Answers to the most complex biological questions are rarely determined solely from the experimental evidence. It requires subsequent analysis of many data sources that are often heterogeneous. Most biological data repositories focus on providing only one particular type of data, such as sequences, molecular interactions, protein structure, or gene expression. In many cases, it is required for researchers to visit several different databases to answer one scientific question. It is essential to develop strategies to integrate disparate biological data sources that are efficient and seamless to facilitate the discovery of novel associations and validate existing hypotheses. This thesis presents the design and development of different integration strategies of biological and clinical systems. The BioSPIDA system is a data warehousing solution that integrates many NCBI databases and other biological sources on protein sequences, protein domains, and biological pathways. It utilizes a universal parser facilitating integration without developing separate source code for each data site. This enables users to execute fine-grained queries that can filter genes by their protein interactions, gene expressions, functional annotation, and protein domain representation. Relational databases can powerfully return and generate quickly filtered results to research questions, but they are not the most suitable solution in all cases. Clinical patients and genes are typically annotated by concepts in hierarchical ontologies and performance of relational databases are weakened considerably when traversing and representing graph structures. This thesis illustrates when relational databases are most suitable as well as comparing the performance benchmarks of semantic web technologies and graph databases when comparing ontological concepts. Several approaches of analyzing integrated data will be discussed to demonstrate the advantages over dependencies on remote data centers. Intensive Care Patients are prioritized by their length of stay and their severity class is estimated by their diagnosis to help minimize wait time and preferentially treat patients by their condition. In a separate study, semantic clustering of patients is conducted by integrating a clinical database and a medical ontology to help identify multi-morbidity patterns. In the biological area, gene pathways, protein interaction networks, and functional annotation are integrated to help predict and prioritize candidate disease genes. This thesis will present the results that were able to be generated from each project through utilizing a local repository of genes, functional annotations, protein interactions, clinical patients, and medical ontologies.

本研究之方向係探究建置大型中醫藥倉儲所需之後設資料(Metadata)，並透過此一初探，瞭解與描述其中所需之分析方法，本研究設想Zachman Framework為合適之資料倉儲開發方法，因而由5W1H面向來衍生該專業領域所需之概念。此類概念可再透過一分析程序，確立後設資料。 因時間之限制，本研究採用「本草備要」、「醫方集解」為範例文件進行相關分析，以減少中醫流派林立及中文本身不準確性帶來之問題，當然，本研究在其中亦力求在整體架構上維持其他中醫藥典籍之適用性。 為達成目標，本研究首先探討了至今中草藥資料庫、資料倉儲、電子超文件領域之發展，因而本研究決定將個別之中醫典籍視為「資料專櫃」，而將分類樹、Metadata描述性資料置於目次(catalog)的概念之下，這樣的做法有利於整合其他典籍及其後設資料於大型資料倉儲中。 首先，本研究由重要中醫藥典籍導出基礎性中草藥概念與名詞，其後透過典藏面及應用面之統計分析，確認範例典籍中的Metadata。在實作方面，本研究以BNF來描述和定義Metadata，並以XML為工具完成雛型以供測試之。其中，本研究發現，基於資料倉儲觀點所擷取之後設資料的分析單位較傳統圖書典藏所得之為小。此外，本研究擷取過程中所涉及之Metadata，以功能性者為多，本研究亦採取了若干語言分析以期同時能維持典籍之文字結構。 / The objective of this research work is to acquisit and design Metadata for the construction of data warehouse of Traditional Chinese Medicine (TCM) literatures in the context of knowledge management. In order to solve the problem of preservation and utilization of TCM literatures, this work aims to designate the Metadata based on the viewpoint of knowledge engineering and data warehouse. In this work, the characteristics of the TCM regarding Metadata result in the 5W1H’s principle, while this work argues for its advantages for deriving more functional descriptions and keeping the syntax structure of the originals at the same time. To minimize the constraints of time, this work chooses “Ben Cao Bei Yao” and “Yi Fang Ji Jie” as the target to analyze. In constructing a prototype, the tacit knowledge in the example TCM literatures is converted through an analytic process explicitly into the organizational knowledge that can be easily preserved and processed by machines. Therefore, a statistical process is employed to derive and verify the Metadata in the context of the example TCM literatures. Then, the components regarding the Metadata are implemented with XML tools to develop the prototype. Last but not the least, this work presents its findings as follows: 1. The unit of analysis for deriving Metadata related to data warehouse is usually in a smaller degree of finesse in comparison to what is addressed in the area of traditional library management. 2. Through the Metadata derived in this work based on a data warehouse approach presents more functional elements, we can still maintain the linguistic structure of the example literatures with some careful linguistically analyses in the last step.

根據<CFO Magazine>調查，75%的高階主管在面臨擬定策略時，通常無法獲得即時且完整的資訊，以致於在競爭激烈的產業環境中，失去了主動出擊的機會，也錯失了替公司創造財富的機會。 企業要提升競爭力，除了與企業本身運作的良窳相關外，也與整體的資訊支援能力有高度的關係。企業相關資訊系統的資訊如何作適度的整合，以利相關使用者，因此導入整合管理、決策與資訊科技的商業智慧系統，對內可以提升管理績效，對外以形成競爭優勢，實際化資訊為行動，並將其績效評估以指標式呈現，藉助有效的績效管理來提升服務，降低管理成本，以求競爭力的提升。Kaplan and Norton在平衡計分卡一書上說「如果您不能對他進行衡量，您便無法管理它」。 在傳統資訊應用的環境裡，只要使用者一有資訊的需求，就會要求資訊人員設計程式來列印所需的報表，或查詢所需的資訊，但也因為要更深入瞭解報表的內容，所以需要用更多的報表來作補充說明，如此就陷入了所謂的「傳統報表的惡性循環」裡，而使用者卻感覺永遠少一張報表。 「Get the right information，to the right people，at the right time」是商業智慧系統應用的最佳境界，然而其資料的來源，除了外部資訊外，絕大部份是從企業的歷史資料或由現有的應用系統提供，商業智慧是整合企業所有應用系統的資料，以提供給所有的使用者使用，也就是這些應用系統所產生的資料是商業智慧的基礎，有了這些資料，商業智慧才能發揮功效，兩者是相輔相成，企業如能充分發揮商業智慧和應用系統的各自特點，結合應用，必能提高企業的競爭力。 因此本論文提供一個銷售分析的實際案例，以Top Down的思考模式來定義各階層使用者的需求，而實際開發的步驟是以Bottom Up的方式來建置，以整體規劃，分階段執行的建置方式來確保商業智慧系統專案的成功。

Decision support systems are interactive, computer-based information systems that provide data and analysis tools in order to better assist managers on different levels of organization in the process of decision making. Data warehouses (DWs) have been developed and deployed as an integral part of decision support systems. A data warehouse is a database that allows to store high volume of historical data required for analytical purposes. This data is extracted from operational databases, transformed into a coherent whole, and loaded into a DW during the extraction-transformation-loading (ETL) process. DW data can be dynamically manipulated using on-line analytical processing (OLAP) systems. DW and OLAP systems rely on a multidimensional model that includes measures, dimensions, and hierarchies. Measures are usually numeric additive values that are used for quantitative evaluation of different aspects about organization. Dimensions provide different analysis perspectives while hierarchies allow to analyze measures on different levels of detail. Nevertheless, currently, designers as well as users find difficult to specify multidimensional elements required for analysis. One reason for that is the lack of conceptual models for DW and OLAP system design, which would allow to express data requirements on an abstract level without considering implementation details. Another problem is that many kinds of complex hierarchies arising in real-world situations are not addressed by current DW and OLAP systems. In order to help designers to build conceptual models for decision-support systems and to help users in better understanding the data to be analyzed, in this thesis we propose the MultiDimER model - a conceptual model used for representing multidimensional data for DW and OLAP applications. Our model is mainly based on the existing ER constructs, for example, entity types, attributes, relationship types with their usual semantics, allowing to represent the common concepts of dimensions, hierarchies, and measures. It also includes a conceptual classification of different kinds of hierarchies existing in real-world situations and proposes graphical notations for them. On the other hand, currently users of DW and OLAP systems demand also the inclusion of spatial data, visualization of which allows to reveal patterns that are difficult to discover otherwise. The advantage of using spatial data in the analysis process is widely recognized since it allows to reveal patterns that are difficult to discover otherwise. However, although DWs typically include a spatial or a location dimension, this dimension is usually represented in an alphanumeric format. Furthermore, there is still a lack of a systematic study that analyze the inclusion as well as the management of hierarchies and measures that are represented using spatial data. With the aim of satisfying the growing requirements of decision-making users, we extend the MultiDimER model by allowing to include spatial data in the different elements composing the multidimensional model. The novelty of our contribution lays in the fact that a multidimensional model is seldom used for representing spatial data. To succeed with our proposal, we applied the research achievements in the field of spatial databases to the specific features of a multidimensional model. The spatial extension of a multidimensional model raises several issues, to which we refer in this thesis, such as the influence of different topological relationships between spatial objects forming a hierarchy on the procedures required for measure aggregations, aggregations of spatial measures, the inclusion of spatial measures without the presence of spatial dimensions, among others. Moreover, one of the important characteristics of multidimensional models is the presence of a time dimension for keeping track of changes in measures. However, this dimension cannot be used to model changes in other dimensions. Therefore, usual multidimensional models are not symmetric in the way of representing changes for measures and dimensions. Further, there is still a lack of analysis indicating which concepts already developed for providing temporal support in conventional databases can be applied and be useful for different elements composing a multidimensional model. In order to handle in a similar manner temporal changes to all elements of a multidimensional model, we introduce a temporal extension for the MultiDimER model. This extension is based on the research in the area of temporal databases, which have been successfully used for modeling time-varying information for several decades. We propose the inclusion of different temporal types, such as valid and transaction time, which are obtained from source systems, in addition to the DW loading time generated in DWs. We use this temporal support for a conceptual representation of time-varying dimensions, hierarchies, and measures. We also refer to specific constraints that should be imposed on time-varying hierarchies and to the problem of handling multiple time granularities between source systems and DWs. Furthermore, the design of DWs is not an easy task. It requires to consider all phases from the requirements specification to the final implementation including the ETL process. It should also take into account that the inclusion of different data items in a DW depends on both, users' needs and data availability in source systems. However, currently, designers must rely on their experience due to the lack of a methodological framework that considers above-mentioned aspects. In order to assist developers during the DW design process, we propose a methodology for the design of conventional, spatial, and temporal DWs. We refer to different phases, such as requirements specification, conceptual, logical, and physical modeling. We include three different methods for requirements specification depending on whether users, operational data sources, or both are the driving force in the process of requirement gathering. We show how each method leads to the creation of a conceptual multidimensional model. We also present logical and physical design phases that refer to DW structures and the ETL process. To ensure the correctness of the proposed conceptual models, i.e., with conventional data, with the spatial data, and with time-varying data, we formally define them providing their syntax and semantics. With the aim of assessing the usability of our conceptual model including representation of different kinds of hierarchies as well as spatial and temporal support, we present real-world examples. Pursuing the goal that the proposed conceptual solutions can be implemented, we include their logical representations using relational and object-relational databases.

Cette recherche s'inscrit dans le domaine de la personnalisation d'Entrepôt de Données (ED) et concerne l'aide à l'exploitation d'un ED. Nous intéressons à l'assistance à apporter à un utilisateur lors d'une analyse en ligne, dans son utilisation de ressources d'exploitation existantes. Le domaine d'application concerné est la gestion hospitalière, dans le cadre de la nouvelle gouvernance, et en se limitant au périmètre du Programme de Médicalisation des Systèmes d'Information (PMSI). Cette recherche a été supportée par l'Assistance Publique des Hôpitaux de Marseille (APHM). L'approche retenue pour développer une telle assistance à l'utilisateur d'ED est sémantique et guidée par l'usage d'ontologies. Le système d'assistance mettant en oeuvre cette approche, nommé Ontologies-based Personalization System (OPS), s'appuie sur une Base de Connaissances (BC) exploitée par un moteur de personnalisation. La BC est composée des trois ontologies : de domaine, de l'ED et des ressources. Le moteur de personnalisation permet d'une part une recherche personnalisée de ressources d'exploitation de l'ED en s'appuyant sur le profil de l'utilisateur, et d'autre part pour une ressource particulière, une recommandation de ressources complémentaires selon trois stratégies possibles. Afin de valider nos propositions, un prototype du système OPS a été développé avec un moteur de personnalisation a été implémenté en Java et exploitant une base de connaissance constituée des trois ontologies en OWL interconnectées. Nous illustrons le fonctionnement de notre système sur trois scenarii d'expérimentation liés au PMSI et définis avec des experts métiers de l'APHM. / This research is situated in the domain of Data Warehouses (DW) personalization and concerns DW assistance. Specifically, we are interested in assisting a user during an online analysis processes to use existing operational resources. The application of this research concerns hospital management, for hospitals governance, and is limited to the scope of the Program of Medicalization of Information Systems (PMSI). This research was supported by the Public Hospitals of Marseille (APHM). Our proposal is a semantic approach based on ontologies. The support system implementing this approach, called Ontology-based Personalization System (OPS), is based on a knowledge base operated by a personalization engine. The knowledge base is composed of three ontologies: a domain ontology, an ontology of the DW structure, and an ontology of resources. The personalization engine allows firstly, a personalized search of resources of the DW based on users profile, and secondly for a particular resource, an expansion of the research by recommending new resources based on the context of the resource. To recommend new resources, we have proposed three possible strategies. To validate our proposal, a prototype of the OPS system was developed, a personalization engine has been implemented in Java. This engine exploit an OWL knowledge composed of three interconnected OWL ontologies. We illustrate three experimental scenarios related to PMSI and defined with APHM domain experts.

Cette thèse traite de l’établissement d’une plateforme logicielle nommée DaWeS permettant le déploiement et la gestion en ligne d’entrepôts de données alimentés par des données provenant de services web et personnalisés à destination des petites et moyennes entreprises. Ce travail s’articule autour du développement et de l’expérimentation de DaWeS. L’idée principale implémentée dans DaWeS est l’utilisation d’une approche virtuelle d’intégration de données (la médiation) en tant queprocessus ETL (extraction, transformation et chargement des données) pour les entrepôts de données gérés par DaWeS. A cette fin, un algorithme classique de réécriture de requêtes (l’algorithme inverse-rules) a été adapté et testé. Une étude théorique sur la sémantique des requêtes conjonctives et datalog exprimées avec des relations munies de limitations d’accès (correspondant aux services web) a été menée. Cette dernière permet l’obtention de bornes supérieures sur les nombres d’appels aux services web requis dans l’évaluation de telles requêtes. Des expérimentations ont été menées sur des services web réels dans trois domaines : le marketing en ligne, la gestion de projets et les services d’aide aux utilisateurs. Une première série de tests aléatoires a été effectuée pour tester le passage à l’échelle. / The role of data warehouse for business analytics cannot be undermined for any enterprise, irrespective of its size. But the growing dependence on web services has resulted in a situation where the enterprise data is managed by multiple autonomous and heterogeneous service providers. We present our approach and its associated prototype DaWeS [Samuel, 2014; Samuel and Rey, 2014; Samuel et al., 2014], a DAta warehouse fed with data coming from WEb Services to extract, transform and store enterprise data from web services and to build performance indicators from them (stored enterprise data) hiding from the end users the heterogeneity of the numerous underlying web services. Its ETL process is grounded on a mediation approach usually used in data integration. This enables DaWeS (i) to be fully configurable in a declarative manner only (XML, XSLT, SQL, datalog) and (ii) to make part of the warehouse schema dynamic so it can be easily updated. (i) and (ii) allow DaWeS managers to shift from development to administration when they want to connect to new web services or to update the APIs (Application programming interfaces) of already connected ones. The aim is to make DaWeS scalable and adaptable to smoothly face the ever-changing and growing web services offer. We point out the fact that this also enables DaWeS to be used with the vast majority of actual web service interfaces defined with basic technologies only (HTTP, REST, XML and JSON) and not with more advanced standards (WSDL, WADL, hRESTS or SAWSDL) since these more advanced standards are not widely used yet to describe real web services. In terms of applications, the aim is to allow a DaWeS administrator to provide to small and medium companies a service to store and query their business data coming from their usage of third-party services, without having to manage their own warehouse. In particular, DaWeS enables the easy design (as SQL Queries) of personalized performance indicators. We present in detail this mediation approach for ETL and the architecture of DaWeS. Besides its industrial purpose, working on building DaWeS brought forth further scientific challenges like the need for optimizing the number of web service API operation calls or handling incomplete information. We propose a bound on the number of calls to web services. This bound is a tool to compare future optimization techniques. We also present a heuristics to handle incomplete information.

BI, business intelligence, which means to collect and analyse data to inform business decisions, is a concept that has grown to be a significant part of business development. In most cases, a storage platform is necessary to provide data from the companies’ different data sources to the BI-tools. There are different ways of doing this. Some of them are by the help of a data lake, a data warehouse or a combination of both. By taking this into consideration the purpose of this study is to create an architectural design of a storage platform for small and medium-sized enterprises (SME). To be able to formulate a result which holds as high validity and reliability as possible, this study conducts both a literature and a case study. The case study has taken place at an IT service company which classifies as a SME and the working methodology has been an agile approach with scrum as reference. This method was chosen to be able to efficiently follow the customer demands.  The architecture provided consists of a combination of a data hub, which acts as a data lake, and a data warehouse. The data hub differs from a data lake by harmonizing and indexing data, which makes it easier to handle. Regarding the intension of the data warehouse, it is to yield relevant and processed data to BI-tools. The architecture design of the platform that has been developed in this study cannot be said to be applicable by all companies. Instead, it can be a basis for companies that are thinking about creating a data platform. / BI, Business intelligence, vilket betyder att samla och analysera data som beslutsstöd, har växt till att vara en betydande del inom företagsutveckling. För att möjliggöra för BI krävs ofta att en datalagringsplattform tillhandahåller data från verksamhetens datakällor. Det finns olika sätt att göra detta, men några av dem är med hjälp av en datasjö, ett datalager eller en kombination av båda. Mot bakgrund av detta, har den här studien syftat till att framställa en övergripande arkitektur som lämpar sig till små  och medelstora företag (SMF). För att frambringa ett resultat med så  hög validitet och reliabilitet som mö jligt baseras studien på  både en litteraturstudie och en fallstudie. Fallstudien har genomförts på  ett IT-konsultföretag som klassas som SMF och arbetsmetodiken har varit agil och baserats pa  scrum. Denna metod valdes för att på  ett effektivt sä tt säkerställa att kundens krav uppfylldes. Den arkitektur som föreslagits är en kombination mellan en datahubb, vilket verkar som en datasjö , och ett datalager. Datahubben skiljer sig från datasjön eftersom den harmoniserar och indexerar data, vilket gör den enklare att hantera. Rörande datalagret, ämnar det tillgodose bearbetad och relevant data för BI-verktyg. Den övergripande arkitektur som presenteras anses inte vara applicerbar för alla verksamheter, men kan användas som en grund för verksamheter som tänker implementera en lagringsplattform.

Being able to perform good data analysis is a fundamental part of running any business or organization. One way of enabling data analysis is with a data warehouse system, a type of database that gathers and transforms data from multiple sources and structures it in the goal of simplifying analysis. It is commonly used to provide support in decision-making. Although a data warehouse enables data analysis, it is also relevant to consider how well the system supports analysis. This thesis is a qualitative research that aims to investigate how work domain analysis (WDA) can be used to evaluate the design of a data warehouse system. To do so, a case study at the IT company Norconsult Astando was performed. A data warehouse system was designed for an issue management system and evaluated using the abstraction hierarchy (AH) model. The research done in this thesis showed that analysis was enabled by adopting Kimball’s bottom-up approach and a star schema design with an accumulating snapshot fact table. Through evaluation of the design, it was shown that most of the design choices made for the data warehouse were captured in the AH. It was concluded that with sufficient data collection methods, WDA can be used to a large extent when evaluating a data warehouse system.

A utilização de bases de dados para estudos epidemiológicos, avaliação da qualidade e quantidade dos serviços de saúde vem despertando a atenção dos pesquisadores no contexto da Saúde Pública. No Brasil, as bases de dados do Sistema Único de Saúde (SUS) são exemplos de repositórios importantes que reúnem informações fundamentais sobre a Saúde. Entretanto, apesar dos avanços em termos de coleta e de ferramentas públicas para a pesquisa nessas bases de dados, tais como o TABWIN e o TABNET, esses recursos ainda não fazem uso de técnicas mais avançadas para a produção de informação gerencial, como as disponíveis em ferramentas OLAP (On Line Analytical Processing) e de mineração de dados. A situação é extremamente agravada pelo fato dos dados da Saúde Pública, produzidos por vários sistemas isolados, não estarem integrados, impossibilitando pesquisas entre diferentes bases de dados. Consequentemente, a produção de informação gerencial torna-se uma tarefa extremamente difícil. Por outro lado, a integração dessas bases de dados pode constituir um recurso indispensável e fundamental para a manipulação do enorme volume de dados disponível nesses ambientes e, assim, possibilitar a produção de informação e conhecimento relevantes, que contribuam para a melhoria da gestão em Saúde Pública. Acompanhar o seguimento de pacientes e comparar diferentes populações são outras importantes limitações das atuais bases de dados, uma vez que não há um identificador unívoco do paciente que possibilite executar tais tarefas. Esta Tese teve como objetivo a construção de um armazém de dados (data warehouse), a partir da análise de dez anos (período de 2000 a 2009) das principais bases de dados do SUS. Os métodos propostos para coleta, limpeza, padronização das estruturas dos bancos de dados, associação de registros ao paciente e integração dos sistemas de informação do SUS permitiram a identificação e o seguimento do paciente com sensibilidade de 99,68% e a especificidade de 97,94%. / The use of databases for epidemiologic studies, quality and quantity evaluation of health services have attracted the attention of researchers in the context of Public Health. In Brazil, the databases of the Sistema Único de Saúde (SUS) are examples of important repositories, which store fundamental information about health. However, despite of the advances in terms of load and public tools for research in those databases, such as TABWIN and TABNET, these resources do not use advanced techniques to produce management information as available in OLAP (On Line Analytical Processing) and data mining tools. The situation is drastically increased for the fact that data in public health, produced for different systems, are not integrated. This makes impossible to do research between different databases. As a consequence, the production of management information is a very difficult task. On the other hand, the integration of these databases can offer an important and fundamental resource to manipulate the enormous volume of data available in those environments and, in this way, to permit the production of relevant information and knowledge to improve the management of public health. The patient follow up and the comparison of different populations are other important limitations of the available databases, due to the absence of a common patient identifier. The objective of this Thesis was the construction of a data warehouse to analyze ten years (period from 2000 to 2009) of the principal databases of SUS. The proposed methods to load, clean, database structure standardization, patient record linkage and SUS information systems integration have been permitted patient identification and follow up with sensitivity of 99.6% and specificity of 97.94%.

Nesta dissertação, são discutidas as vantagens e dificuldades de um Sistema Gerencial de Apoio a Decisão em um ambiente Intranet/Internet, sua execução, bem como a utilização de aplicações de bancos de dados na web. Um sistema dessa natureza, denominado SAG (Sistema de Apoio Gerencial), foi concebido para dar suporte às atividades de gerência, supervisão e controle da rede de distribuição de energia elétrica da Eletropaulo Metropolitana (São Paulo, Brasil). O SAG possibilita o estabelecimento de uma sistemática de supervisão, visando, a partir da análise das condições atuais da rede de distribuição e em função dos recursos disponíveis, permitir o acompanhamento da evolução da qualidade do fornecimento de energia elétrica. Disponibilizando informações que irão orientar ações para corrigir os possíveis desvios inadequados e fixar políticas e diretrizes a serem seguidas nos níveis gerenciais. Como resultado, é esperado tornar mais ágil o processo decisório, bem como o acesso a dados técnicos e de carregamento dos equipamentos da rede de distribuição. / In this dissertation are discussed the advantages and difficulties of an Executive Information System (EIS) implementation on an Intranet or Internet environment, as well as the use of Database applications on the web. An Executive Information System named SAG was implemented at Eletropaulo Metropolitana (São Paulo, Brazil); it was conceived to address maintenance, operational and engineering departments\' needs. The System allows the establishment of a systematic supervision to attend the quality of the electric energy supplied. The analysis of the actual distribution network conditions, the available resources and the electric energy supplied quality will guide the actions to correct possible inadequacies as well as set and fix policies and guidelines to be followed on a manager\'s level. As a result it\'s expected faster decision-making process and access on distribution network equipment\'s technical data and load.