as dedicated accelerators to process each individual query, which results in underutilization of GPU. When a single query data warehousing workload was run on an open source GPU query engine, the utilization of main GPU resources was found to be less than 25%. The low utilization then leads to low system throughput. To resolve this problem, this paper suggests a way to transfer all of the desired data into the global memory of GPU and keep it until all queries are executed as one batch. The PCIe transfer time from CPU to GPU is minimized, which results in better performance in less time of overall query processing. The execution time was improved by up to 40% when running multiple queries, compared to dedicated processing.

Questo lavoro di tesi vuole definire un framework per un data warehouse di traiettorie, ovvero un data warehouse capace di contenere informazioni aggregate relative a traiettorie di oggetti, e che offra operazioni OLAP visuali per la loro analisi. Il modello include sia una dimensione temporale che una spaziale, che ne assicurano flessibilità, rendendolo in grado di gestire sia oggetti che si muovono liberamente o seguendo dei vincoli. Queste dimensioni, e le gerarchie associate, riflettono la struttura degli oggetti stessi e dell’ambiente in cui si muovono. Il framework proposto include anche una interfaccia visuale che permette di navigare in modo semplice tra le misure aggregate attraverso query OLAP a differenti granularità. Per evidenziare l’utilità del sistema proposto, proporremo due casi di studio, che si differenziano per il tipo di oggetti studiati, le informazioni disponibili su di loro e al tipo di vincoli sui loro movimenti.
This thesis is aimed at designing a formal framework for modelling a Trajectory Data Warehouse, namely a data warehouse able to store aggregate information related to trajectories of moving objects, which also offers visual OLAP operations for data analysis. The TDW model includes both a temporal and a spatial dimensions, that ensure flexibility, making our model general enough to deal with objects that are either completely free or constrained in their movements. This dimensions and associated hierarchies reflect the structure of the objects and of the environment in which they travel. Our framework also includes a visual interface for easily navigating aggregate measures obtained from OLAP queries at different granularities. To highlight the usefulness of the framework, we propose two different case studies, differing for the type of the observed moving objects, the available information about the objects, and their movement constraints.

In ambito aziendale è importantissima la capacità di esaminare grandi quantità di dati, in continuo aumento, generati da diversi processi aziendali. Questo viene considerato il target principale della business intelligence (BI). Al giorno d’oggi ogni azienda è dotata di diversi sistemi operazionali, utilizzati per gestire, standardizzare ed automatizzare il flusso delle informazioni prodotte durante l’esecuzione delle attività. Ciascuno di questi sistemi ha il proprio database per memorizzare le informazioni sul dominio, mantenendo così ogni contesto separato. Da qui la necessità di integrare dati provenienti da sistemi diversi, per consentire agli imprenditori di effettuare analisi sulle integrazioni e quindi prendere decisioni in base ai risultati ottenuti. Poiché i sistemi sono costituiti da database creati utilizzando diverse tecnologie, che non si integrano tra loro, sono necessarie operazioni e trasformazioni sui dati per ottenere l'integrazione per costruire il sistema. L’obiettivo fondamentale di questa tesi è proprio quello di descrivere come possiamo integrare dei dati e come adattare un sistema data warehouse all’interno dello stack tecnologico per la progettazione e realizzazione di un sistema di BI. Il progetto tratta un caso di studio per un processo aziendale di grandi dimensioni.

The use of electronic services results in the generation of vast amounts of Online Resource Usage (ORU) data. ORU data typically consists of user login, printing and executed process information. The structure of this type of data restricts the ability of decision makers to effectively and efficiently analyse ORU data. A data warehouse (DW) structure is required which satisfies an organisation’s information requirements. In order to design a DW structure a methodology is needed to provide a design template according to acknowledged practices. The aim of this research was to primarily propose a methodology specifically for the design of a DW structure to support the efficient and effective analysis of ORU data. A variety of relevant DW structure design methodologies were investigated and a number of limitations were identified. These methodologies do not provide methodological support for metadata documentation, physical design and implementation. The most comprehensive methodology identified in the investigation was modified and the Adapted Triple-Driven DW Structure Design Methodology (ATDM) was proposed. The ATDM was successfully applied to the information and communication technology services (ICTS) department of the Nelson Mandela Metropolitan University as the case study for this research. The proposed ATDM consists of different phases which include a requirements analysis phase that was adapted from the identified comprehensive methodology. A physical design and an implementation phase were included in the ATDM. The ATDM was successfully applied to the ICTS case study as a proof of concept. The application of the ATDM to ICTS resulted in the generation and documentation of semantic and technical metadata which describes the DW structure derived from the application of the ATDM at a logical and physical level respectively. The implementation phase was applied using the Microsoft SQL Server integrated tool to obtain an implemented DW structure for ICTS that is described by technical metadata at an implementation level. This research has shown that the ATDM can be successfully applied to obtain an effective and efficient DW structure for analysing ORU data. The ATDM provides guidelines to develop a DW structure for ORU data and future research includes the generalisation of the ATDM to accommodate various domains and different data types.

Thesis (M.S.)--University of Florida, 2000.
Title from first page of PDF file. Document formatted into pages; contains viii, 65 p.; also contains graphics. Vita. Includes bibliographical references (p. 62-64).

Many maintenance policies have been proposed for refreshing a warehouse. The difficulties of selecting an appropriate maintenance policy for a specific scenario with specific source characteristics, user requirements etc. has triggered researcher to develop algorithms and cost-models for predicting cost associated with a policy and a scenario. In this dissertation, we develop a benchmarking tool for testing scenarios and retrieve real world data that can be compared against algorithms and cost-models. The approach was to support a broad set of configurations, including the support of source characteristics proposed in [ENG00], to be able to test a diversity set of scenarios.

Dissertation (Ph.D.)--University of Toledo, 2005.
Typescript. "Submitted as partial fulfillment of the requirements for the Doctor of Philosophy degree in Manufacturing Management and Engineering. " "A dissertation entitled"--at head of title. Title from title page of PDF document. Bibliography: p. 113-118.

Tese de doutoramento do Programa de Doutoramento em Ciências e Tecnologias da Informação, apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra
Data Warehouses (DWs) store sensitive data that encloses many business secrets. They have become the most common data source used by analytical tools for producing business intelligence and supporting decision making in most enterprises. This makes them an extremely appealing target for both inside and outside attackers. Given these facts, securing them against data damage and information leakage is critical. This thesis proposes a security framework for integrating data confidentiality solutions and intrusion detection in DWs. Deployed as a middle tier between end user interfaces and the database server, the framework describes how the different solutions should interact with the remaining tiers. To the best of our knowledge, this framework is the first to integrate confidentiality solutions such as data masking and encryption together with intrusion detection in a unique blueprint, providing a broad scope data security architecture. Packaged database encryption solutions are been well-accepted as the best form for protecting data confidentiality while keeping high database performance. However, this thesis demonstrates that they heavily increase storage space and introduce extremely large response time overhead, among other drawbacks. Although their usefulness in their security purpose itself is indisputable, the thesis discusses the issues concerning their feasibility and efficiency in data warehousing environments. This way, solutions specifically tailored for DWs (i.e., that account for the particular characteristics of the data and workloads are capable of delivering better tradeoffs between security and performance than those proposed by standard algorithms and previous research. This thesis proposes a reversible data masking function and a novel encryption algorithm that provide diverse levels of significant security strength while adding small response time and storage space overhead. Both techniques take numerical input and produce numerical output, using data type preservation to minimize storage space overhead, and simply use arithmetical operators mixed with eXclusive OR and modulus operators in their data transformations. The operations used in these data transformations are native to standard SQL, which enables both solutions to use transparent SQL rewriting to mask or encrypt data. Transparently rewriting SQL allows discarding data roundtrips between the database and the encryption/decryption mechanisms, thus avoiding I/O and network bandwidth bottlenecks. Using operations and operators native to standard SQL also enables their full portability to any type of DataBase Management System (DBMS) and/or DW. Experimental evaluation demonstrates the proposed techniques outperform standard and state-of-the-art research algorithms while providing substantial security strength. From an intrusion detection view, most Database Intrusion Detection Systems (DIDS) rely on command-syntax analysis to compute data access patterns and dependencies for building user profiles that represent what they consider as typical user activity. However, the considerable ad hoc nature of DW user workloads makes it extremely difficult to distinguish between normal and abnormal user behavior, generating huge amounts of alerts that mostly turn out to be false alarms. Most DIDS also lack assessing the damage intrusions might cause, while many allow various intrusions to pass undetected or only inspect user actions a posteriori to their execution, which jeopardizes intrusion damage containment. This thesis proposes a DIDS specifically tailored for DWs, integrating a real-time intrusion detector and response manager at the SQL command level that acts transparently as an extension of the database server. User profiles and intrusion detection processes rely on analyzing several distinct aspects of typical DW workloads: the user command, processed data and results from processing the command. An SQL-like rule set extends data access control and statistical models are built for each feature to obtain individual user profiles, using statistical tests for intrusion detection. A self-calibration formula computes the contribution of each feature in the overall intrusion detection process. A risk exposure method is used for alert management, which is proven more efficient in damage containment than using alert correlation techniques to deal with the generation of high amounts of alerts. Experiments demonstrate the overall efficiency of the proposed DIDS.
As Data Warehouses (DWs) armazenam dados sensíveis que muitas vezes encerram os segredos do negócio. São actualmente a forma mais utilizada por parte de ferramentas analíticas para produzir inteligência de negócio e proporcionar apoio à tomada de decisão em muitas empresas. Isto torna as DWs um alvo extremamente apetecível por parte de atacantes internos e externos à própria empresa. Devido a estes factos, assegurar que o seu conteúdo é devidamente protegido contra danos que possam ser causados nos dados, ou o roubo e utilização ou divulgação desses dados, é de uma importância crítica. Nesta tese, é apresentada uma framework de segurança que possibilita a integração conjunta das soluções de confidencialidade de dados e detecção de intrusões em DWs. Esta integração conjunta de soluções é definida na framework como uma camada intermédia entre os interfaces dos utilizadores e o servidor de base de dados, descrevendo como as diferentes soluções interagem com os restantes pares. Consideramos esta framework como a primeira do género que combina tipos distintos de soluções de confidencialidade, como mascaragem e encriptação de dados com detecção de intrusões, numa única arquitectura integrada, promovendo uma solução de segurança de dados transversal e de grande abrangência. A utilização de pacotes de soluções de encriptação incluídos em servidores de bases de dados tem sido considerada como a melhor forma de proteger a confidencialidade de dados sensíveis e conseguir ao mesmo tempo manter um nível elevado de desempenho nas bases de dados. Contudo, esta tese demonstra que a utilização de encriptação resulta tipicamente num aumento extremamente considerável do espaço de armazenamento de dados e no tempo de processamento e resposta dos comandos SQL, entre outras desvantagens ou aspectos negativos relativos ao seu desempenho. Apesar da sua utilidade indiscutível no cumprimento dos pressupostos em termos de segurança propriamente ditos, nesta tese discutimos os problemas inerentes que dizem respeito à sua aplicabilidade, eficiência e viabilidade em ambientes de data warehousing. Argumentamos que soluções especificamente concebidas para DWs, que tenham em conta as características particulares dos seus dados e as actividades típicas dos seus utilizadores, são capazes de produzir um melhor equilíbrio entre segurança e desempenho do que as soluções previamente disponibilizadas por algoritmos standard e outros trabalhos de investigação para bases de dados na sua generalidade. Nesta tese, propomos uma função reversível de mascaragem de dados e um novo algoritmo de encriptação, que providenciam diversos níveis de segurança consideráveis, ao mesmo tempo que adicionam pequenos aumentos de espaço de armazenamento e tempo de processamento. Ambas as técnicas recebem dados numéricos de entrada e produzem dados numéricos de saída, usam preservação do tipo de dados para minimizar o aumento do espaço de armazenamento, e simplesmente utilizam combinações de operadores aritméticos conjuntamente com OU exclusivos (XOR) e restos de divisão (MOD) nas operações de transformação de dados. Como este tipo de operações se conseguem realizar recorrendo a comandos nativos de SQL, isto permite a ambas as soluções utilizar de forma transparente a reescrita de comandos SQL para mascarar e encriptar dados. Este manuseamento transparente de comandos SQL permite requerer a execução desses mesmos comandos ao Sistema de Gestão de Base de Dados (SGBD) sem que os dados tenham de ser transportados entre a base de dados e os mecanismos de mascaragem/desmascaragem e encriptação/ decriptação, evitando assim o congestionamento em termos de I/O e rede. A utilização de operações e operadores nativos ao SQL também permite a sua portabilidade para qualquer tipo de SGBD e/ou DW. As avaliações experimentais demonstram que as técnicas propostas obtêm um desempenho significativamente superior ao obtido por algoritmos standard e outros propostos pelo estado da arte da investigação nestes domínios, enquanto providenciam um nível de segurança considerável. Numa perspectiva de detecção de intrusões, a maioria dos Sistemas de Detecção de Intrusões em Bases de Dados (SDIBD) utilizam formas de análise de sintaxe de comandos para determinar padrões de acesso e dependências que determinam os perfis que consideram representativos da actividade típica dos utilizadores. Contudo, a carga considerável de natureza ad hoc existente em muitas acções por parte dos utilizadores de DWs gera frequentemente um número avassalador de alertas que, na sua maioria, se revelam falsos alarmes. Muitos SDIBD também não fazem qualquer tipo de avaliação aos potenciais danos que as intrusões podem causar, enquanto muitos outros permitem que várias intrusões passem indetectadas ou apenas inspeccionam as acções dos utilizadores após essas acções terem completado a sua execução, o que coloca em causa a possível contenção e/ou reparação de danos causados. Nesta tese, propomos um SDIBD especificamente concebido para DWs, integrando um detector de intrusões em tempo real, com capacidade de parar ou impedir a execução da acção do utilizador, e que funciona de forma transparente como uma extensão do SGBD. Os perfis dos utilizadores e os processos de detecção de intrusões recorrem à análise de diversos aspectos distintos característicos da actividade típica de utilizadores de DWs: o comando SQL emitido, os dados processados, e os dados resultantes desse processamento. Um conjunto de regras tipo SQL estende o alcance das políticas de controlo de acesso a dados, e modelos estatísticos são construídos baseados em cada variável relevante à determinação dos perfis dos utilizadores, sendo utilizados testes estatísticos para analisar as acções dos utilizadores e detectar possíveis intrusões. Também é descrito um método de calibragem automatizado da contribuição de cada uma dessas variáveis no processo global de detecção de intrusões, com base na eficiência que vão apresentando ao longo do tempo nesse mesmo processo. Um método de exposição de risco é definido para fazer a gestão de alertas, que é mais eficiente do que as técnicas de correlação habitualmente utilizadas para este fim, de modo a lidar com a geração de quantidades elevadas de alertas. As avaliações experimentais incluídas nesta tese demonstram a eficiência do SDIBD proposto.

碩士
中原大學
資訊管理研究所
90
We are in the keen competition 21st century; Data Warehousing was changed from strategic weapon to organizational essential. Organization faces large amount and quickly change information in every moment. So, how to collect and manage these data effectively, and use Data Warehouse System to turn it to useful decision information, are decide the company’s competition power. Nevertheless, every organization that initiates a data warehouse project encounters its own unique set of issues around a common set of factors. These include the business climate in which the organization exists, project sponsorship and organization issues, the information intensity of the organization, the technological sophistication of the organization, the age and quality of the operational systems, the quality of the data, and the existing decision-support environment Implement Data Warehouse is not spend long time, and need a lots of capital with highly risk, so many organizations they don’t want to implement hastily. Therefore, Implement Data Warehouse in a effective way, make sure this information infrastructure could successful is very important. The purpose of this research is use case study method,，examine all the critical factors come from relate articles. Find out the important ness for data warehousing, and explorer an organizational prerequisite model for organization. Successful implement Data Warehouse System could help decision maker in organization to share and use widely stored digital data, raise the knowledge worker’s analysis ability, and create the organization’s intelligence. We hope our research could let organizations know the critical factors that will affect the Data Warehousing, and reduce the possible obstacle and risk to fail during the implantation. For the organization that wants to implement the Data Warehouse, they can refer this prerequisite model. Base on their status, to internally assess the likelihood of data warehousing project success, and to identify the areas that require attention prior to commencing implementation.

碩士
國立交通大學
資訊科學系
89
Data warehouse is an information provider that collects necessary data from individual source databases to support the analytical processing of decision-support functions. Data warehousing (DW) and online analytical processing (OLAP) are becoming critical components in decision support system. The queries of OLAP/OLTP requirements in a data warehouse may be very complex and time consuming, a good indexing strategy that can reduce the query time is necessary for the users of the data warehouses. In this thesis, the new, efficient and parallelized indexing methods based upon bit-wised indexing for data warehousing will be proposed to reduce the overhead. Three indexing models, including simple bit-wise indexing method, condensable bit-wise indexing method and frequency-based bit-wised indexing method, are proposed for three types of data warehousing and OLAP environments requires. Also, the corresponding indexing and matching algorithms for such indexing models are also proposed. Finally, the parallelized issues of three indexing methods are proposed.