48,986 research outputs found

    Design Data Warehouse for Medical Data

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    Organizing and managing the database relations in term of data warehouse technology has been addressed widely in different complex environments. The data warehouse contains a source of valuable data mining. The data contained in the data warehouse is cleaned, integrated, and organized. This study highlighted the existing issues on the medical databases which present a huge number of information across various departments, managing this type of data require time, and laborious tasks to separately access and integrate reliably. Hence, this study aimed to model new medical data warehouse architecture for managing and organizing the medical dataset operation into data warehouse. Technically OLAP has been used to design the proposed architecture, for the hospitable administrators, and top manager and/or sophisticated user can use MDW by using Microsoft SQL Server 2005. Building the proposed architecture adopted by using Microsoft Visual Studio for performing the OLE database operations. The performing process has been tested through the using of use test case technique

    Improving the Data Warehouse Architecture Using Design Patterns

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    Data warehousing is an important part of the enterprise information system. Business intelligence (BI) relies on data warehouses to improve business performance. Data quality plays a key role in BI. Source data is extracted, transformed, and loaded (ETL) into the data warehouses periodically. The ETL operations have the most crucial impact on the data quality of the data warehouse. ETL-related data warehouse architectures including structure-oriented layer architectures and enterprise-view data mart architecture were studied in the literature. Existing architectures have the layer and data mart components but do not make use of design patterns; thus, those approaches are inefficient and pose potential problems. This paper relays how to use design patterns to improve data warehouse architectures

    A Horizontal Fragmentation Algorithm for the Fact Relation in a Distributed Data Warehouse

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    Data warehousing is one of the major research topics of appliedside database investigators. Most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data are based. Unfortunately, this approach is very expensive and tends to ignore the advantages realized during the past decade in the area of distribution and support for data localization in a geographically dispersed corporate structure. This research investigates building distributed data warehouses with particular emphasis placed on distribution design for the data warehouse environment. The article provides an architectural model for a distributed data warehouse, the formal definition of the relational data model for data warehouse and a methodology for distributed data warehouse design along with a "horizontal" fragmentation algorithm for the fact relation. Most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data is based. The centralized data warehouse is very expensive and tends to ignore the advantages realized during the past decade in the areas of distribution and support for data localization in a geographically dispersed corporate structure. Further, it would be unwise to enforce a centralized data warehouse when the operational systems exist over a widely distributed geographical area. The distributed data warehouse supports the decision makers by providing a single view of data even though that data are physically distributed across multiple data warehouses in multiple systems at different branches. Currently, the field of distributed data warehouse in terms of architecture and design is considered an important research problem that needs investigation. This research contributes to the problem of distributed data warehouse architecture and design by: Keywords distributed data warehouse architecture, distributed data warehouse design, horizontal fragmentation. Extending the preliminary architecture model that has been presented in [8] by proposing a distributed data warehouse system architecture and describing the functionality of its components

    A Data Warehouse Architecture Model for Al-Quds Open University

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    Data warehousing is important, not only in business enterprises, but in the university environment as well. The goal of a data warehouse is to integrate timely, accurate information and to make it available to an organization's employees and decision makers. The data warehouse is developing in response to increasing data and information requirements. The traditional notion of data warehouses is evolving into a federated warehouse augmented by a set of processes and services to support integrated and consistent access to heterogeneous, decentralized warehouse systems. This study will explore the design and implementation of a data warehouse architecture model for the Al-Quds Open University (QOU) in Palestine, within the Context of Relational Online Analytical Processing (OLAP). The model aims at integrating data from different sources in the QOU

    ANALYSIS AND DESIGN OF DATA WAREHOUSE BASED ON SNDIKTI USING DATA WAREHOUSE LIFE CYCLE METHOD AT UNSOED ENGINEERING FACULTY

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    The National Standard for Higher Education (Standar Nasional Pendidikan Tinggi or SNDIKTI) is a standard unit that includes the National Education Standards, plus the Research Standards, and Community Service Standards. Based on the policy in the Regulation of the Minister of Education and Culture (Peraturan Menteri Pendidikan dan Kebudayaan or Permendikbud) No. 3 of 2020 regarding SNDIKTI, Jenderal Soedirman University (Unsoed) Architecture needs to prepare data/ information for analysis based on SNDIKTI 2020. The data is obtained from several internal systems and manual recapitulation within a predetermined period of time. Currently the architecture already has many information systems, but it is still difficult to manage the existing data. So it is important for the architecture to have an important repository to minimize human errors and data inconsistencies. So to perform complex data management, a data warehouse is needed. In this study, the data warehouse design uses the Data Warehouse Life Cycle (DWLC) method with the data warehouse model used is the star schema model. The results of the data warehousing process are in the form of a dashboard, Online Analytical Processing (OLAP), and reports intended for the executives of the Unsoed Faculty of Engineering. The output is presented using a BI (Business Intelligence) tool called Knowage

    Perencanaan Arsitektur Teknologi Pada PT. X

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    PT. X has grown to be a company of papermaking which has several division to run enterprise business processes. Nowadays information system that support enterprise business processes have inadequacy due to fully unintegrated. That information system only support accountancy, whereas another business process mostly use non electronic. Based on that reason, therefore company need analysis and information system design which is enterprise architecture planning framework. Furthermore, exploring company business processes need analysis of business model and business strategy. Thereby evaluating the condition of information technology, the problems faced and the need of information technology in the future.As a result, the design of data architecture, application architecture and technology architecture are made. The design of data architecture for company consist of several sub system. Moreover, the major applications from design of applications architecture are human resource information system, sales and marketing information system, logistic information system, manufacture information system, warehouse and quality control information system, purchasing information system, accounting and finance information system. Technology architecture purpose use client/server architecture pattern and service oriented architecture data services

    Formal design of data warehouse and OLAP systems : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems at Massey University, Palmerston North, New Zealand

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    A data warehouse is a single data store, where data from multiple data sources is integrated for online business analytical processing (OLAP) of an entire organisation. The rationale being single and integrated is to ensure a consistent view of the organisational business performance independent from different angels of business perspectives. Due to its wide coverage of subjects, data warehouse design is a highly complex, lengthy and error-prone process. Furthermore, the business analytical tasks change over time, which results in changes in the requirements for the OLAP systems. Thus, data warehouse and OLAP systems are rather dynamic and the design process is continuous. In this thesis, we propose a method that is integrated, formal and application-tailored to overcome the complexity problem, deal with the system dynamics, improve the quality of the system and the chance of success. Our method comprises three important parts: the general ASMs method with types, the application tailored design framework for data warehouse and OLAP, and the schema integration method with a set of provably correct refinement rules. By using the ASM method, we are able to model both data and operations in a uniform conceptual framework, which enables us to design an integrated approach for data warehouse and OLAP design. The freedom given by the ASM method allows us to model the system at an abstract level that is easy to understand for both users and designers. More specifically, the language allows us to use the terms from the user domain not biased by the terms used in computer systems. The pseudo-code like transition rules, which gives the simplest form of operational semantics in ASMs, give the closeness to programming languages for designers to understand. Furthermore, these rules are rooted in mathematics to assist in improving the quality of the system design. By extending the ASMs with types, the modelling language is tailored for data warehouse with the terms that are well developed for data-intensive applications, which makes it easy to model the schema evolution as refinements in the dynamic data warehouse design. By providing the application-tailored design framework, we break down the design complexity by business processes (also called subjects in data warehousing) and design concerns. By designing the data warehouse by subjects, our method resembles Kimball's "bottom-up" approach. However, with the schema integration method, our method resolves the stovepipe issue of the approach. By building up a data warehouse iteratively in an integrated framework, our method not only results in an integrated data warehouse, but also resolves the issues of complexity and delayed ROI (Return On Investment) in Inmon's "top-down" approach. By dealing with the user change requests in the same way as new subjects, and modelling data and operations explicitly in a three-tier architecture, namely the data sources, the data warehouse and the OLAP (online Analytical Processing), our method facilitates dynamic design with system integrity. By introducing a notion of refinement specific to schema evolution, namely schema refinement, for capturing the notion of schema dominance in schema integration, we are able to build a set of correctness-proven refinement rules. By providing the set of refinement rules, we simplify the designers's work in correctness design verification. Nevertheless, we do not aim for a complete set due to the fact that there are many different ways for schema integration, and neither a prescribed way of integration to allow designer favored design. Furthermore, given its °exibility in the process, our method can be extended for new emerging design issues easily

    High-level Architecture For a Digital Oilfield: Features of the Transition to Data-driven Decision Management

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    This paper is devoted to the design of a distributed heterogeneous data warehouse of a digital oilfield. With increasing amounts of data collected from intelligent controllers and sensors, the lack of mechanisms for combining data from different sources and providing them to consumers affect the overall management efficiency. In addition, without it is impossible making the next logical step - the effective application of intelligent analysis methods. The paper describes the high-level architecture, as well as subsystems of operational management and decision support. The presented data are intermediate results of the project "Digital oilfield heterogeneous distributed data warehouse for informational support of decision-making processes"
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