What is Model driven DSS?

What is the difference between data driven DSS and model driven DSS?

Model data driven user interacts primarily with a mathematical model and its results while data driven DSS is user interacts primarily with the data

What is the difference between a model-driven and data-driven DSS?

A model-driven DSS relies on mathematical or statistical models to analyze data and make predictions, while a data-driven DSS uses historical and real-time data to generate insights and support decision-making without relying heavily on predefined models. Model-driven DSS are more structured and use algorithms to process data, while data-driven DSS focus on exploring patterns and trends in data to inform decisions.

What is the differences between model driven and data driven?

Model data driven user interacts primarily with a mathematical model and its results while data driven DSS is user interacts primarily with the data

what is the difference between model driven and data driven DSS?

In a model-driven DSS, decision-making is based on predefined mathematical or statistical models, where users input data to generate output. In a data-driven DSS, decision-making is based on analyzing large volumes of historical data to identify patterns and trends, without necessarily relying on predefined models.

A model-driven DSS is primarily a stand-alone system that uses some type of model to perform what-if and other kinds of analyzes?

Datamart is primarily a stand-alone DSS that uses some type of model to perform "what-if" and other kinds of analyses.Visit http://www.prodigyhub.org for more such answers

Is not a DSS Component?

Decision Support System (DSS) is a combination of software, data, and models, not a specific component. A DSS typically includes components such as a database, model base, user interface, and analytical tools.

What The Google business model is driven by the sale of?

The sales model is driven by advertisements.

'Explain the concept need for and importance of a Decision Support System'?

Computerized decision support systems became practical with the development of minicomputers, timeshare operating systems and distributed computing. The history of the implementation of such systems begins in the mid-1960s. In a technology field as diverse as DSS, chronicling history is neither neat nor linear. Different people perceive the field of Decision Support Systems from various vantage points and report different accounts of what happened and what was important (cf., Arnott & Pervan, 2005; Eom & Lee, 1990b; McCosh & Correa-Perez, 2006; Power, 2003; Power, 2004a; Silver, 1991). As technology evolved new computerized decision support applications were developed and studied. Researchers used multiple frameworks to help build and understand these systems. Today one can organize the history of DSS into the five broad DSS categories explained in Power (2001; 2002; 2004b), including: communications-driven, data-driven, document driven, knowledge-driven and model-driven decision support systems. This hypertext document is a starting point in explaining the origins of the various technology threads that are converging to provide integrated support for managers working alone, in teams and in organization hierarchies to manage organizations and make more rational decisions. History is both a guide to future activity in this field and a record of the ideas and actions of those who have helped advance our thinking and practice. Historical facts can be sorted out and better understood, but more information gathering is necessary. This web page is a starting point in collecting more first hand accounts and in building a more complete mosaic of what was occurring in universities, software companies and in organizations to build and use DSS. This document traces decision support applications and research studies related to model and data-oriented systems, management expert systems, multidimensional data analysis, query and reporting tools, online analytical processing (OLAP), Business Intelligence, group DSS, conferencing and groupware, document management, spatial DSS and Executive Information Systems as the technologies emerge, converge and diverge. All of these technologies have been used to support decision making. A timeline of major historical milestones relevant to DSS is included in Appendix I. The study of decision support systems is an applied discipline that uses knowledge and especially theory from other disciplines. For this reason, many DSS research questions have been examined because they were of concern to people who were building and using specific DSS. Hence much of the broad DSS knowledge base provides generalizations and directions for building more effective DSS (cf., Baskerville & Myers, 2002; Keen, 1980). The next section describes the origins of the field of decision support systems. Section 3 discusses the decision support systems theory development that occurred in the late 1970s and early 1980s. Section 4 discusses important developments to communications-driven , data-driven, document driven, knowledge-driven and model-driven DSS (cf., Power, 2002). The final section briefly discusses how DSS practice, research and technology is continuing to

Does this model have a cambelt or chain driven?

Sorta, hard to answer as you listed no model.

What does MDG stand for?

Model Driven Generation

The way a company does business is driven by?

Its business model