Submitted by Lucelia Lucena (lucelia.lucena@ufpe.br) on 2015-03-04T18:33:54Z No. of bitstreams: 2 DISSERTAÇÃO Ana Cecília Vital de Andrade.pdf: 4377501 bytes, checksum: 62069caff1753987554f40017f60aa10 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-04T18:33:54Z (GMT). No. of bitstreams: 2 DISSERTAÇÃO Ana Cecília Vital de Andrade.pdf: 4377501 bytes, checksum: 62069caff1753987554f40017f60aa10 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2014-03-14 / SOLAP é uma tecnologia resultante da integração das ferramentas OLAP (Online Analytical Processing) e SIG (Sistemas de Informação Geográfica), cujo objetivo é proporcionar novas possibilidades de análises, uma vez que permite integrar processamento espacial (SIG) e processamento multidimensional-multinível (OLAP) (e.g., qual foi o total de produtos vendidos por loja, cliente, ano, semestre e mês para os clientes que residem até 1km da matriz?). Embora existam muitos trabalhos relacionados à SOLAP, poucos dão atenção à especificação do cliente SOLAP. Neste contexto, ressalta-se que a maioria dos trabalhos existentes apenas dá suporte à manipulação de uma única dimensão espacial e apresentam poucos (algumas vezes nenhum) detalhes sobre a arquitetura e/ou recursos visuais para especificar e sincronizar graficamente operações OLAP e SIG em um cliente SOLAP. Visando propor uma alternativa ao problema descrito, esta dissertação especifica e implementa uma arquitetura de software e uma interface gráfica para um cliente SOLAP Web, o qual visa especificar graficamente operações SOLAP sobre uma ou mais dimensões espaciais, bem como sobre temas externos ao cubo de dados. Como prova de conceito, foi realizado um exemplo de uso do cliente SOLAP construído, que testa a realização de operações SOLAP com as características citadas.

The subject of the thesis is the proof of concept of Business Intelligence (BI) implementation in the Communications service department from the Kapsch BusinessCom s.r.o. company. The aim of the work is developing a BI proof solution and consequently producing an output for the final users. The main outcome of the thesis is operating BI solutions used in practice. The solution was carried out using tools MS SQL Server 2008 R2 Management Studio, MS SQL Server 2008 R2 Business Intelligence Development studio and MS Excel 2007. The project analyses the data obtained from the ERP (Enterprise Resource Planning) database acquired from the MS Dynamics Navision system. Information on "Voice" projects from the Communications service department, which mainly focuses on the service of PBXs and similar products, are recorded in the project among others. A multidimensional analysis was carried out, data warehouse was designed, data pumps were created, OLAP (On-line Analytical Processing) block and output in the Microsoft Excel 2007 program were carried out during the project.

全民健康保險自實施以來，透過危險分擔與社會互助的原則，降低了民眾就醫時的財務障礙，進而促進了全體國民的健康。可是由於收入成長減緩與支出不斷增加的情形，使得這個制度目前已面臨嚴重的財務危機，然而在目前政治與經濟環境的雙重影響之下，健保收入已無法有效增加，因此努力控制醫療費用支出，便成為當務之急。但是全民健康保險是一項社會福利政策，不能因為控制醫療費用而降低了醫療品質。如何將醫療資源有效分配，以便減少醫療資源浪費、維持醫療品質並減輕醫療費用的支出，便需依賴一個良好的審查制度。 然而對於醫療費用的審查，不管在設計、分析、控管乃至評估上，都是知識密集的工作，而且審查的過程還必須藉由專業審查者的參與始能完成，因此如何善用資訊科技予以適當之輔助，便成為醫務管理上一個非常重要的議題。 本研究使用健保局北區分局感冒等疾病之就醫資料作為樣本，在分析過國內外對醫療費用審查的建議方式後，嘗試設計一個新的自動化審查機制，並發展一套以資料發掘為基礎的自動化審查雛型系統，希望能在醫療院所申報的記錄中找出共同的規則，並利用這些規則自動將有問題的資料篩選出來，幫助健保局與專業審查者將焦點集中在有問題的資料上，以便能更有效率的進行審查的工作。 本研究所得到的結果，經健保局人員與專業審查醫師檢視後，認為確實可行，除了證明資料發掘技術可以有效地應用在醫療費用審查，並帶來可觀的效益之外，還達到有效降低審查人力、提昇審查效率的目的。而對於這種自動篩選出異常的審查方式，應如何實際加以應用，本研究也提出了具體的建議架構及實施步驟，供健保局在未來建立自動化審查制度時的參考依據。

This master's thesis, divided into two sections, compares in part one existing (non-)commercial systems for OLAP presentation using contingency table or graph. The main focus is put on a graph. Results received from my observations in part one are used for implementing a graphic subsystem within internet browser's environment. User friendly interface and good arrangement of displayed data are the most important tasks.

商業智慧是近年來企業邁向成功重要的工具，在競爭劇烈的市場上採用商業智慧來作業其管理效果卓越。 企業除了提高產能、準時交貨率、服務品質、客戶滿意度等競爭優勢等；另外降低不良率、減少應收帳款周轉天數、減低庫存周轉率、重工率、報廢率、退貨率、客戶抱怨、成本….等。 本文就是利用商業智慧 (Business Intelligence)的企業管理儀表板 (EMD – Enterprise Management Dashboard)的一個成功的案例，例如：單一畫面全方位管理、多維度互動式 (Multi-Dimensional)、相關性快速分析、交叉分析 (Slice & Dice)、穿透鑽取 (Drill-Through)、上捲下鑽 (Drill-Down & Drill-Up)及線上分析 (OLAP – On Line Analytical Processing)引擎，提供各級主管即時的資訊作為管理工具，即時自動轉為視覺化資訊 (Visualizer Web) – 協助企業快速透視整體營運狀況，進而採取最佳行動方案快速超過對手，創造出良好的營運績效為企業帶來可觀的收益與利潤。

Most organisations use several data management and business intelligence solutions which are on-premise and, or cloud-based to manage and analyse their constantly growing business data. Challenges faced by organisations nowadays include, but are not limited to growth limitations, big data, inadequate analytics, computing, and data storage capabilities. Although these organisations are able to generate reports and dashboards for decision-making in most cases, effective use of their business data and an appropriate business intelligence solution could achieve and retain informed decision-making and allow competitive reaction to the dynamic external environment. A data management and analytic model has been proposed on which organisations could rely for decisive guidance when planning to procure and implement a unified business intelligence solution. To achieve a sound model, literature was reviewed by extensively studying business intelligence in general, and exploring and developing various deployment models and architectures consisting of naïve, on-premise, and cloud-based which revealed their benefits and challenges. The outcome of the literature review was the development of a hybrid business intelligence model and the accompanying architecture as the main contribution to the study.In order to assess the state of business intelligence utilisation, and to validate and improve the proposed architecture, two case studies targeting users and experts were conducted using quantitative and qualitative approaches. The case studies found and established that a decision to procure and implement a successful business intelligence solution is based on a number of crucial elements, such as, applications, devices, tools, business intelligence services, data management and infrastructure. The findings further recognised that the proposed hybrid architecture is the solution for managing complex organisations with serious data challenges. / Computing / M. Sc. (Computing)

Made available in DSpace on 2016-06-02T19:05:54Z (GMT). No. of bitstreams: 1 3993.pdf: 3294402 bytes, checksum: 982c043143364db53c8a4e2084205995 (MD5) Previous issue date: 2011-08-26 / A data warehousing environment supports the decision-making process through the investigation and analysis of data in an organized and agile way. However, the current data warehousing technologies do not allow that the decision-making processe be carried out based on images pictorial (intrinsic) features. This analysis can not be carried out in a conventional data warehousing because it requires the management of data related to the intrinsic features of the images to perform similarity comparisons. In this work, we propose a new data warehousing environment called iCube to enable the processing of OLAP perceptual similarity queries over images, based on their pictorial (intrinsic) features. Our approach deals with and extends the three main phases of the traditional data warehousing process to allow the use of images as data. For the data integration phase, or ETL phase, we propose a process to represent the image by its intrinsic content (such as color or texture numerical descriptors) and integrate this data with conventional data in the DW. For the dimensional modeling phase, we propose a star schema, called iStar, that stores both the intrinsic and the conventional image data. Moreover, at this stage, our approach models the schema to represent and support the use of different user-defined perceptual layers. For the data analysis phase, we propose an environment in which the OLAP engine uses the image similarity as a query predicate. This environment employs a filter mechanism to speed-up the query execution. The iStar was validated through performance tests for evaluating both the building cost and the cost to process IOLAP queries. The results showed that our approach provided an impressive performance improvement in IOLAP query processing. The performance gain of the iCube over the best related work (i.e. SingleOnion) was up to 98,21%. / Um ambiente de data warehousing (DWing) auxilia seus usuários a tomarem decisões a partir de investigações e análises dos dados de maneira organizada e ágil. Entretanto, os atuais recursos de DWing não possibilitam que o processo de tomada de decisão seja realizado com base em comparações do conteúdo intrínseco de imagens. Esta análise não pode ser realizada por aplicações de DW convencionais porque essa utiliza, como base, imagens digitais e necessita realizar operações baseadas em similaridade, para as quais um DW convencional não oferece suporte. Neste trabalho, é proposto um ambiente de data warehouse chamado iCube que provê suporte ao processamento de consultas IOLAP (Image On-Line Analytical Processing) baseadas em diversas percepções de similaridade entre as imagens. O iCube realiza adaptações nas três principais fases de um ambiente de data warehousing convencional para permitir o uso de imagens como dados de um data warehouse (DW). Para a fase de integração, ou fase ETL (Extract, Trasnform and Load), nós propomos um processo para representar as imagens a partir de seu conteúdo intrínseco (i.e., por exemplo por meio de descritores numéricos que representam cor ou textura dessas imagens) e integrar esse conteúdo intrínseco a dados convencionais em um DW. Neste trabalho, nós também propomos um esquema estrela otimizado para o iCube, denominado iStar, que armazena tanto dados convencionais quanto dados de representação do conteúdo intrínseco das imagens. Ademais, nesta fase, o iStar foi projetado para representar e prover suporte ao uso de diferentes camadas perceptuais definidas pelo usuário. Para a fase de análise de dados, o iCube permite que processos OLAP sejam executados com o uso de comparações de similaridade como predicado de consultas e com o uso de mecanismos de filtragem para acelerar o processamento de consultas OLAP. O iCube foi validado a partir de testes de desempenho para a construção da estrutura e para o processamento de consultas IOLAP. Os resultados demonstraram que o iCube melhora significativamente o desempenho no processamento de consultas IOLAP quando comparado aos atuais recursos de IDWing. Os ganhos de desempenho do iCube contra o melhor trabalho correlato (i.e. SingleOnion) foram de até 98,21%.

The main goal of this thesis is to summarize and introduce general theoretical concepts of Data Warehousing by using the systems approach. The thesis defines Data Warehousing and its main areas and delimitates Data Warehousing area in terms of higher-level area called Business Intelligence. It also describes the history of Data Warehousing & Business Intelligence, focuses on key principals of Data Warehouse building and explains the practical applications of this solution. The aim of the practical part is to perform the evaluation of theoretical concepts. Based on that, design and build Data Warehouse in environment of an existing company. The final solution shall include Data Warehouse design, hardware and software platform selection, loading with real data by using ETL services and building of end users reports. The objective of the practical part is also to demonstrate the power of this technology and shall contribute to business decision-making process in this company.