Model-Driven Decision Support Systems (DSS) and Data-Driven Decision Support Systems are two different approaches used in decision support systems. Here's how they differ:
Model-Driven DSS:
Emphasis on Models: In a model-driven DSS, the focus is on mathematical or analytical models. These models represent the decision-making process and help in evaluating different scenarios. Assumptions and Rules: Model-driven DSS often rely on predefined rules and assumptions about the problem domain. These rules guide decision-making. Predictive: They are more predictive in nature, aiming to forecast outcomes based on the models and assumptions. Examples: Financial planning systems, supply chain optimization, and simulation-based decision support systems fall under this category. Data-Driven DSS:
Emphasis on Data: Data-driven DSS, as the name suggests, prioritize data. They collect and analyze large amounts of data to make informed decisions. Data Mining and Analytics: These systems often use data mining, machine learning, and analytics to discover patterns and insights in the data. Descriptive and Diagnostic: Data-driven DSS are more descriptive and diagnostic in nature. They provide insights into past and current data to inform decisions. Examples: Business intelligence systems, data warehouses, and dashboards are common examples of data-driven DSS. In summary, the main difference lies in their approach: model-driven DSS rely on predefined models and assumptions to make predictions about future scenarios, while data-driven DSS use large volumes of data and advanced analytics to provide insights into historical and current data, aiding in understanding the current state of affairs and making data-informed decisions. The choice between these two approaches depends on the specific needs and objectives of the decision support system.
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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?
Relationship is purely a construct of the er data model.Relation is a structure of the relational data model.
RECORD type works only in PL/SQL. But OBJECT type gets stored in database and can be used in both SQL and PL/SQL (without redefining it in PL/SQL).
data is like cordanites but information is is like a history book
monkey that's the answer
What is the difference between a model-driven and data-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
Model data driven user interacts primarily with a mathematical model and its results while data driven DSS is user interacts primarily with the data
Data driven research- data obtained from experiments lead to development of theory Theory Driven research-Theory lead to design of experimental tests
Network data model is just like a normal database model. In network model the data is seen as related to each other by links. Or we can say the relation between the data is represented by links.
Goal driven reasoning or backward chaining - an inference technique which uses IF THEN rules to repetitively break a goal into smaller sub-goals which are easier to prove. Data driven reasoning or forward chaining - an inference technique which uses IF THEN rules to deduce a problem solution from initial data.
Relationship is purely a construct of the er data model.Relation is a structure of the relational data model.
A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
difference between Data Mining and OLAP
Data-driven reasoning takes the facts of the problem and applies the rules or legal moves to produce new facts that lead to a goal. Goal-driven reasoning focus on the goal,finds the rules that could produce the goal,and chains backward through successive rules and subgoals to the given facts of the problem.
difference between serch data structure and allocation data structure
A database is a collection of tables that is used for some purpose (typically an application of some sort). A database model is a description of that database, and describes how the tables relate to each other. Typically, a model is designed first, then the actual database is implemented using the model as a blueprint.