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Measure (data Warehouse)
In a data warehouse, a measure is a property on which calculations (e.g., sum, count, average, minimum, maximum) can be made. A measure can either be categorical, algebraic or holistic. Example For example, if a retail store sold a specific product, the quantity and prices of each item sold could be added or averaged to find the total number of items sold or the total or average price of the goods sold. Use of ISO representation terms When entering data into a metadata registry such as ISO/IEC 11179, representation terms such as number, value and measure are typically used as measures. See also * Data warehouse * Dimension (data warehouse) A dimension is a structure that categorizes Fact (data warehouse), facts and Measure (data warehouse), measures in order to enable users to answer business questions. Commonly used dimensions are people, products, place and time. (Note: People an ... References * Kimball, Ralph et al. (1998); ''The Data Warehouse Lifecycle Toolkit'', p17. P ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Warehouse
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for Business intelligence, reporting and data analysis and is a core component of business intelligence. Data warehouses are central Repository (version control), repositories of data integrated from disparate sources. They store current and historical data organized in a way that is optimized for data analysis, generation of reports, and developing insights across the integrated data. They are intended to be used by analysts and managers to help make organizational decisions. The data stored in the warehouse is uploaded from operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the data warehouse for reporting. The two main workflows for building a data warehouse system are extract, transform, load (ETL) and extract, load, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
ISO/IEC 11179
The ISO/IEC 11179 metadata registry (MDR) standard is an international International Organization for Standardization, ISO/International Electrotechnical Commission, IEC standard for representing metadata for an organization in a metadata registry. It documents the standardization and registration of metadata to make data understandable and shareable. Structure of an ISO/IEC 11179 metadata registry The ISO/IEC 11179 model is a result of two principles of semantic theory, combined with basic principles of data modelling. The first principle from semantic theory is the thesaurus type relation between wider and more narrow (or specific) concepts, e.g. the wide concept "income" has a relation to the more narrow concept "net income". The second principle from semantic theory is the relation between a concept and its representation, e.g., "buy" and "purchase" are the same concept although different terms are used. A basic principle of data modelling is the combination of an object c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Representation Term
A representation term is a word, or a combination of words, that semantically represent the data type (value domain) of a data element. A representation term is commonly referred to as a ''class word'' by those familiar with data dictionaries. ISO/IEC 11179-5:2005 defines ''representation term'' as a ''designation of an instance of a representation class'' As used in ISO/IEC 11179, the representation term is that part of a data element name that provides a semantic pointer to the underlying data type. A '' Representation class'' is a class of representations. This ''representation class'' provides a way to classify or group data elements. A ''Representation Term'' may be thought of as an attribute of a data element in a metadata registry that classifies the data element according to the type of data stored in the data element. Representation terms are typically "approved" by the organization or standards body using them. For example, the UN publishes its approved list as part ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Warehouse
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for Business intelligence, reporting and data analysis and is a core component of business intelligence. Data warehouses are central Repository (version control), repositories of data integrated from disparate sources. They store current and historical data organized in a way that is optimized for data analysis, generation of reports, and developing insights across the integrated data. They are intended to be used by analysts and managers to help make organizational decisions. The data stored in the warehouse is uploaded from operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the data warehouse for reporting. The two main workflows for building a data warehouse system are extract, transform, load (ETL) and extract, load, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dimension (data Warehouse)
A dimension is a structure that categorizes Fact (data warehouse), facts and Measure (data warehouse), measures in order to enable users to answer business questions. Commonly used dimensions are people, products, place and time. (Note: People and time sometimes are not modeled as dimensions.) In a data warehouse, dimensions provide structured labeling information to otherwise unordered numeric measures. The dimension is a data set composed of individual, non-overlapping data elements. The primary functions of dimensions are threefold: to provide filtering, grouping and labelling. These functions are often described as ":wiktionary:slice and dice, slice and dice". A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product. A dimensional data element is similar to a categorical v ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ralph Kimball
Ralph Kimball (born July 18, 1944) is an author on the subject of data warehousing and business intelligence. He is one of the original architects of data warehousing and is known for long-term convictions that data warehouses must be designed to be understandable and fast. His bottom-up methodology, also known as dimensional modeling or the Kimball methodology, is one of the two main data warehousing methodologies alongside Bill Inmon. He is the principal author of the best-selling books ''The Data Warehouse Toolkit'' (1996), ''The Data Warehouse Lifecycle Toolkit'' (1998), ''The Data Warehouse ETL Toolkit'' (2004) and ''The Kimball Group Reader'' (2015), published by Wiley and Sons. Career After receiving a Ph.D. in 1973 from Stanford University in electrical engineering (specializing in man-machine systems), Ralph joined the Xerox Palo Alto Research Center (PARC). At PARC Ralph was a principal designer of the Xerox Star Workstation, the first commercial product to use mice ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Warehousing
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for Business intelligence, reporting and data analysis and is a core component of business intelligence. Data warehouses are central Repository (version control), repositories of data integrated from disparate sources. They store current and historical data organized in a way that is optimized for data analysis, generation of reports, and developing insights across the integrated data. They are intended to be used by analysts and managers to help make organizational decisions. The data stored in the warehouse is uploaded from operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the data warehouse for reporting. The two main workflows for building a data warehouse system are extract, transform, load (ETL) and extract, load, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
