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  • With RFID, the System is the System
    . To use the data, it has to be communicated to and integrated into appropriate back-end software systems and these systems need to be integrated with each other. Collecting receiving data may help improve warehouse systems and inventory accuracy, but unless this data is also linked to order entry
  • Backtalk 8/20/2009
    that best fit the Asian population by using the ergonomic data collected from the Chinese population. These newly designed masks can well protect the Chinese from pollution and viruses like H1N1 in an innovative and fashionable way. " A new Web search engine for car parts, , makes it easy to find

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  • Data Warehouse Systems
    Regarding data warehouse design , we make a clear distinction between the three abstraction levels of the American National Standards Institute (ANSI) database architecture, that is, conceptual, logical, and physical, unlike the usual approaches, which do not distinguish clearly between the conceptual and …
  • Data Warehousing and Knowledge Discovery
    The top- ics included data warehouse modeling, spatial data warehouses, mining social networks and graphs, physical data warehouse design , dependency mining, busi- ness intelligence and analytics, outlier and image mining, pattern mining, and data cleaning and variable selection.
  • Encyclopedia of Database Systems
    The active data warehouse design extends the technol- ogy behind active database systems.
  • Oracle Warehouse Builder 11g: Getting Started
    various tasks, including data warehouse design and implementation, administration, .
  • Pro SQL Server 2012 BI Solutions
    I’ve seen a lot of students struggle with topics such as data warehouse design , ETL processing, or OLAP cubes.
  • DataWarehousing and Knowledge Discovery
    Data Warehouse Design .
  • Microsoft SQL Server 2014 Business Intelligence Development Beginner’s Guide
    Chapter 1: Data Warehouse Design .
  • Expert Cube Development with SSAS Multidimensional Models
    In this chapter, we will touch on just about every aspect of data warehouse design , and mention several subjects that cannot be analyzed in depth in a single chapter.
  • Data Warehousing and Knowledge Discovery
    The topics included data warehouse modeling, spatial data warehouses, mining social net- works and graphs, physical data warehouse design , dependency mining, business intelligence and analytics, outlier and image mining, pattern mining, and data cleaning and variable selection.
  • Advances in Conceptual Modeling. Recent Developments and New Directions
    Current approaches for data warehouse design advocate to start the development by defining a conceptual model in order to describe real-world situations by using MD structures [13].
  • Data Warehousing and Knowledge Discovery
    The topics included data warehouse modeling, spatial data warehouses, mining social networks and graphs, physical data warehouse design , dependency mining, business intelli- gence and analytics, outlier and image mining, pattern mining, and data cleaning and variable selection.
  • Database and Expert Systems Applications
    This section then discusses multi-granular data storage, both at the detail and the aggregated levels, according to the needs of the application domain and highlights the inappropriateness of the standard data warehousing design approaches to handle multi-granular data.
  • Journal on Data Semantics XIII
    The main purpose of DLs is to provide a formal theory that can be used to validate conceptual schemata (Franconi & Ng, 2000) of heterogeneous databases (Mena et al., 2000), data warehousing design and multidimensional aggregation modelling (Baader & Sattler, 2003).
  • Data Warehousing and Knowledge Discovery
    Golfarelli, M., Rizzi, S.: Methodological framework for data warehouse design .