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OLAP
In computing, online analytical processing (OLAP) (), is an approach to quickly answer multi-dimensional analytical (MDA) queries. The term ''OLAP'' was created as a slight modification of the traditional database term online transaction processing (OLTP). OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include Financial reporting, business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture. OLAP tools enable users to analyse multidimensional data interactively from multiple perspectives. OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing.O'Brien, J. A., & Marakas, G. M. (2009). Management information systems (9th ed.). Boston, MA: McGr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
OLAP Cube
An OLAP cube is a multi-dimensional array of data. Online analytical processing (OLAP) is a computer-based technique of analyzing data to look for insights. The term ''cube'' here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than three. Terminology A cube can be considered a multi-dimensional generalization of a two- or three-dimensional spreadsheet. For example, a company might wish to summarize financial data by product, by time-period, and by city to compare actual and budget expenses. Product, time, city and scenario (actual and budget) are the data's dimensions. ''Cube'' is a shorthand for ''multidimensional dataset'', given that data can have an arbitrary number of ''Dimension (data warehouse), dimensions''. The term hypercube is sometimes used, especially for data with more than three dimensions. A cube is not a "cube" in the strict mathematical sense, as the sides are not all necessarily equal. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Aggregate Function
In database management, an aggregate function or aggregation function is a function where multiple values are processed together to form a single summary statistic. Common aggregate functions include: * Average (i.e., arithmetic mean) * Count * Maximum * Median * Minimum * Mode * Range * Sum Others include: * Nanmean (mean ignoring NaN values, also known as "nil" or "null") * Stddev Formally, an aggregate function takes as input a set, a multiset (bag), or a list from some input domain and outputs an element of an output domain . The input and output domains may be the same, such as for SUM, or may be different, such as for COUNT. Aggregate functions occur commonly in numerous programming languages, in spreadsheets, and in relational algebra. The listagg function, as defined in the SQL:2016 standard aggregates data from multiple rows into a single concatenated string. In the entity relationship diagram, aggregation is represented as seen in Figure 1 with a rectang ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pivot Table
A pivot table is a table of values which are aggregations of groups of individual values from a more extensive table (such as from a database, spreadsheet, or business intelligence program) within one or more discrete categories. The aggregations or summaries of the groups of the individual terms might include sums, averages, counts, or other statistics. A pivot table is the outcome of the statistical processing of tabularized raw data and can be used for decision-making. Although ''pivot table'' is a generic term, Microsoft held a trademark on the term in the United States from 1994 to 2020. History In their book ''Pivot Table Data Crunching'', Bill Jelen and Mike Alexander refer to Pito Salas as the "father of pivot tables". While working on a concept for a new program that would eventually become Lotus Improv, Salas noted that spreadsheets have patterns of data. A tool that could help the user recognize these patterns would help to build advanced data models quickly. With ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Online Transaction Processing
Online transaction processing (OLTP) is a type of database system used in transaction-oriented applications, such as many operational systems. "Online" refers to the fact that such systems are expected to respond to user requests and process them in real-time (process transactions). The term is contrasted with online analytical processing (OLAP) which instead focuses on data analysis (for example planning and management systems). Meaning of the term transaction The term "transaction" can have two different meanings, both of which might apply: in the realm of computers or database transactions it denotes an atomic change of state, whereas in the realm of business or finance, the term typically denotes an exchange of economic entities (as used by, e.g., Transaction Processing Performance Council or commercial transactions.) OLTP may use transactions of the first type to record transactions of the second type. Compared to OLAP OLTP is typically contrasted to online analytical p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Star Schema
In computing, the star schema or star model is the simplest style of data mart Logical schema, schema and is the approach most widely used to develop data warehouses and dimensional data marts. The star schema consists of one or more fact tables referencing any number of Dimension (data warehouse), dimension tables. The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries. The star schema gets its name from the Physical data model, physical model'sC J Date, "An Introduction to Database Systems (Eighth Edition)", p. 708 resemblance to a Star polygon, star shape with a fact table at its center and the dimension tables surrounding it representing the star's points. Model The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes related to fact data. Examples of fact data include sales price, sale quantity, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Snowflake Schema
In computing, a snowflake schema or snowflake model is a Logical schema, logical arrangement of tables in a multidimensional database such that the Entity-relationship model, entity relationship diagram resembles a snowflake shape. The snowflake schema is represented by centralized fact tables which are connected to multiple Dimension (data warehouse), dimensions. "Snowflaking" is a method of normalizing the dimension tables in a star schema. When it is completely normalized along all the dimension tables, the resultant structure resembles a snowflake with the fact table in the middle. The principle behind snowflaking is normalization of the dimension tables by removing low cardinality attributes and forming separate tables. The snowflake schema is similar to the star schema. However, in the snowflake schema, dimensions are Normalization (database), normalized into multiple related tables, whereas the star schema's dimensions are denormalized with each dimension represented by ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Business Intelligence
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. Common functions of BI technologies include Financial reporting, reporting, online analytical processing, analytics, Dashboard (business), dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, Predictive Analysis, predictive analytics, and prescriptive analytics. BI tools can handle large amounts of structured and sometimes unstructured data to help organizations identify, develop, and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights is assumed to potentially provide businesses with a competitive market advantage and long-term stability, and help them take strategic decisions. Busine ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Relational Database
A relational database (RDB) is a database based on the relational model of data, as proposed by E. F. Codd in 1970. A Relational Database Management System (RDBMS) is a type of database management system that stores data in a structured format using rows and columns. Many relational database systems are equipped with the option of using SQL (Structured Query Language) for querying and updating the database. History The concept of relational database was defined by E. F. Codd at IBM in 1970. Codd introduced the term ''relational'' in his research paper "A Relational Model of Data for Large Shared Data Banks". In this paper and later papers, he defined what he meant by ''relation''. One well-known definition of what constitutes a relational database system is composed of Codd's 12 rules. However, no commercial implementations of the relational model conform to all of Codd's rules, so the term has gradually come to describe a broader class of database systems, which at a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multi-dimensional Analytical
In statistics, econometrics and related fields, multidimensional analysis (MDA) is a data analysis process that groups data into two categories: data dimensions and measurements. For example, a data set consisting of the number of wins for a single football team at each of several years is a single-dimensional (in this case, longitudinal) data set. A data set consisting of the number of wins for several football teams in a single year is also a single-dimensional (in this case, cross-sectional) data set. A data set consisting of the number of wins for several football teams over several years is a two-dimensional data set. Higher dimensions In many disciplines, two-dimensional data sets are also called panel data. While, strictly speaking, two- and higher-dimensional data sets are "multi-dimensional", the term "multidimensional" tends to be applied only to data sets with three or more dimensions. For example, some forecast data sets provide forecasts for multiple target periods, co ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dimension Table
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 orga ... [...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] |
