M

Metadata

Data about the data in a data warehouse. Metadata provides a directory to help the DSS locate the contents of the data warehouse; it is a guide to mapping data as it is transformed from the operational environment to the data warehouse environment; and it serves as a guide to the algorithms used for summarization of current detailed data. Metadata is semantic information associated with a given variable. Metadata must include business definitions of the data and clear, accurate descriptions of data types, potential values, original source system, data formats, and other characteristics. Metadata defines and describes business data. Examples of metadata include data element descriptions, data type descriptions, attribute/property descriptions, range/domain descriptions, and process/method descriptions. The repository environment encompasses all corporate metadata resources: database catalogs, data dictionaries, and navigation services. Metadata includes things like the name, length, valid values, and description of a data element. Metadata is stored in a data dictionary and repository. It insulates the data warehouse from changes in the schema of operational systems.

Methodology

A system of principles, practices, and procedures applied to a specific branch of knowledge.

Middleware

A communications layer that allows applications to interact across hardware and network environments.

Model Base

A collection of preprogrammed quantitative models (e.g., statistical, financial, optimization) organized as a single unit.

Model-Driven DSS

This type of DSS emphasizes access to and manipulation of a model, e.g., statistical, financial, optimization and/or simulation. Simple statistical and analytical tools provide the most elementary level of functionality. Some OLAP systems that allow complex analysis of data may be classified as hybrid DSS systems providing both modeling and data retrieval and data summarization functionality. Data Mining is also a hybrid approach to DSS. In general, Model-Driven DSS use complex financial, simulation, optimization and/or rule (expert) models to provide decision support. Model-Driven DSS use data and parameters provided by decision makers to aid decision makers in analyzing a situation, but they are not usually data intensive, that is very large data bases are usually not need for Model-Driven DSS. Early versions of Model-Driven DSS were called Model Oriented DSS by Alter (1980) and Computationally Oriented DSS by Bonczek, Holsapple and Whinston (1981).

Modeling Tools

Software programs that help developers/users build mathematical models quickly. Spreadsheets and planning languages like IFPS are modeling tools.

Multi-Dimensional Database (MDBS and MDBMS)

A database that lets users analyze large amounts of data. An MDBS captures and presents data as arrays that can be arranged in multiple dimensions. Variables are the objects that hold data in a multidimensional database. These are simply arrays of values (usually numeric) that are "dimensioned" by the dimensions in a database. For example, a UNITS variable may be dimensioned by MONTH, PRODUCT, and REGION. This three-dimensional variable or array is often visualized as a cube of data. Multi-dimensional databases can have multiple variables, with common or a unique set of dimensions. This multi-dimensional view of data is especially powerful for OLAP applications.

Multi-Participant DSS

A decision support system that supports multiple participants engaged in a decision-making task (or functions as one of the participants). See Group DSS.

Multipoint Conference

An audio, data and/or video conference among more than two remote participants.

Multipoint Control Unit

A device used to link remote sites into a single conference call or a device to manage several simultaneous, independent conferences.