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Fact Table
In data warehousing, a fact table consists of the measurements, metrics or facts of a business process. It is located at the center of a star schema or a snowflake schema surrounded by dimension tables. Where multiple fact tables are used, these are arranged as a fact constellation schema. A fact table typically has two types of columns: those that contain facts and those that are a foreign key to dimension tables. The primary key of a fact table is usually a composite key that is made up of all of its foreign keys. Fact tables contain the content of the data warehouse and store different types of measures like additive, non-additive, and semi-additive measures. Fact tables provide the (usually) additive values that act as independent variables by which dimensional attributes are analyzed. Fact tables are often defined by their ''grain''. The grain of a fact table represents the most atomic level by which the facts may be defined. The grain of a sales fact table might be stated ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Aggregate (data Warehouse)
Aggregates are used in dimensional models of the data warehouse to produce positive effects on the time it takes to query large sets of data. At the simplest form an aggregate is a simple summary table that can be derived by performing a '' Group by'' SQL query. A more common use of aggregates is to take a dimension and change the granularity of this dimension. When changing the granularity of the dimension the fact table has to be partially summarized to fit the new grain of the new dimension, thus creating new dimensional and fact tables, fitting this new level of grain. Aggregates are sometimes referred to as pre-calculated summary data, since aggregations are usually precomputed, partially summarized data, that are stored in new aggregated tables. When facts are aggregated, it is either done by eliminating dimensionality or by associating the facts with a rolled up dimension. Rolled up dimensions should be shrunken versions of the dimensions associated with the granular base ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dimension (data Warehouse)
A dimension is a structure that categorizes facts and 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 " 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 variable in statistics. Typically dimensions in a data warehouse are organi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Timestamps
A timestamp is a sequence of characters or encoded information identifying when a certain event occurred, usually giving date and time of day, sometimes accurate to a small fraction of a second. Timestamps do not have to be based on some absolute notion of time, however. They can have any epoch, can be relative to any arbitrary time, such as the power-on time of a system, or to some arbitrary time in the past. The term "timestamp" derives from rubber stamps used in offices to stamp the current date, and sometimes time, in ink on paper documents, to record when the document was received. Common examples of this type of timestamp are a postmark on a letter or the "in" and "out" times on a time card. In modern times usage of the term has expanded to refer to digital date and time information attached to digital data. For example, computer files contain timestamps that tell when the file was last modified, and digital cameras add timestamps to the pictures they take, recording t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Junction Table
An associative entity is a term used in relational and entity–relationship theory. A relational database requires the implementation of a base relation (or base table) to resolve many-to-many relationships. A base relation representing this kind of entity is called, informally, an associative table. As mentioned above, associative entities are implemented in a database structure using associative tables, which are tables that can contain references to columns from the same or different database tables within the same database. An associative (or junction) table maps two or more tables together by referencing the primary keys (PK) of each data table. In effect, it contains a number of foreign keys (FK), each in a many-to-one relationship from the junction table to the individual data tables. The PK of the associative table is typically composed of the FK columns themselves. Associative tables are colloquially known under many names, including association table, bridge ta ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Surrogate Key
A surrogate key (or synthetic key, pseudokey, entity identifier, factless key, or technical key) in a database is a unique identifier for either an ''entity'' in the modeled world or an ''object'' in the database. The surrogate key is ''not'' derived from application data, unlike a ''natural'' (or ''business'') key. Definition There are at least two definitions of a surrogate: ; Surrogate (1) – Hall, Owlett and Todd (1976): A surrogate represents an ''entity'' in the outside world. The surrogate is internally generated by the system but is nevertheless visible to the user or application. ; Surrogate (2) – Wieringa and De Jonge (1991): A surrogate represents an ''object'' in the database itself. The surrogate is internally generated by the system and is invisible to the user or application. The ''Surrogate (1)'' definition relates to a data model rather than a storage model and is used throughout this article. See Date (1998). An important distinction between a s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dimension (data Warehouse)
A dimension is a structure that categorizes facts and 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 " 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 variable in statistics. Typically dimensions in a data warehouse are organi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Time Series
In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. A time series is very frequently plotted via a run chart (which is a temporal line chart). Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements. Time series ''analysis'' comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series ''fore ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Raw Fact
Raw is an adjective usually describing: * Raw materials, basic materials from which products are manufactured or made * Raw food, uncooked food Raw or RAW may also refer to: Computing and electronics * .RAW, a proprietary mass spectrometry data format * Raw audio format, a file type used to represent sound in uncompressed form * Raw image format, a variety of image files used by digital cameras, containing unprocessed data * Rawdisk, binary level disk access * Read after write, technologies used for CD-R and CD-RW * Read after write (RAW) hazard, a data dependency hazard considered in microprocessor architecture * Raw display, a raw framed monitor. Film and television * Raw TV, a British TV production company * ''Raw'' (film), a 2016 film * ''Raw'' (TV series), an Irish drama series * '' Eddie Murphy Raw'', a 1987 live stand-up comedy recording * '' Ramones: Raw'', a 2004 music documentary * ''Raw FM'', an Australian television series * ''WWE Raw'', a weekly World Wrestl ... [...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 reporting and data analysis and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise. The data stored in the warehouse is uploaded from the 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 DW for reporting. Extract, transform, load (ETL) and extract, load, transform (ELT) are the two main approaches used to build a data warehouse system. ETL-based data warehousing The typical extract, transform, load (ETL)-based data warehouse uses staging, data integration, and access layers ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Foreign Key
A foreign key is a set of attributes in a table that refers to the primary key of another table. The foreign key links these two tables. Another way to put it: In the context of relational databases, a foreign key is a set of attributes subject to a certain kind of inclusion dependency constraints, specifically a constraint that the tuples consisting of the foreign key attributes in one relation, R, must also exist in some other (not necessarily distinct) relation, S, and furthermore that those attributes must also be a candidate key in S. In simpler words, a foreign key is a set of attributes that ''references'' a candidate key. For example, a table called TEAM may have an attribute, MEMBER_NAME, which is a foreign key referencing a candidate key, PERSON_NAME, in the PERSON table. Since MEMBER_NAME is a foreign key, any value existing as the name of a member in TEAM must also exist as a person's name in the PERSON table; in other words, every member of a TEAM is also a PERSON. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fact Constellation Schema
Fact constellation is a measure of online analytical processing, which is a collection of multiple fact tables sharing dimension tables, viewed as a collection of stars. It can be seen as an extension of the star schema. A fact constellation schema has multiple fact tables. It is also known as galaxy schema. It is widely used schema and more complex than star schema and snowflake schema. It is possible to create fact constellation schema by splitting original star schema into more star schema. It has many fact tables and some common dimension table. See also * Online analytical processing Online analytical processing, or OLAP (), is an approach to answer multi-dimensional analytical (MDA) queries swiftly in computing. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, repor ... * Star schema * Snowflake schema References {{Reflist Online analytical processing ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
