9781155909783 (3 results)

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Taschenbuch. Condition: Neu. Neuware -Source: Wikipedia. Pages: 103. Chapters: Aggregate (data warehouse), Anchor Modeling, Bill Inmon, Bitemporal Modeling, Business analytics, Dashboard (business), Data extraction, Data mart, Data Vault Modeling, Data warehouse appliance, Data warehouse architectures, Degenerate dimension, Dimensional Fact Model, Dimensional modeling, Dimension (data warehouse), Dimension table, DIRKS, Document warehouse, Early-arriving fact, Enterprise bus matrix, Expense and Cost Recovery System (ECRS), Extract, transform, load, Fact (data warehouse), Fact table, Hub and spokes architecture, Integration warehouse, Log trigger, Master data management, Measure (data warehouse), Noetix, OLAP cube, Operational database, Operational data store, Operational system, Ralph Kimball, Reverse star schema, Slowly changing dimension, Snowflake schema, Spreadmart, Staging (data), The Kimball Lifecycle, Time variance. Excerpt: 176 article summaries including: On issues of data warehouse architectures - managing Australian resources data . Data warehouse architectures; selection factors and success evaluation . Why is the snowflake schema a good data warehouse design . A Data Warehouse Architecture for Clinical Data Warehousing . Migrating an Operational Database Schema to Data Warehouse Schemas . Cloud based Data Warehouse 20 Storage Architecture: An extension to traditional Data Warehousing . Derivations of Initial Data Warehouse Structure by Mapping Operational Database on Transaction Patterns . A full-scale implementation of the NAPP 1880 US Census data set using dimensional modeling and data-warehousing technology . pygrametl: A Powerful Programming Framework for Extract-Transform-Load Programmers . Data Warehousing . Analytical Master Data Management 20 . Data Warehouse . Practical Approach for Master Data Management . Enterprise Master Data Management Trends and Solutions . An ERP-centric Master Data Management Approach . Enterprise Master Data Management Trends and Solutions . Ontological engineering in data warehousing . Text Analytics to Data Warehousing . Recent Developments in Data Warehousing . DATA WAREHOUSING - Conceitos e Modelos . Data warehousing mobile code design . Data warehousing in molecular biology . Justification of Data Warehousing Projects . Clinical Data Warehousing - A Survey . Toward Active XML Data Warehousing . Strategic Business Requirements for Master Data Management Systems . Panoramic and main features of Business Analytics . FEASIBILITY OF INVESTMENT IN BUSINESS ANALYTICS . Towards a business analytics capability maturity model . Attribute oriented induction with star schema . 71 Attribute oriented induction with star schema . Database Vs Data Warehouse . An Automated Data Warehouse . Fundamentos del Data Warehouse . Process Data Warehouse . Data Warehouse Benchmarking . Designing a Data Warehouse . The LCB Data Warehouse . Database Vs Data Warehouse . Spatial Data Warehouse Modelling . Community and data integration approach using requirement centric operational data store model (ReCODS-Model) for business intelligence applications . Data warehousing support for mobile environment . Data Warehousing (DW) - Models and Business Application . Improving data quality in data warehousing applications . A Data Warehousing System for Web Information . HYBRIDJOIN for Near-real-time Data Warehousing . AN XML-BASED DATA WAREHOUSING TOOL . HYBRIDJOIN for near-real-time Data Warehousing . HYBRIDJOIN for Near Real-time Data Wa.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Source: Wikipedia. Pages: 37. Chapters: Data warehouse, Extract, transform, load, Slowly changing dimension, Data warehouse appliance, Master data management, OLAP cube, Anchor Modeling, Dimensional modeling, Data Vault Modeling, Business analytics, Dimensional Fact Model, Snowflake schema, Fact table, Enterprise bus matrix, Data mart, Bill Inmon, Aggregate, Star schema, Ralph Kimball, Spreadmart, Dashboard, The Kimball Lifecycle, Dimension table, Data warehouse architectures, Degenerate dimension, Operational database, Data extraction, SAS Rapid Data Warehouse Methodology, Operational data store, Document warehouse, Time variance, Operational system, Early-arriving fact, Staging, Integration warehouse, Measure, DIRKS. Excerpt: In computing, a data warehouse (DW) is a database used for reporting and analysis. The data stored in the warehouse is uploaded from the operational systems. The data may pass through an operational data store for additional operations before it is used in the DW for reporting. A data warehouse maintains its functions in three layers: staging, integration, and access. Staging is used to store raw data for use by developers. The integration layer is used to integrate data and to have a level of abstraction from users. The access layer is for getting data out for users. This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support (Marakas & O'Brien 2009). However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata. A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to: The concept of data warehousing dates back to the late 1980s when IBM researchers Barry Devlin and Paul Murphy developed the 'business data warehouse'. In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. In the absence of a data warehousing architecture, an enormous amount of redundan 104 pp. Englisch.