No description available.

This study compares the impact of dimensional modelling and data vault modelling on the performance and maintenance effort of data warehouses. Dimensional modelling is a data warehouse modelling technique pioneered by Ralph Kimball in the 1980s that is much more effective at querying large volumes of data in relational databases than third normal form data models. Data vault modelling is a relatively new modelling technique for data warehouses that, according to its creator Dan Linstedt, was created in order to address the weaknesses of dimensional modelling. To date, no scientific comparison between the two modelling techniques have been conducted. A scientific comparison was achieved in this study, through the implementation of several experiments. The experiments compared the data warehouse implementations based on dimensional modelling techniques with data warehouse implementations based on data vault modelling techniques in terms of load performance, query performance, storage requirements, and flexibility to business requirements changes. An analysis of the results of each of the experiments indicated that the data vault model outperformed the dimensional model in terms of load performance and flexibility. However, the dimensional model required less storage space than the data vault model. With regards to query performance, no statistically significant differences existed between the two modelling techniques. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2014

This study compares the impact of dimensional modelling and data vault modelling on the performance and maintenance effort of data warehouses. Dimensional modelling is a data warehouse modelling technique pioneered by Ralph Kimball in the 1980s that is much more effective at querying large volumes of data in relational databases than third normal form data models. Data vault modelling is a relatively new modelling technique for data warehouses that, according to its creator Dan Linstedt, was created in order to address the weaknesses of dimensional modelling. To date, no scientific comparison between the two modelling techniques have been conducted. A scientific comparison was achieved in this study, through the implementation of several experiments. The experiments compared the data warehouse implementations based on dimensional modelling techniques with data warehouse implementations based on data vault modelling techniques in terms of load performance, query performance, storage requirements, and flexibility to business requirements changes. An analysis of the results of each of the experiments indicated that the data vault model outperformed the dimensional model in terms of load performance and flexibility. However, the dimensional model required less storage space than the data vault model. With regards to query performance, no statistically significant differences existed between the two modelling techniques. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2014

Esta pesquisa tem como objetivo analisar o sistema de informação do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto USP a fim de identificar quais são os problemas que afetam a geração de informações gerenciais no nível estratégico, bem como a elaboração do plano de desenvolvimento e etapas necessárias para a construção de um sistema de informações gerenciais. Trata-se de uma revisão bibliográfica de publicações como: livros, artigos, dissertações de mestrado e teses de doutorado, acerca de Sistemas de Informações, Sistemas de Informações Gerenciais, Sistemas de Informações Hospitalares, Inteligência de Negócios, Gestão em Saúde e Gestão Hospitalar. Numa primeira etapa foi feita a leitura dos resumos dos estudos encontrados, a fim de averiguar a adequação desses estudos aos objetivos deste trabalho. Na segunda, foi feita a leitura integral dos trabalhos que foram selecionados na primeira, onde foi observada a coerência entre proposta, metodologia e resultados de tais trabalhos. Na terceira, foi elaborado um plano de desenvolvimento e etapas necessárias para a construção de um sistema de informações gerenciais para algumas áreas de negócio do hospital. Na última década, pelo menos nas grandes corporações de saúde e em alguns poucos hospitais de menor porte do país este panorama experimentou um crescimento bastante expressivo. O processo de informatização hospitalar é bastante complexo, principalmente em um hospital universitário que além de assistência a saúde trabalha em prol do ensino e pesquisa. Um sistema de informação hospitalar é concebido a fim de atuar e contribuir em três níveis hierárquicos: operacional, gerencial e estratégico. O hospital possui um sistema de informação integrado composto de quatro dezenas de sub-sistemas integrados. Os gastos do hospital, levados por diversos fatores, são cada vez maiores. Seria necessário um incremento no montante dos recursos alocados como também uma racionalidade na aplicação dos mesmos. Os gestores devem conseguir aumentar a produtividade a fim de alcançar maiores níveis de cobertura para satisfazer as necessidades dos usuários dos serviços de saúde. Fica evidente a necessidade de transformar dados em informações para a tomada de decisões gerenciais. Grande parte dos dados que necessitamos está armazenada nos sub-sistemas do HCFMRP, porém transformá-los em informações não é tarefa fácil. Não há nada de errado com estes sistemas e seus bancos de dados operacionais, os mesmos em primeira instância foram criados para dar suporte aos processos da empresa e suas operações, seus dados estão armazenados de maneira pormenorizada, ou seja, nos mínimos detalhes, prejudicando assim outras funções como apoio a tomada de decisão gerencial. Torna-se necessário à existência de um ambiente propício para consultas específicas com acessos rápidos e disponibilidade de informações. A fim de vencer este desafio a tecnologia de Data Warehouse se apresenta como alternativa para simplificar, agilizar e qualificar o processo de apoio à tomada de decisão gerencial. / This research has for objective to analyze the Information System of the Clinical Hospital of the Faculty of Medicine of Ribeirão Preto (HCFMRP-USP) to identify the problems that affects the information generation at the strategic level, as well as the elaboration of a development plan and the phases needed to construct a Management Information System. This researched was based on bibliographic review of books, articles, dissertations, theses, and other source that were related to Information Systems, Management Information Systems, Hospitals Information Systems, Business Intel ligence, Health Management and Hospital Management. This work was divided into phases. The first phase of this work was the reading of the summary of the found material to verify the fitnesses of this study to the main objective of this research. The second was the full reading of the original material selected in the first phase. In this moment was observed the coherence between the proposal, the methods and the results of such works. In the third phase was developed a plan to create a Management Information System for specifics areas in the hospital, and the phases to build it was defined. In this last decade, at least in largest health corporation and in some small hospital of Brazil this scenery experienced an expressive growth. The hospital\'s informatization process is very complex, especially in university hospital in which besides taking care of health problems it has to deal with teaching and research. A hospital information system is design to contribute to three hierarchy levels: operational, management and strategic. The hospital (HCFMRP-USP) has an integrated information system that has for dozen of integrated subsystems. The amount spent by the hospital is increased by different factors. Would be necessary to increase the allocated resources as well as a better rationale to use them. The managers must increase the productivity in order to achieve greater levels of coverage to satisfy the client\'s needs for better healthy services. It is evident the need to transform data in information to support management decisions. Great part of the data that are needed is stored in subsystems at HCFMRP, but to turn them into information isn\'t as simple as it may seem. There is nothing wrong with this system or its operational database. They were developed to support the corporation and its operations, and the data are stored in such structured detailed that doesn\'t support, as it should, management decisions. This is one of the main arguments why there is the need of an ambient that will provide faster and specific access to information and the data warehouse technology has provide some alternative to speed up and qualify the process to support management decision.

Atualmente, uma das grandes vantagens competitivas que uma empresa possui em relação a seu concorrente é a informação sobre seu cliente. As estratégias de Customer Relationship Management (CRM), propiciam o profundo conhecimento do cliente, para que a empresa possa tratá-lo de forma personalizada e reconhecê-lo como seu principal patrimônio. Segundo TAURION (2000) e DW BRASIL (2001), para suportar essa tecnologia, é necessário que as empresas possuam um repositório de dados históricos de clientes. O Data Warehouse (DW) possui diversas características que utilizam, de forma adequada e eficiente, ferramentas de desenvolvimento de modernos bancos de dados. Através da ferramenta Data Mining (DM), é possível descobrir novas correlações, padrões e tendências entre informações de uma empresa pela extração e análise dos dados do DW. A análise dos dados também pode ser feita através de sistemas On Line Analytical Proccess (OLAP), os quais ajudam analistas a sintetizar informações sobre as empresas, por meio de comparações, visões personalizadas, análise histórica e projeção de dados em vários cenários. Diante deste contexto, parece possível afirmar que o DW, juntamente com o OLAP, podem proporcionar grande suporte à estratégia de CRM. Desta forma, esta pesquisa apresenta como objetivo identificar e analisar as principais contribuições que o DW e suas ferramentas podem dar à estratégia CRM Analítico. / Nowadays, the great competitive advantage that a company possesses in relation to your competitor is the information about its customer. The strategies of Customer Relationship Management (CRM) provide deep knowledge about the customer, so that the company can treat them in a personalized way and it recognizes them as its main patrimony. According to TAURION (2000) and DW BRASIL (2001), to support that technology, it is necessary that the companies possess a repository of customers\' historical data. Data Warehouse (DW) possesses several characteristics that use, in appropriate and efficient way, tools of development of modern databases and, through the too Data Mining (DM) discovers new correlations, pattems and tendencies among information of a company, for the analysis of the data of DW. The analysis of the data can also be made through the systems On Line Analytical Proccess (OLAP), which help analysts and executives to synthesize information on the companies, by means of comparisons, personalized visions, historical analysis and projection of data in several sceneries. In this context, it can be stated that DW and DM can provide great support to the strategy of CRM. Thus, this work presents as objective to identify the main contributions that DW and their tools can give to the strategy of Analytical CRM.

Esta pesquisa tem como objetivo analisar o sistema de informação do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto USP a fim de identificar quais são os problemas que afetam a geração de informações gerenciais no nível estratégico, bem como a elaboração do plano de desenvolvimento e etapas necessárias para a construção de um sistema de informações gerenciais. Trata-se de uma revisão bibliográfica de publicações como: livros, artigos, dissertações de mestrado e teses de doutorado, acerca de Sistemas de Informações, Sistemas de Informações Gerenciais, Sistemas de Informações Hospitalares, Inteligência de Negócios, Gestão em Saúde e Gestão Hospitalar. Numa primeira etapa foi feita a leitura dos resumos dos estudos encontrados, a fim de averiguar a adequação desses estudos aos objetivos deste trabalho. Na segunda, foi feita a leitura integral dos trabalhos que foram selecionados na primeira, onde foi observada a coerência entre proposta, metodologia e resultados de tais trabalhos. Na terceira, foi elaborado um plano de desenvolvimento e etapas necessárias para a construção de um sistema de informações gerenciais para algumas áreas de negócio do hospital. Na última década, pelo menos nas grandes corporações de saúde e em alguns poucos hospitais de menor porte do país este panorama experimentou um crescimento bastante expressivo. O processo de informatização hospitalar é bastante complexo, principalmente em um hospital universitário que além de assistência a saúde trabalha em prol do ensino e pesquisa. Um sistema de informação hospitalar é concebido a fim de atuar e contribuir em três níveis hierárquicos: operacional, gerencial e estratégico. O hospital possui um sistema de informação integrado composto de quatro dezenas de sub-sistemas integrados. Os gastos do hospital, levados por diversos fatores, são cada vez maiores. Seria necessário um incremento no montante dos recursos alocados como também uma racionalidade na aplicação dos mesmos. Os gestores devem conseguir aumentar a produtividade a fim de alcançar maiores níveis de cobertura para satisfazer as necessidades dos usuários dos serviços de saúde. Fica evidente a necessidade de transformar dados em informações para a tomada de decisões gerenciais. Grande parte dos dados que necessitamos está armazenada nos sub-sistemas do HCFMRP, porém transformá-los em informações não é tarefa fácil. Não há nada de errado com estes sistemas e seus bancos de dados operacionais, os mesmos em primeira instância foram criados para dar suporte aos processos da empresa e suas operações, seus dados estão armazenados de maneira pormenorizada, ou seja, nos mínimos detalhes, prejudicando assim outras funções como apoio a tomada de decisão gerencial. Torna-se necessário à existência de um ambiente propício para consultas específicas com acessos rápidos e disponibilidade de informações. A fim de vencer este desafio a tecnologia de Data Warehouse se apresenta como alternativa para simplificar, agilizar e qualificar o processo de apoio à tomada de decisão gerencial. / This research has for objective to analyze the Information System of the Clinical Hospital of the Faculty of Medicine of Ribeirão Preto (HCFMRP-USP) to identify the problems that affects the information generation at the strategic level, as well as the elaboration of a development plan and the phases needed to construct a Management Information System. This researched was based on bibliographic review of books, articles, dissertations, theses, and other source that were related to Information Systems, Management Information Systems, Hospitals Information Systems, Business Intel ligence, Health Management and Hospital Management. This work was divided into phases. The first phase of this work was the reading of the summary of the found material to verify the fitnesses of this study to the main objective of this research. The second was the full reading of the original material selected in the first phase. In this moment was observed the coherence between the proposal, the methods and the results of such works. In the third phase was developed a plan to create a Management Information System for specifics areas in the hospital, and the phases to build it was defined. In this last decade, at least in largest health corporation and in some small hospital of Brazil this scenery experienced an expressive growth. The hospital\'s informatization process is very complex, especially in university hospital in which besides taking care of health problems it has to deal with teaching and research. A hospital information system is design to contribute to three hierarchy levels: operational, management and strategic. The hospital (HCFMRP-USP) has an integrated information system that has for dozen of integrated subsystems. The amount spent by the hospital is increased by different factors. Would be necessary to increase the allocated resources as well as a better rationale to use them. The managers must increase the productivity in order to achieve greater levels of coverage to satisfy the client\'s needs for better healthy services. It is evident the need to transform data in information to support management decisions. Great part of the data that are needed is stored in subsystems at HCFMRP, but to turn them into information isn\'t as simple as it may seem. There is nothing wrong with this system or its operational database. They were developed to support the corporation and its operations, and the data are stored in such structured detailed that doesn\'t support, as it should, management decisions. This is one of the main arguments why there is the need of an ambient that will provide faster and specific access to information and the data warehouse technology has provide some alternative to speed up and qualify the process to support management decision.

Data warehouse system är något som har växt fram under 1990-talet och det har implementeras hos flera verksamheter. De källsystem som en verksamhet har kan integreras ihop med ett data warehouse system för att skapa en version av verkligheten och ta fram rapporter för beslutsunderlag. Med en version av verkligheten menas att det skapas en gemensam bild som visar hur verksamhetens dagliga arbete sker och utgör grundinformation för de framtagna analyserna från data warehouse systemet. Det blir därför väsenligt för verksamheten att de framtagna rapporterna håller en, enligt verksamheten, tillfredställande god datakvalitet. Detta leder till att datakvaliteten hos data warehouse systemet behöver hålla en tillräckligt hög kvalitetsnivå. Om datakvaliteten hos beslutsunderlaget brister kommer verksamheten inte att ta de optimala besluten för verksamheten utan det kan förekomma att beslut tas som annars inte hade tagits. Att förbättra datakvaliteten hos data warehouse systemet blir därför centralt för verksamheten. Med hjälp av kvalitetsfilosofin Total Quality Management, TQM, har verksamheten ett stöd för att kunna förbättra datakvaliteten eftersom det möjliggör att ett helhetsgrepp om kvaliteten kan tas. Anledningen till att ta ett helhetsperspektiv angående datakvaliteten är att orsakerna till bristande datakvalitet inte enbart beror på orsaker inom själva data warehouse systemet utan beror även på andra orsaker. De kvalitetsförbättrande åtgärder som behöver utföras inom verksamheter varierar eftersom de är situationsanpassade beroende på hur verksamheten fungerar även om det finns mer övergripande gemensamma åtgärder. Det som kommuniceras i form av exempelvis rapporter från data warehouse systemet behöver anses av verksamhetens aktörer som förståeligt och trovärdigt. Anledningen till det är att de framtagna beslutunderlagen behöver vara förståliga och trovärdiga för mottagaren av informationen. Om exempelvis det som kommuniceras i form av rapporter innehåller skräptecken bli det svårt för mottagaren att anse informationen som trovärdig och förståelig. Förbättras kvaliteten hos det kommunikativa budskapet, det vill säga om kommunikationskvaliteten förbättras, kommer datakvaliteten hos data warehouse systemet i slutändan också förbättras. Inom uppsatsen har det tagits fram riktlinjer för att kunna förbättra datakvaliteten hos data warehouse system med hjälp av kommunikationskvaliteten samt TQM. Riktlinjernas syfte är att förbättra datakvaliteten genom att förbättra kvaliteten hos det som kommuniceras inom företagets data warehouse system. Det finns olika åtgärder som är situationsanpassade för att förbättra datakvaliteten med hjälp av kommunikationskvalitet. Ett exempel är att införa en möjlighet för mottagaren att få reda på vem som är sändaren av informationsinnehållet hos de framtagna rapporterna. Detta för att mottagaren bör ha möjlighet att kritisera och kontrollera den kommunikativa handlingen med sändaren, som i sin tur har möjlighet att försvara budskapet. Detta leder till att öka trovärdigheten hos den kommunikativa handlingen. Ett annat exempel är att införa inmatningskontroller hos källsystemen för att undvika att aktörer matar in skräptecken som sedan hamnar i data warehouse systemet. Detta leder till att mottagarens förståelse av det som kommuniceras förbättras. / The data warehouse system is something that has grown during the 1990s and has been implemented in many companies. The operative information system that a company has, can be integrated with a data warehouse system to build one version of the reality and take forward the decision basis. This means that a version of the reality creates a common picture that show how the company’s daily work occurs and constitutes the base of information for the created analysis reports from the data warehouse system. It is therefore important for a company that the reports have an acceptable data quality. This leads to that the data quality in the data warehouse system needs to hold an acceptable level of high quality. If data quality at the decision basis falls short, the company will not take the optimal decision for the company. Instead the company will take decision that normally would not have been taken. To improve the data quality in the data warehouse system would therefore be central for the company. With help from a quality philosophy, like TQM, the company have support to improve the data quality since it makes it possible for wholeness about the quality to be taken. The reason to take a holistic perspective about the data quality is because lacking of the data quality not only depends on reasons in the data warehouse system, but also on other reasons. The measurement of the quality improvement which needs to perform in the company depends on the situation on how the company works even in the more overall actions. The communication in form of for example reports from the data warehouse system needs to be understandable and trustworthy for the company’s actors. The reason is that the decision basis needs to be understandable and trustworthy for the receiver of the information. If for example the communication in form of reports contains junk characters it gets difficulty for the receiver of the information to consider if it is trustworthy and understandable. If the quality in the communication message is improving, videlicet that the communication quality improves, the data quality in the data warehouse will also improve in the end. In the thesis has guiding principles been created with the purpose to improve data quality in a data warehouse system with help of communication quality and TQM. Improving the quality in the communication, which is performed at the company’s data warehouse to improve the data quality, does this. There are different measures that are depending on the situations to improve the data quality with help of communication quality. One example is to introduce a possibility for the receiver to get information about who the sender of the information content in the reports is. This is because the receiver needs to have the option to criticize and control the communication acts with the sender, which will have the possibility to defend the message. This leads to a more improved trustworthy in the communication act. Another example is to introduce input controls in the operative system to avoid the actors to feed junk characters that land in the data warehouse system. This leads to that the receivers understanding of the communication improves.

<p>Data warehouse system är något som har växt fram under 1990-talet och det har implementeras hos flera verksamheter. De källsystem som en verksamhet har kan integreras ihop med ett data warehouse system för att skapa en version av verkligheten och ta fram rapporter för beslutsunderlag. Med en version av verkligheten menas att det skapas en gemensam bild som visar hur verksamhetens dagliga arbete sker och utgör grundinformation för de framtagna analyserna från data warehouse systemet. Det blir därför väsenligt för verksamheten att de framtagna rapporterna håller en, enligt verksamheten, tillfredställande god datakvalitet. Detta leder till att datakvaliteten hos data warehouse systemet behöver hålla en tillräckligt hög kvalitetsnivå. Om datakvaliteten hos beslutsunderlaget brister kommer verksamheten inte att ta de optimala besluten för verksamheten utan det kan förekomma att beslut tas som annars inte hade tagits.</p><p>Att förbättra datakvaliteten hos data warehouse systemet blir därför centralt för verksamheten. Med hjälp av kvalitetsfilosofin Total Quality Management, TQM, har verksamheten ett stöd för att kunna förbättra datakvaliteten eftersom det möjliggör att ett helhetsgrepp om kvaliteten kan tas. Anledningen till att ta ett helhetsperspektiv angående datakvaliteten är att orsakerna till bristande datakvalitet inte enbart beror på orsaker inom själva data warehouse systemet utan beror även på andra orsaker. De kvalitetsförbättrande åtgärder som behöver utföras inom verksamheter varierar eftersom de är situationsanpassade beroende på hur verksamheten fungerar även om det finns mer övergripande gemensamma åtgärder.</p><p>Det som kommuniceras i form av exempelvis rapporter från data warehouse systemet behöver anses av verksamhetens aktörer som förståeligt och trovärdigt. Anledningen till det är att de framtagna beslutunderlagen behöver vara förståliga och trovärdiga för mottagaren av informationen. Om exempelvis det som kommuniceras i form av rapporter innehåller skräptecken bli det svårt för mottagaren att anse informationen som trovärdig och förståelig. Förbättras kvaliteten hos det kommunikativa budskapet, det vill säga om kommunikationskvaliteten förbättras, kommer datakvaliteten hos data warehouse systemet i slutändan också förbättras. Inom uppsatsen har det tagits fram riktlinjer för att kunna förbättra datakvaliteten hos data warehouse system med hjälp av kommunikationskvaliteten samt TQM. Riktlinjernas syfte är att förbättra datakvaliteten genom att förbättra kvaliteten hos det som kommuniceras inom företagets data warehouse system.</p><p>Det finns olika åtgärder som är situationsanpassade för att förbättra datakvaliteten med hjälp av kommunikationskvalitet. Ett exempel är att införa en möjlighet för mottagaren att få reda på vem som är sändaren av informationsinnehållet hos de framtagna rapporterna. Detta för att mottagaren bör ha möjlighet att kritisera och kontrollera den kommunikativa handlingen med sändaren, som i sin tur har möjlighet att försvara budskapet. Detta leder till att öka trovärdigheten hos den kommunikativa handlingen. Ett annat exempel är att införa inmatningskontroller hos källsystemen för att undvika att aktörer matar in skräptecken som sedan hamnar i data warehouse systemet. Detta leder till att mottagarens förståelse av det som kommuniceras förbättras.</p> / <p>The data warehouse system is something that has grown during the 1990s and has been implemented in many companies. The operative information system that a company has, can be integrated with a data warehouse system to build one version of the reality and take forward the decision basis. This means that a version of the reality creates a common picture that show how the company’s daily work occurs and constitutes the base of information for the created analysis reports from the data warehouse system. It is therefore important for a company that the reports have an acceptable data quality. This leads to that the data quality in the data warehouse system needs to hold an acceptable level of high quality. If data quality at the decision basis falls short, the company will not take the optimal decision for the company. Instead the company will take decision that normally would not have been taken.</p><p>To improve the data quality in the data warehouse system would therefore be central for the company. With help from a quality philosophy, like TQM, the company have support to improve the data quality since it makes it possible for wholeness about the quality to be taken. The reason to take a holistic perspective about the data quality is because lacking of the data quality not only depends on reasons in the data warehouse system, but also on other reasons. The measurement of the quality improvement which needs to perform in the company depends on the situation on how the company works even in the more overall actions.</p><p>The communication in form of for example reports from the data warehouse system needs to be understandable and trustworthy for the company’s actors. The reason is that the decision basis needs to be understandable and trustworthy for the receiver of the information. If for example the communication in form of reports contains junk characters it gets difficulty for the receiver of the information to consider if it is trustworthy and understandable. If the quality in the communication message is improving, videlicet that the communication quality improves, the data quality in the data warehouse will also improve in the end. In the thesis has guiding principles been created with the purpose to improve data quality in a data warehouse system with help of communication quality and TQM. Improving the quality in the communication, which is performed at the company’s data warehouse to improve the data quality, does this.</p><p>There are different measures that are depending on the situations to improve the data quality with help of communication quality. One example is to introduce a possibility for the receiver to get information about who the sender of the information content in the reports is. This is because the receiver needs to have the option to criticize and control the communication acts with the sender, which will have the possibility to defend the message. This leads to a more improved trustworthy in the communication act. Another example is to introduce input controls in the operative system to avoid the actors to feed junk characters that land in the data warehouse system. This leads to that the receivers understanding of the communication improves.</p>

Made available in DSpace on 2016-06-02T19:07:09Z (GMT). No. of bitstreams: 1 BERBEL_Talita_2015.pdf: 2383674 bytes, checksum: 3c3c42908a145864cffb9aa42b7d45b7 (MD5) Previous issue date: 2015-03-23 / With the rapid growth of unstructured data, such as text documents, it becomes more and more interesting and necessary to extract such information to support decision making in business intelligence systems. Recommendations can be used in the OLAP process, because they allow users to have a particular experience in exploiting data. The process of recommendation, together with the possibility of query personalisation, allows recommendations to be increasingly relevant. The main contribution of this work is to propose an effective solution for semantic recommendation of documents through personalisation of OLAP aggregation queries in a data warehousing environment. In order to aggregate and recommend documents, we propose the use of semantic similarity. Domain ontology and the statistical measure of frequency are used in order to verify the similarity between documents. The threshold of similarity between documents in the recommendation process is adjustable and this is the personalisation that provides to the user an interactive way to improve the relevance of the results. The proposed case study is based on articles from PubMed and its domain ontology in order to create a prototype using real data. The results of the experiments are presented and discussed, showing that good recommendations and aggregations are possible with the suggested approach. The results are discussed on the basis of evaluation measures: precision, recall and F1-measure. / Com o crescimento do volume dos dados não estruturados, como os documentos de texto, torna-se cada vez mais interessante e necessário extrair informações deste tipo de dado para dar suporte à tomada de decisão em sistemas de Business Intelligence. Recomendações podem ser utilizadas no processo OLAP, pois permitem que os usuários tenham uma experiência diferenciada na exploração dos dados. O processo de recomendação, aliado à possibilidade da personalização das consultas dos usuários, tomadores de decisão, permite que as recomendações possam ser cada vez mais relevantes. A principal contribuição deste trabalho é a proposta de uma solução eficaz para a recomendação semântica de documentos mediante a personalização de consultas de agregação OLAP em um ambiente de Data Warehousing. Com o intuito de agregar e recomendar documentos propõe-se a utilização da similaridade semântica. A ontologia de domínio e a medida estatística de frequência são utilizadas com o objetivo de verificar a similaridade entre os documentos. O limiar de similaridade entre os documentos no processo de recomendação pode ser parametrizado e é esta a personalização que oferece ao usuário uma maneira interativa de melhorar a relevância dos resultados obtidos. O estudo de caso proposto se baseia em artigos da PubMed e em sua ontologia de domínio com o propósito de criar um protótipo utilizando dados reais. Os resultados dos experimentos realizados são expostos e analisados, mostrando que boas recomendações e agregações são possíveis utilizando a abordagem sugerida. Os resultados são discutidos com base nas métricas de avaliação: precision, recall e F1-measure.

Atualmente, uma das grandes vantagens competitivas que uma empresa possui em relação a seu concorrente é a informação sobre seu cliente. As estratégias de Customer Relationship Management (CRM), propiciam o profundo conhecimento do cliente, para que a empresa possa tratá-lo de forma personalizada e reconhecê-lo como seu principal patrimônio. Segundo TAURION (2000) e DW BRASIL (2001), para suportar essa tecnologia, é necessário que as empresas possuam um repositório de dados históricos de clientes. O Data Warehouse (DW) possui diversas características que utilizam, de forma adequada e eficiente, ferramentas de desenvolvimento de modernos bancos de dados. Através da ferramenta Data Mining (DM), é possível descobrir novas correlações, padrões e tendências entre informações de uma empresa pela extração e análise dos dados do DW. A análise dos dados também pode ser feita através de sistemas On Line Analytical Proccess (OLAP), os quais ajudam analistas a sintetizar informações sobre as empresas, por meio de comparações, visões personalizadas, análise histórica e projeção de dados em vários cenários. Diante deste contexto, parece possível afirmar que o DW, juntamente com o OLAP, podem proporcionar grande suporte à estratégia de CRM. Desta forma, esta pesquisa apresenta como objetivo identificar e analisar as principais contribuições que o DW e suas ferramentas podem dar à estratégia CRM Analítico. / Nowadays, the great competitive advantage that a company possesses in relation to your competitor is the information about its customer. The strategies of Customer Relationship Management (CRM) provide deep knowledge about the customer, so that the company can treat them in a personalized way and it recognizes them as its main patrimony. According to TAURION (2000) and DW BRASIL (2001), to support that technology, it is necessary that the companies possess a repository of customers\' historical data. Data Warehouse (DW) possesses several characteristics that use, in appropriate and efficient way, tools of development of modern databases and, through the too Data Mining (DM) discovers new correlations, pattems and tendencies among information of a company, for the analysis of the data of DW. The analysis of the data can also be made through the systems On Line Analytical Proccess (OLAP), which help analysts and executives to synthesize information on the companies, by means of comparisons, personalized visions, historical analysis and projection of data in several sceneries. In this context, it can be stated that DW and DM can provide great support to the strategy of CRM. Thus, this work presents as objective to identify the main contributions that DW and their tools can give to the strategy of Analytical CRM.