Les systèmes d’information bancaires, outils incontournables de la stratégie des banques, sont devenus complets et complexes. Et les systèmes d’information décisionnels ou stratégiques deviennent de plus en plus présents.Or, il persiste encore des inefficacités en matière de conception des systèmes d’informations, dues à une conception sauvage ou plutôt une construction sauvage des systèmes d’informations stratégiques, avec une mise à l’écart systématique des utilisateurs finals.Une solution parait être de s’appuyer sur l’intelligence économique pour tenter de résoudre le problème de la construction de ces systèmes d’information stratégiques, et donc d’améliorer la prise de décision. Car, le système d’informations stratégique, noyau des systèmes décisionnels, est le cœur même du système d’intelligence économique.Notre thèse est qu’une démarche d’intelligence économique appliquée à la conception des systèmes d’informations bancaires permettrait de réduire le « risque prêt ». Ceci, précisément, dans le domaine de la banque de détail et pour la clientèle des particuliers, des professionnels et des entrepreneurs.- Risque pour le client qui ne doit pas se lancer dans des remboursements qu’il ne pourra assumer, s’engager dans un projet de prêt qui ne correspondrait pas aux enjeux qu’il s’est définis ;- Risque évidemment pour la banque qui ne tient pas à accumuler des clients non solvables et ce qui ne correspondrait pas non plus à des enjeux définis par les décideurs de la banque.Après avoir rappelé les particularités de la banque et la complexité de son environnement, nous montrerons en quoi l'approche actuelle de la gestion des risques au sein des banques nous paraît « incomplète » et segmentée, et de ce fait, perfectible notamment pour ce qui concerne la clientèle des particuliers et des professionnels. Ensuite, nous comptons proposer des règles méthodologiques pour la conception de systèmes d’informations stratégiques bancaires, ainsi qu’un modèle d’architecture d’un tel système prenant en compte les besoins de l’utilisateur final qui sera, dans le cas de notre thèse, le décisionnaire d’un dossier de crédit ou bien le conseiller bancaire, voire l’analyste du risque de crédit. Enfin, après avoir établi ce modèle d'architecture de système d’informations stratégiques, nous comparerons ce qu'il permettrait d'améliorer, relativement à l'existant. Notre thèse se situe au carrefour, au confluent, d'une thèse en sciences de gestion, plus particulièrement en finance bancaire, et d’une thèse en système d'informations et en informatique ; et elle s’appuie en grande partie sur notre expérience professionnelle dans le secteur bancaire en France.Ainsi, avec le domaine bancaire, nous souhaitons explorer un nouveau domaine d’application des recherches en intelligence économique, notamment en liaison avec les résultats issus des travaux de l’équipe de recherches SITE (« Modélisation et Développement de Systèmes d’Intelligence Economique ») du LORIA (Laboratoire Lorrain de Recherches en Informatique et ses Applications) pour ce qui concerne la conception de systèmes d’informations pour l’intelligence économique.Après avoir présenté le concept d’intelligence économique et le processus décisionnel, nous montrerons les spécificités de la banque et de son système d’informations. Ensuite, nous expliciterons les difficultés de la gestion du risque de crédit au sein des banques avant de présenter nos propositions pour la mise en place d’un système d’informations stratégiques permettant d’améliorer la gestion du risque de crédit bancaire. / Bank Information Systems, key tools in banking strategies, have become comprehensive and complex. And the decision-making or strategic information systems are playing an increasingly more important role.Nevertheless, some inefficiencies in the conception of information systems still continue to exist, due to the uncontrolled design or rather construction of strategic information systems, systematically alienating the end-user.One solution seems to be to rely on economic intelligence to attempt to solve the matter of the construction of those strategic information systems, and consequently to improve decision-making. Because, the strategic information system, the core of decision-making systems, is the heart itself of the economic intelligence system.Our theory is that an approach of economic intelligence applied to the conception of information systems in banking would allow the reduction of the “loan risk”. This specifically in the sector of retail banking and for the individual, professional and contractor customer.- Risk for the customer who should not start loan payments which he cannot cover, or commit to loan projects which do not match the stakes he would have set himself.- Risk obviously for the bank which is not willing to accumulate uncreditworthy customers, and which would not match either the stakes set by the bank decision-makers.After putting emphasis on the distinctive features of the bank and the complexity of its environment, we will show the evidence that the current approach to risk management inside banks seems “incomplete” and fragmented, and consequently, where there is room for improvement particularly for individual and professional customers.Then, we intend to suggest some methodological rules for the conception of strategic information systems in banking, as well as a business model of such a system taking into account the needs of the end-user who will be, as shown in this present thesis, the decision-maker of a credit file or the bank adviser, or even the credit risk analyst. Finally, after drawing up this model of strategic information systems, we will compare how it could improve on the existing one. Our thesis is situated at a crossroads, at a confluence, of a thesis in management sciences, more particularly in bank finance, and of a thesis in information systems, and in computer science; and it leans largely on our professional experience in the banking sector in France.Thus, along with the banking sector, we wish to explore the new field of application of research in economic intelligence, particularly linked to the results stemming from the work by the research team SITE of LORIA as far as the conception of information systems for economic intelligence is concerned.After introducing the concept of economic intelligence and the decision-support process (chapter no. 1), we will outline the specificities of the banking sector and its information systems (chapter no. 2). Then we will clarify the difficulties of credit risk management within banks (chapter no. 3) before submitting our proposals for the implementation of a strategic information system enabling the improvement of credit risk management in banking (chapter no. 4).

Aliada à crescente evolução da computação, dois fatores também começaram a receber maior atenção: o conhecimento e a informação. Esta evolução faz com que a informação possa estar disponibilizada a todos, contribuindo de certa forma, para o auxílio na aquisição de conhecimento e visando posterior tomada de decisão, observa-se também a grande importância dos bancos de dados. Este trabalho apresenta a proposta de um Sistema de Apoio à Decisão (SAD), utilizando-se de avançadas metodologias de armazenamento de dados em poderosos bancos de dados (Data Warehouse), para o controle e gerenciamento agrícola em usinas de açúcar e álcool. Foi desenvolvido um ambiente estruturado, extensível, projetado para a análise de dados não voláteis, logicamente e fisicamente transformados, provenientes de diversas aplicações, alinhados com a estrutura da empresa, atualizados e mantidos por um longo período de tempo, referidos em termos utilizados no negócio e sumarizados para análise rápida. Com a implantação destas novas tecnologias, a empresa estará apta a obter informações do nível gerencial e estratégico para ajudar nos seus processos de tomada de decisão, o que antes não era possível com os atuais sistemas de informação existentes na empresa. / Information and knowledge are receiving more attention with the growing of computing evolution. This evolution provides available information to everybody contributing for the acquisition of knowledge, transforming the database in an important key of this evolution process. This work presents a research about the use of technologies of systems of support to the decision by using methodologies of data storage in powerful data bases (Data Warehouse) for the control and management agricultural in sugar and ethanol plants. An environment was developed for the analysis of non-volatile data, extensible, projected and physically transformed, proceeding from diverse applications, aligned with the structure of the company, updated and kept for a long period of time, related in business\' terms and grouped for fast analysis. With the implantation of these new technologies, the company will be able to obtain information of the managerial and strategic level to help on its decision making processes, what was not possible before with the current systems of information existent at the company.

Made available in DSpace on 2015-04-14T13:56:11Z (GMT). No. of bitstreams: 1 405417.pdf: 1389351 bytes, checksum: 6f00a0ed9add46a5b7f8106681a81fdd (MD5) Previous issue date: 2008-01-31 / Com abertura do mercado de energia el?trica e o aumento da competitividade no setor el?trico brasileiro, as concession?rias de energia buscam ferramentas para minimizar as perdas comerciais e maximizar seus lucros. Visando solucionar este problema foi desenvolvido um m?todo de minera??o de dados para detec??o de desvio de comportamento no uso de energia em concession?ria de energia el?trica. Pois quanto menos perde-se, menos precisa ser gerado, e menos se desperdi?a recursos naturais. Na elabora??o do m?todo compreendeu etapas de an?lise e avalia??o dos dados, assim como constru??o de um Data Warehouse mais adequado para o desenvolvimento deste trabalho. Foram analisadas curvas de cargas dos clientes e atrav?s dessa an?lise observou-se o perfil de consumo dos mesmos, embasados na an?lise foram aplicados os algoritmos de minera??o de dados, como o algoritmo de associa??o Apriori para fornecer padr?es de indicadores de perfil dos consumidores bem como os algoritmos de ?rvore de Decis?o e Classificadores Bayesianos. Os resultados validam o m?todo desenvolvido e implementado permitindo sua utiliza??o em uma concession?ria de energia el?trica sendo utilizado como mais uma ferramenta de GLD para auxiliar e somar-se a a??es j? existentes na concession?ria.

Nos últimos anos, a utilização do Data Warehouse mudou significativamente. Nos dias de hoje, as empresas precisam distribuir informações, num intervalo de tempo adequado, para os colaboradores que atuam na linha de frente do negócio, de modo que estes possam obter um desempenho superior na execução de suas tarefas. O presente trabalho apresenta as novas tendências em inteligência empresarial, dando destaque aos aspectos táticos e operacionais, a necessidade de redução na latência de atualização dos dados, as limitações do ambiente de Data Warehouse nesse contexto. O desenvolvimento deste trabalho baseou-se na revisão da literatura existente, no estudo de novos mecanismos de integração de dados, na apresentação de casos reais de aplicação e em um relato de caso prático. Este trabalho buscou destacar o papel fundamental que as novas tecnologias de integração de dados têm na criação de novos sistemas de apoio e automação de decisões nas empresas realizando negócios em tempo real. / During the last years many things have changed in the utilization the Data Warehouse. Nowadays, enterprises need to distribute information in a timely fashion to front-line workers, enabling them to deliver superior performance in the execution of their duties. This work presents new trends in business intelligence, emphasizing the tactical and operational aspects, the demand for reducing data latency, the limitations of the Data Warehouse in this context. The development of this work was based on the review of existing literature, the study of new mechanisms for data integration, the presentation of real case scenarios of application and a practical case story. This study highlights the key role that the new data integration technologies have while are creating new automating decisions systems in companies doing business in real time.

Data warehousing is becoming an increasingly important technology for information integration and data analysis. Given the dynamic nature of modern distributed environments, both source data updates and schema changes are likely to occur autonomously and even concurrently in different data sources. Current approaches to maintain a data warehouse in such dynamic environments sequentially schedule maintenance processes to occur in isolation. Furthermore, each maintenance process is handling the maintenance of one single source update. This limits the performance of current data warehouse maintenance systems in a distributed environment where the maintenance of source updates endures the overhead of network delay as well as IO costs for each maintenance query. In this thesis work, we propose two different optimization strategies which can greatly improve data warehouse maintenance performance for a set of source updates in such dynamic environments. Both strategies are able to support source data updates and schema changes. The first strategy, the parallel data warehouse maintainer, schedules multiple maintenance processes concurrently. Based on the DWMS_Transaction model, we formalize the constraints that exist in maintaining data and schema changes concurrently and propose several parallel maintenance process schedulers. The second strategy, the batch data warehouse maintainer, groups multiple source updates and then maintains them within one maintenance process. We propose a technique for compacting the initial sequence of updates, and then for generating delta changes for each source. We also propose an algorithm to adapt/maintain the data warehouse extent using these delta changes. A further optimization of the algorithm also is applied using shared queries in the maintenance process. We have designed and implemented both optimization strategies and incorporated them into the existing DyDa/TxnWrap system. We have conducted extensive experiments on both the parallel as well as the batch processing of a set of source updates to study the performance achievable under various system settings. Our findings include that our parallel maintenance gains around 40 ~ 50% performance improvement compared to sequential processing in environments that use single-CPU machines and little network delay, i.e, without requiring any additional hardware resources. While for batch processing, an improvement of 400 ~ 500% improvement compared with sequential maintenance is achieved, however at the cost of less frequent refreshes of the data warehouse content.

Made available in DSpace on 2015-04-14T14:49:21Z (GMT). No. of bitstreams: 1 423037.pdf: 1478944 bytes, checksum: d2974fba2b6145147b3c83b7003075fb (MD5) Previous issue date: 2010-03-16 / Embora o ?ltimo levantamento epidemiol?gico (BRASIL, 2006) revele uma sens?vel melhora na condi??o bucal da popula??o brasileira nas ?ltimas duas d?cadas, patologias bucais ainda demandam grandes investimentos financeiros por parte do governo federal. Este trabalho tem como objetivo desenvolver e documentar uma abordagem computacional (Knowledge Discovery in Database) capaz de gerenciar grandes volumes de dados e produzir modelos mais compreens?veis para o suporte ? tomada de decis?o, formula??o de melhores pol?ticas de sa?de bucal, bem como a viabiliza??o de novas atividades de ensino e pesquisa na ?rea. Para tanto, analisamos uma amostra de dados referente ?s fichas odontol?gicas de 598 pacientes de baixa renda, atendidos junto a uma unidade da PUCRS vinculada ao SUS (Sistema ?nico de Sa?de). Como principal resultado deste trabalho, identificamos oportunidades de data mining pela extra??o de modelos preditivos aplicados ? an?lise de patologias periodontais, m?-oclus?o e indicadores de c?rie dent?ria.

Nos últimos anos, a utilização do Data Warehouse mudou significativamente. Nos dias de hoje, as empresas precisam distribuir informações, num intervalo de tempo adequado, para os colaboradores que atuam na linha de frente do negócio, de modo que estes possam obter um desempenho superior na execução de suas tarefas. O presente trabalho apresenta as novas tendências em inteligência empresarial, dando destaque aos aspectos táticos e operacionais, a necessidade de redução na latência de atualização dos dados, as limitações do ambiente de Data Warehouse nesse contexto. O desenvolvimento deste trabalho baseou-se na revisão da literatura existente, no estudo de novos mecanismos de integração de dados, na apresentação de casos reais de aplicação e em um relato de caso prático. Este trabalho buscou destacar o papel fundamental que as novas tecnologias de integração de dados têm na criação de novos sistemas de apoio e automação de decisões nas empresas realizando negócios em tempo real. / During the last years many things have changed in the utilization the Data Warehouse. Nowadays, enterprises need to distribute information in a timely fashion to front-line workers, enabling them to deliver superior performance in the execution of their duties. This work presents new trends in business intelligence, emphasizing the tactical and operational aspects, the demand for reducing data latency, the limitations of the Data Warehouse in this context. The development of this work was based on the review of existing literature, the study of new mechanisms for data integration, the presentation of real case scenarios of application and a practical case story. This study highlights the key role that the new data integration technologies have while are creating new automating decisions systems in companies doing business in real time.

The Business Process Management (BPM) lifecycle consists of four phases: design and analysis, configuration, enactment, and evaluation - also known as performance analysis and improvement. Performance analysis and improvement of business processes, one of the core phases in the BPM life cycle, is becoming on top of the agenda for many enterprises. An emerging approach to that is to use the business intelligence approaches that attempt to facilitate the analytical capabilities of business process management systems by implementing process-oriented data warehouse and mining techniques. However, little work has been has done on developing core methods and tools for performance analysis and improvement of business processes. In particular, adequate methods, clearly defined steps or instructions that can guide process managers for analyzing and improving processes using process warehouse (PW) are not available. In the absence of such methods, guidelines or clearly defined steps, important steps may be ignored and credible improvements steps cannot be taken. This research addresses the described limitations by developing a method for performance analysis and improvement of business processes. The key feature of the developed method is, it employs business-orientation in the design and utilization of a PW. The method is composed of three steps, building goal-structure, integrating goal-structure with PW, and analyzing and improving business processes. During the first step, a set of top-level performance goals are identified for the process of interest. Subsequently, the identified  goals are decomposed to generate a goal-structure that is aligned with the functional decomposition of the process of interest. The second step describes a technique for integrating the generated goal-structure with PW. The third step describes, a performance estimation model, a decision model and a step by step approach that focuses on utilizing PW for analysis and improvement of business processes. In order to facilitate the use of the proposed method a prototype is developed. The prototype offers a graphical user interface for defining goal structure, integrating goals with PW, and goal-based navigation of PW. In order to evaluate the proposed method, we first develop an evaluation framework and subsequently use it for the evaluation of the proposed method. The framework consists of three components, each representing a type of evaluation. The components are, methodological-structure evaluation, performance-based evaluation and perception-based evaluation. The results of the evaluation show partial support for the methodological structure. However, the results of performance and perception evaluation show promising results of the proposed method. / <p>QC 20121217</p>

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.

Wurden früher Data-Warehouse-Systeme meist nur zur Datenanalyse für die Entscheidungsunterstützung des Managements eingesetzt, haben sie sich nunmehr zur zentralen Plattform für die integrierte Informationsversorgung eines Unternehmens entwickelt. Dies schließt vor allem auch die Einbindung des Data-Warehouses in operative Prozesse mit ein, für die zum einen sehr aktuelle Daten benötigt werden und zum anderen eine schnelle Anfrageverarbeitung gefordert wird. Daneben existieren jedoch weiterhin klassische Data-Warehouse-Anwendungen, welche hochqualitative und verfeinerte Daten benötigen. Die Anwender eines Data-Warehouse-Systems haben somit verschiedene und zum Teil konfligierende Anforderungen bezüglich der Datenaktualität, der Anfragelatenz und der Datenstabilität. In der vorliegenden Dissertation wurden Methoden und Techniken entwickelt, die diesen Konflikt adressieren und lösen. Die umfassende Zielstellung bestand darin, eine Echtzeit-Data-Warehouse-Architektur zu entwickeln, welche die Informationsversorgung in seiner ganzen Breite -- von historischen bis hin zu aktuellen Daten -- abdecken kann. Zunächst wurde ein Verfahren zur Ablaufplanung kontinuierlicher Aktualisierungsströme erarbeitet. Dieses berücksichtigt die widerstreitenden Anforderungen der Nutzer des Data-Warehouse-Systems und erzeugt bewiesenermaßen optimale Ablaufpläne. Im nächsten Schritt wurde die Ablaufplanung im Kontext mehrstufiger Datenproduktionsprozesse untersucht. Gegenstand der Analyse war insbesondere, unter welchen Bedingungen eine Ablaufplanung in Datenproduktionsprozessen gewinnbringend anwendbar ist. Zur Unterstützung der Analyse komplexer Data-Warehouse-Prozesse wurde eine Visualisierung der Entwicklung der Datenzustände, über die Produktionsprozesse hinweg, vorgeschlagen. Mit dieser steht ein Werkzeug zur Verfügung, mit dem explorativ Datenproduktionsprozesse auf ihr Optimierungspotenzial hin untersucht werden können. Das den operativen Datenänderungen unterworfene Echtzeit-Data-Warehouse-System führt in der Berichtsproduktion zu Inkonsistenzen. Daher wurde eine entkoppelte und für die Anwendung der Berichtsproduktion optimierte Datenschicht erarbeitet. Es wurde weiterhin ein Aggregationskonzept zur Beschleunigung der Anfrageverarbeitung entwickelt. Die Vollständigkeit der Berichtsanfragen wird durch spezielle Anfragetechniken garantiert. Es wurden zwei Data-Warehouse-Fallstudien großer Unternehmen vorgestellt sowie deren spezifische Herausforderungen analysiert. Die in dieser Dissertation entwickelten Konzepte wurden auf ihren Nutzen und ihre Anwendbarkeit in den Praxisszenarien hin überprüft.