This article has multiple issues. Please help improve it or discuss these issues on the talk page . (Learn how and when to remove these template messages)

Thomsen Diagrams are the diagrammatic methodology developed by Erik Thomsen in 1997^{ [1] } is essentially a metaphor for describing multidimensional data spaces in the OLAP system.^{ [2] } It may be thought of as a multidimensional domain structure. In the structure, each dimension is represented by a vertical line, and hence each dimension is described independently.
A diagram is a symbolic representation of information according to visualization technique. Diagrams have been used since ancient times, but became more prevalent during the Enlightenment. Sometimes, the technique uses a threedimensional visualization which is then projected onto a twodimensional surface. The word graph is sometimes used as a synonym for diagram.
Every member of a dimension is represented by a unit interval on the line. A multidimensional model is built by combining the resultant lines for the particular dimensions.
The Thomsen diagrammatical technique is not based on angular defined dimensions, and is thus able to represent any number of dimensions. It may be referred to as a multidimensional type structure^{ [3] } (MTS). The MTS permits the viewing of information about hierarchies and data flows, both within and between structures, hence enhancing the capabilities of the OLAP system.
In computing, a data warehouse, 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.
A chart is a graphical representation of data, in which "the data is represented by symbols, such as bars in a bar chart, lines in a line chart, or slices in a pie chart". A chart can represent tabular numeric data, functions or some kinds of qualitative structure and provides different info.
Online analytical processing, or OLAP, is an approach to answer multidimensional analytical (MDA) queries swiftly in computing. 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 business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture. The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP).
In dynamical system theory, a phase space is a space in which all possible states of a system are represented, with each possible state corresponding to one unique point in the phase space. For mechanical systems, the phase space usually consists of all possible values of position and momentum variables. The concept of phase space was developed in the late 19th century by Ludwig Boltzmann, Henri Poincaré, and Willard Gibbs.
A data mart is a structure / access pattern specific to data warehouse environments, used to retrieve clientfacing data. The data mart is a subset of the data warehouse and is usually oriented to a specific business line or team. Whereas data warehouses have an enterprisewide depth, the information in data marts pertains to a single department. In some deployments, each department or business unit is considered the owner of its data mart including all the hardware, software and data. This enables each department to isolate the use, manipulation and development of their data. In other deployments where conformed dimensions are used, this business unit ownership will not hold true for shared dimensions like customer, product, etc.
A flowchart is a type of diagram that represents an algorithm, workflow or process. The flowchart shows the steps as boxes of various kinds, and their order by connecting the boxes with arrows. This diagrammatic representation illustrates a solution model to a given problem. Flowcharts are used in analyzing, designing, documenting or managing a process or program in various fields.
An OLAP cube is a multidimensional array of data. Online analytical processing (OLAP) is a computerbased technique of analyzing data to look for insights. The term cube here refers to a multidimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than 3.
is the simplest style of data mart 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 tables. The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries.
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 singledimensional data set. A data set consisting of the number of wins for several football teams in a single year is also a singledimensional data set. A data set consisting of the number of wins for several football teams over several years is a twodimensional data set.
In computing, a snowflake schema is a logical arrangement of tables in a multidimensional database such that the entity relationship diagram resembles a snowflake shape. The snowflake schema is represented by centralized fact tables which are connected to multiple 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.
Essbase is a multidimensional database management system (MDBMS) that provides a multidimensional database platform upon which to build analytic applications. Essbase, whose name derives from "extended spreadsheet database", began as a product of Arbor Software, which merged with Hyperion Software in 1998. Oracle Corporation acquired Hyperion Solutions Corporation in 2007, as of 2009 Oracle marketed Essbase as "Oracle Essbase" and more recently, Essbase is offered as part of the Oracle Analytics Cloud. Until late 2005 IBM also marketed an OEM version of Essbase as DB2 OLAP Server.
In computer programming, an Iliffe vector, also known as a display, is a data structure used to implement multidimensional arrays. An Iliffe vector for an ndimensional array consists of a vector of pointers to an (n − 1)dimensional array. They are often used to avoid the need for expensive multiplication operations when performing address calculation on an array element. They can also be used to implement jagged arrays, such as triangular arrays, triangular matrices and other kinds of irregularly shaped arrays. The data structure is named after John K. Iliffe.
In computer programming contexts, a data cube is a multidimensional ("nD") array of values. Typically, the term datacube is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multiterabyte/petabyte data warehouses and time series of image data.
Multidimensional Expressions (MDX) is a query language for online analytical processing (OLAP) using a database management system. Much like SQL, it is a query language for OLAP cubes. It is also a calculation language, with syntax similar to spreadsheet formulas.
Microsoft SQL Server Analysis Services, SSAS, is an online analytical processing (OLAP) and data mining tool in Microsoft SQL Server. SSAS is used as a tool by organizations to analyze and make sense of information possibly spread out across multiple databases, or in disparate tables or files. Microsoft has included a number of services in SQL Server related to business intelligence and data warehousing. These services include Integration Services, Reporting Services and Analysis Services. Analysis Services includes a group of OLAP and data mining capabilities and comes in two flavors  Multidimensional and Tabular.
Dimensional modeling (DM) is part of the Business Dimensional Lifecycle methodology developed by Ralph Kimball which includes a set of methods, techniques and concepts for use in data warehouse design. The approach focuses on identifying the key business processes within a business and modelling and implementing these first before adding additional business processes, a bottomup approach. An alternative approach from Inmon advocates a top down design of the model of all the enterprise data using tools such as entityrelationship modeling (ER).
Diagrammatic reasoning is reasoning by means of visual representations. The study of diagrammatic reasoning is about the understanding of concepts and ideas, visualized with the use of diagrams and imagery instead of by linguistic or algebraic means.
A database model is a type of data model that determines the logical structure of a database and fundamentally determines in which manner data can be stored, organized and manipulated. The most popular example of a database model is the relational model, which uses a tablebased format.
The dimensional fact model (DFM) is an ad hoc and graphical formalism specifically devised to support the conceptual modeling phase in a DW project. DFM is extremely intuitive and can be used by analysts and nontechnical users as well. A shortterm working is sufficient to realize a clear and exhaustive representation of multidimensional concepts. It can be used from the initial DW lifecycle steps, to rapidly devise a conceptual model to share with customers.