The document discusses data warehouses, including their history, structure, components, applications, and how they support decision making processes by storing integrated data from various sources over time to enable analysis and informed decisions. A data warehouse contains subject-oriented data from multiple sources that is integrated, non-volatile, and time-variant to support reporting, analysis, and decision support systems. It also reviews the various components involved in collecting, transforming, storing, and accessing the data to make it available for analysis.
The document discusses using decision support systems to help Nintendo analyze pricing for a new product. It presents an introduction to Nintendo's situation, outlines the problem, and describes DSS techniques like the solver and decision trees that could be applied. A decision tree is created to determine customer satisfaction based on user type. The solver is then implemented with constraints and variables to maximize profit based on two data sets. It concludes the DSS tools could help Nintendo regain their leading market position if the new product is priced appropriately based on manufacturing costs.
This document provides an overview of decision support systems (DSS), including their history, evolution, definitions, components, users, and categories. It discusses how DSS have developed from early computers used for calculations during World War II to today's data-driven systems. A DSS is defined as an information system that provides knowledge workers with information to make informed decisions. Key components of a DSS include a database, model base, knowledge base, and user interface. DSS support but do not replace human decision makers such as executives and managers. Common categories of DSS are data-driven versus model-driven and individual versus group-oriented systems.
This document provides an introduction and overview of decision support systems (DSS). It begins by defining DSS as interactive computer-based systems that help support semi-structured decision-making. It then discusses the evolution of DSS from early frameworks developed by Gorry and Scott Morton. The document outlines the key components, functions, classifications, users and development methods of DSS. It concludes by noting that DSS are now widely used around the world to help solve complex problems in business, government and other organizations.
This document provides an overview of decision support system (DSS) concepts. It defines DSS as an interactive, computer-based system that helps decision-makers utilize data and models to solve unstructured or semi-structured problems. The document outlines different types of DSS based on the mode of assistance (document, communication, data, model, and knowledge-driven) and the degree of problem solving support. It also discusses characteristics of DSS and presents a typical DSS model consisting of five main components: the user, user interface, model base, inference engine, and knowledge base.
Decision support systems (DSS) are interactive computer-based systems designed to help decision makers utilize data and models to solve semi-structured or unstructured problems. There are several types of DSS including data-driven, model-driven, communications-driven, document-driven, and knowledge-driven systems. Group decision support systems (GDSS) are a specialized type of DSS that provide technology and structure to help groups make decisions and include additional capabilities to support collaboration.
The document defines decision support systems (DSS) as interactive computer systems that help decision-makers use data and models to solve structured, unstructured, or semi-structured problems. It discusses how DSS can aid in decision-making by integrating information and supporting alternatives. The document also outlines the key stages of decision-making - intelligence, design, choice, and implementation - and describes different types of DSS like data-driven, model-driven, and knowledge-driven systems. Examples are provided of how DSS are used in domains like airline reservations and loan approval.
Training Slides of Decision Support System, discussing how the system as an interactive computer-based system that is being effectively used in communications technologies.
Some keypoints:
- The Decision Support Paradigm
- Basic Concepts of DSS
- Examples of DSS
For further information regarding the course, please contact:
info@asia-masters.com
Decision support systems (DSS) are interactive computer-based systems that help managers solve structured, semi-structured, and unstructured decision problems. DSS use data warehouses to store integrated data from multiple sources and allow users to analyze and visualize the data. Data mining tools can then be used to discover patterns in large datasets and predict future trends to help managers make strategic decisions.
The document discusses using decision support systems to help Nintendo analyze pricing for a new product. It presents an introduction to Nintendo's situation, outlines the problem, and describes DSS techniques like the solver and decision trees that could be applied. A decision tree is created to determine customer satisfaction based on user type. The solver is then implemented with constraints and variables to maximize profit based on two data sets. It concludes the DSS tools could help Nintendo regain their leading market position if the new product is priced appropriately based on manufacturing costs.
This document provides an overview of decision support systems (DSS), including their history, evolution, definitions, components, users, and categories. It discusses how DSS have developed from early computers used for calculations during World War II to today's data-driven systems. A DSS is defined as an information system that provides knowledge workers with information to make informed decisions. Key components of a DSS include a database, model base, knowledge base, and user interface. DSS support but do not replace human decision makers such as executives and managers. Common categories of DSS are data-driven versus model-driven and individual versus group-oriented systems.
This document provides an introduction and overview of decision support systems (DSS). It begins by defining DSS as interactive computer-based systems that help support semi-structured decision-making. It then discusses the evolution of DSS from early frameworks developed by Gorry and Scott Morton. The document outlines the key components, functions, classifications, users and development methods of DSS. It concludes by noting that DSS are now widely used around the world to help solve complex problems in business, government and other organizations.
This document provides an overview of decision support system (DSS) concepts. It defines DSS as an interactive, computer-based system that helps decision-makers utilize data and models to solve unstructured or semi-structured problems. The document outlines different types of DSS based on the mode of assistance (document, communication, data, model, and knowledge-driven) and the degree of problem solving support. It also discusses characteristics of DSS and presents a typical DSS model consisting of five main components: the user, user interface, model base, inference engine, and knowledge base.
Decision support systems (DSS) are interactive computer-based systems designed to help decision makers utilize data and models to solve semi-structured or unstructured problems. There are several types of DSS including data-driven, model-driven, communications-driven, document-driven, and knowledge-driven systems. Group decision support systems (GDSS) are a specialized type of DSS that provide technology and structure to help groups make decisions and include additional capabilities to support collaboration.
The document defines decision support systems (DSS) as interactive computer systems that help decision-makers use data and models to solve structured, unstructured, or semi-structured problems. It discusses how DSS can aid in decision-making by integrating information and supporting alternatives. The document also outlines the key stages of decision-making - intelligence, design, choice, and implementation - and describes different types of DSS like data-driven, model-driven, and knowledge-driven systems. Examples are provided of how DSS are used in domains like airline reservations and loan approval.
Training Slides of Decision Support System, discussing how the system as an interactive computer-based system that is being effectively used in communications technologies.
Some keypoints:
- The Decision Support Paradigm
- Basic Concepts of DSS
- Examples of DSS
For further information regarding the course, please contact:
info@asia-masters.com
Decision support systems (DSS) are interactive computer-based systems that help managers solve structured, semi-structured, and unstructured decision problems. DSS use data warehouses to store integrated data from multiple sources and allow users to analyze and visualize the data. Data mining tools can then be used to discover patterns in large datasets and predict future trends to help managers make strategic decisions.
Organization’s success depends on quality of managers’ decisions
When decisions involve large amounts of data and complex processing, a DSS is a valuable tool
When decision making involves many uncertainties and/or lots of alternatives a DSS is needed
DSS and decision support system and its typesHammalAkhtar
The document discusses a group project on decision support systems (DSS). It defines DSS as a computer-based system that supports business or organizational decision-making. It describes the key components and characteristics of DSS, including that they facilitate decision-making, allow interaction, and are intended for repeated use to improve decision accuracy and quality. The document also outlines several applications of DSS and lists advantages like time savings and competitive benefits, as well as potential disadvantages like information overload.
The document provides an overview of the syllabus for a Decision Support System & MIS course. It outlines 4 units that will be covered: [1] Decision Support System overview and components, [2] using information systems for strategic advantage, [3] information system analysis and design, and [4] specific types of information systems like marketing, manufacturing, accounting, and financial. It also provides sample questions and answers that define decision support systems, explain their characteristics and needs, and components and classifications.
Rashmiranjan Das presented on definitions, characteristics, objectives, elements, components, users, capabilities, and limitations of decision support systems (DSS). According to Gerrity, DSS is an effective blend of human intelligence, information technology, and software that interact closely to solve complex problems. DSS is characterized as an interactive computer-based system that facilitates the solution of unstructured problems and provides quick yet flexible analysis that allows for managerial intuition. The objectives of DSS are to identify how information processing supports managerial work and to describe decision making systems and processes in organizations.
Decision support systems (DSS) are computer-based systems that analyze data and help decision-makers solve semi-structured or unstructured problems. DSS provide access to internal and external data, models, and documents to help identify problems and solutions. There are different types of DSS including data-driven, model-driven, communication-driven, document-driven, and knowledge-driven systems. DSS have benefits like improved efficiency, faster decision-making, and competitive advantages. They are used in various applications including clinical decision support, banking, and analyzing business performance.
The document discusses decision support systems (DSS), which are computer-based systems that help organizational decision-making. It describes the components, tools, and models used in DSS, including databases, model bases, dialog generation systems, and mathematical models like linear programming. Linear programming is used to optimize outcomes under constraints by finding the best values for decision variables. DSS can help decision-making but also have disadvantages like overemphasizing decisions or obscuring responsibility.
This document defines and describes decision support systems (DSS). It begins by defining DSS as computer-based systems that help decision makers use data and models to solve semi-structured or unstructured problems. It then discusses decision making processes, how DSS incorporate different information systems, basic themes and a taxonomy of DSS types. The document also outlines the benefits of DSS, their components, applications, characteristics and capabilities.
Decision support systems (DSS) are computer-based systems that analyze data and help decision-makers make better judgments. A DSS has three main components: a database, a model, and a user interface. DSS can classify data inputs, user expertise, outputs, and generated decisions. They are used in various fields like healthcare, business, and transportation to improve efficiency, speed up decision-making, and gain a competitive advantage. Key benefits of DSS include faster problem solving, increased organizational control, and promoting learning.
The document discusses decision support systems (DSS). It defines DSS as interactive software systems that support business and organizational decision-making. The document outlines the key components of DSS including data management systems, model-based systems, and dialog generation systems. It also describes the different types of DSS and their characteristics, objectives, advantages, disadvantages, and applications.
Decision support systems are interactive software tools that help managers make decisions by providing access to large amounts of information from various systems. DSS uses analytical models, summaries, exceptions, patterns and trends from this data to help decision makers identify and solve problems, but does not make the decisions itself. Key components of a DSS include a database management system, model management system, and support tools. There are various ways to classify DSS, including whether they are text, database, spreadsheet, solver or rules oriented, and different types support operational, management or strategic decision making.
The document discusses decision support systems (DSS). It provides an introduction to DSS, defining them as interactive computer-based systems that help decision-makers use data, documents, knowledge and models to identify and solve problems. It then outlines the goals and requirements of a course on DSS, including familiarizing students with different DSS forms and the practical issues of implementing them. The document concludes by discussing the multidisciplinary foundations of DSS research.
This PPT Covers the following topics:
Decision Making as a Component of Problem Solving, Problem Solving Factors, Characteristics of a DSS, Example of DSS, Integration of TPS, MIS, Web-Based Decision Support Systems, Components of a DSS, Advantages and Disadvantages of Modeling, Group Decision Support System, Executive Support System, Characteristics of ESS.
Decision Support System & Group Decision Support SystemNaresh Rupareliya
This document discusses decision support systems and group decision support systems. It defines DSS as applications that help with the intelligence phase of decision making by identifying problems and potential solutions. It describes different types of DSS like status inquiry, data analysis, and model-based systems. It then discusses group decision support systems, how they allow multiple people to collaborate over networks or teleconferencing, and examples of GDSS tools and applications.
The document discusses decision support systems (DSS), which help executives make better decisions by using historical and current data from internal and external sources. DSS combine large amounts of data with analytical models and tools to provide better information for decision making. The document also describes group decision support systems (GDSS), which are electronic meeting systems that facilitate group collaboration to solve problems. Finally, the document defines intelligent systems as systems that can learn from experiences to improve performance and decision making.
The document discusses decision support systems and their components. It covers topics like decision structures, decision support trends, decision support system components, online analytical processing, geographic information systems, expert systems, and their applications. Key aspects of decision making like relevant information, types of decisions, and decision making structures are also explained.
Group decision support systems (GDSS) combine communication, computing, and decision support technologies to facilitate complex decision making by groups. GDSS provide structured meeting support and allow participants to collaborate over a shared computer network. Research has found GDSS can promote greater participation, synergy, record keeping, and satisfaction in group meetings by facilitating interdisciplinary collaboration and organizational learning, though some tasks are not amenable to GDSS and communication can be slowed.
1. The document discusses Group Decision Support Systems (GDSS), which are computer systems that support groups of people working together to make decisions. GDSS are intended to improve group decision making processes and outcomes.
2. GDSS have different levels of technology, ranging from basic communication support to more advanced modeling and analysis tools. Types of GDSS include face-to-face systems, network-based systems, and handset-based systems.
3. Examples show how GDSS can be used to prioritize research projects and support emergency management decision making in China. GDSS provide structure to group processes and aim to reduce problems like some members dominating discussions.
The document discusses future trends in decision support systems (DSS). It predicts that within the next 10-15 years, DSS tools will be able to pull data from more sources and integrate data more effectively through increased classification. DSS interfaces will also likely shift to tablets and require less training to use. The role of artificial intelligence in DSS is explored along with potential applications like virtual reality and telemedicine. The document concludes by emphasizing the importance of health informatics in the future and noting that its success depends on appropriate, socially-minded applications.
The document discusses decision support systems (DSS), which are computer-based tools that help decision-makers in organizations solve problems and make decisions. It describes the four stages of decision making - intelligence, design, choice, and implementation. It then explains different types of DSS, including communication-based, data-based, document-based, knowledge-based, and model-based systems. Finally, it discusses benefits of using DSS and group decision support systems.
A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. A DSS collects data from multiple sources, formats and collates the data into a database, and provides tools for reporting, monitoring, and analyzing the data to help decision makers make better decisions. DSS can be classified based on their relationship with the user, such as passive, active or cooperative DSS, and based on their scope and use, such as enterprise-wide or desktop DSS. The objectives of a DSS are to increase the effectiveness of decision making, support but not replace the decision maker, and improve decision making effectiveness.
A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. A DSS can provide suggestions or solutions to help decision makers, and allows modification of suggestions before validation. DSS can be classified based on their relationship with the user as passive, active or cooperative, and based on their scope as enterprise-wide or desktop. The objectives of a DSS are to increase effectiveness of decision making and improve directors' effectiveness. A DSS has components like inputs, user knowledge, outputs, and decisions.
Organization’s success depends on quality of managers’ decisions
When decisions involve large amounts of data and complex processing, a DSS is a valuable tool
When decision making involves many uncertainties and/or lots of alternatives a DSS is needed
DSS and decision support system and its typesHammalAkhtar
The document discusses a group project on decision support systems (DSS). It defines DSS as a computer-based system that supports business or organizational decision-making. It describes the key components and characteristics of DSS, including that they facilitate decision-making, allow interaction, and are intended for repeated use to improve decision accuracy and quality. The document also outlines several applications of DSS and lists advantages like time savings and competitive benefits, as well as potential disadvantages like information overload.
The document provides an overview of the syllabus for a Decision Support System & MIS course. It outlines 4 units that will be covered: [1] Decision Support System overview and components, [2] using information systems for strategic advantage, [3] information system analysis and design, and [4] specific types of information systems like marketing, manufacturing, accounting, and financial. It also provides sample questions and answers that define decision support systems, explain their characteristics and needs, and components and classifications.
Rashmiranjan Das presented on definitions, characteristics, objectives, elements, components, users, capabilities, and limitations of decision support systems (DSS). According to Gerrity, DSS is an effective blend of human intelligence, information technology, and software that interact closely to solve complex problems. DSS is characterized as an interactive computer-based system that facilitates the solution of unstructured problems and provides quick yet flexible analysis that allows for managerial intuition. The objectives of DSS are to identify how information processing supports managerial work and to describe decision making systems and processes in organizations.
Decision support systems (DSS) are computer-based systems that analyze data and help decision-makers solve semi-structured or unstructured problems. DSS provide access to internal and external data, models, and documents to help identify problems and solutions. There are different types of DSS including data-driven, model-driven, communication-driven, document-driven, and knowledge-driven systems. DSS have benefits like improved efficiency, faster decision-making, and competitive advantages. They are used in various applications including clinical decision support, banking, and analyzing business performance.
The document discusses decision support systems (DSS), which are computer-based systems that help organizational decision-making. It describes the components, tools, and models used in DSS, including databases, model bases, dialog generation systems, and mathematical models like linear programming. Linear programming is used to optimize outcomes under constraints by finding the best values for decision variables. DSS can help decision-making but also have disadvantages like overemphasizing decisions or obscuring responsibility.
This document defines and describes decision support systems (DSS). It begins by defining DSS as computer-based systems that help decision makers use data and models to solve semi-structured or unstructured problems. It then discusses decision making processes, how DSS incorporate different information systems, basic themes and a taxonomy of DSS types. The document also outlines the benefits of DSS, their components, applications, characteristics and capabilities.
Decision support systems (DSS) are computer-based systems that analyze data and help decision-makers make better judgments. A DSS has three main components: a database, a model, and a user interface. DSS can classify data inputs, user expertise, outputs, and generated decisions. They are used in various fields like healthcare, business, and transportation to improve efficiency, speed up decision-making, and gain a competitive advantage. Key benefits of DSS include faster problem solving, increased organizational control, and promoting learning.
The document discusses decision support systems (DSS). It defines DSS as interactive software systems that support business and organizational decision-making. The document outlines the key components of DSS including data management systems, model-based systems, and dialog generation systems. It also describes the different types of DSS and their characteristics, objectives, advantages, disadvantages, and applications.
Decision support systems are interactive software tools that help managers make decisions by providing access to large amounts of information from various systems. DSS uses analytical models, summaries, exceptions, patterns and trends from this data to help decision makers identify and solve problems, but does not make the decisions itself. Key components of a DSS include a database management system, model management system, and support tools. There are various ways to classify DSS, including whether they are text, database, spreadsheet, solver or rules oriented, and different types support operational, management or strategic decision making.
The document discusses decision support systems (DSS). It provides an introduction to DSS, defining them as interactive computer-based systems that help decision-makers use data, documents, knowledge and models to identify and solve problems. It then outlines the goals and requirements of a course on DSS, including familiarizing students with different DSS forms and the practical issues of implementing them. The document concludes by discussing the multidisciplinary foundations of DSS research.
This PPT Covers the following topics:
Decision Making as a Component of Problem Solving, Problem Solving Factors, Characteristics of a DSS, Example of DSS, Integration of TPS, MIS, Web-Based Decision Support Systems, Components of a DSS, Advantages and Disadvantages of Modeling, Group Decision Support System, Executive Support System, Characteristics of ESS.
Decision Support System & Group Decision Support SystemNaresh Rupareliya
This document discusses decision support systems and group decision support systems. It defines DSS as applications that help with the intelligence phase of decision making by identifying problems and potential solutions. It describes different types of DSS like status inquiry, data analysis, and model-based systems. It then discusses group decision support systems, how they allow multiple people to collaborate over networks or teleconferencing, and examples of GDSS tools and applications.
The document discusses decision support systems (DSS), which help executives make better decisions by using historical and current data from internal and external sources. DSS combine large amounts of data with analytical models and tools to provide better information for decision making. The document also describes group decision support systems (GDSS), which are electronic meeting systems that facilitate group collaboration to solve problems. Finally, the document defines intelligent systems as systems that can learn from experiences to improve performance and decision making.
The document discusses decision support systems and their components. It covers topics like decision structures, decision support trends, decision support system components, online analytical processing, geographic information systems, expert systems, and their applications. Key aspects of decision making like relevant information, types of decisions, and decision making structures are also explained.
Group decision support systems (GDSS) combine communication, computing, and decision support technologies to facilitate complex decision making by groups. GDSS provide structured meeting support and allow participants to collaborate over a shared computer network. Research has found GDSS can promote greater participation, synergy, record keeping, and satisfaction in group meetings by facilitating interdisciplinary collaboration and organizational learning, though some tasks are not amenable to GDSS and communication can be slowed.
1. The document discusses Group Decision Support Systems (GDSS), which are computer systems that support groups of people working together to make decisions. GDSS are intended to improve group decision making processes and outcomes.
2. GDSS have different levels of technology, ranging from basic communication support to more advanced modeling and analysis tools. Types of GDSS include face-to-face systems, network-based systems, and handset-based systems.
3. Examples show how GDSS can be used to prioritize research projects and support emergency management decision making in China. GDSS provide structure to group processes and aim to reduce problems like some members dominating discussions.
The document discusses future trends in decision support systems (DSS). It predicts that within the next 10-15 years, DSS tools will be able to pull data from more sources and integrate data more effectively through increased classification. DSS interfaces will also likely shift to tablets and require less training to use. The role of artificial intelligence in DSS is explored along with potential applications like virtual reality and telemedicine. The document concludes by emphasizing the importance of health informatics in the future and noting that its success depends on appropriate, socially-minded applications.
The document discusses decision support systems (DSS), which are computer-based tools that help decision-makers in organizations solve problems and make decisions. It describes the four stages of decision making - intelligence, design, choice, and implementation. It then explains different types of DSS, including communication-based, data-based, document-based, knowledge-based, and model-based systems. Finally, it discusses benefits of using DSS and group decision support systems.
A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. A DSS collects data from multiple sources, formats and collates the data into a database, and provides tools for reporting, monitoring, and analyzing the data to help decision makers make better decisions. DSS can be classified based on their relationship with the user, such as passive, active or cooperative DSS, and based on their scope and use, such as enterprise-wide or desktop DSS. The objectives of a DSS are to increase the effectiveness of decision making, support but not replace the decision maker, and improve decision making effectiveness.
A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. A DSS can provide suggestions or solutions to help decision makers, and allows modification of suggestions before validation. DSS can be classified based on their relationship with the user as passive, active or cooperative, and based on their scope as enterprise-wide or desktop. The objectives of a DSS are to increase effectiveness of decision making and improve directors' effectiveness. A DSS has components like inputs, user knowledge, outputs, and decisions.
This document provides information on decision support systems (DSS). It discusses definitions of DSS and how they support decision making. DSS can take many forms, from model-driven to data-driven systems. The document outlines frameworks for developing DSS and describes different types of DSS including passive, active, and cooperative systems. It also discusses applications of DSS in areas like business and agriculture.
This document discusses different levels and types of decision support systems (DSS). It begins by explaining that DSS are designed to support semi-structured and unstructured decision making by providing analytical models and access to databases. It then describes different levels of DSS including unstructured DSS which provide support for decision making in ill-structured situations. The document also covers capabilities and components of DSS, as well as how they can be developed and classified. It discusses group decision support systems and executive information systems.
Decision Support Systems: Concept, Constructing a DSS, Executive Information ...Ashish Hande
Decision Support Systems: Concept, Constructing a DSS,
Executive Information System, (EIS), Artifical Intelligence
System (AIS), knowledge Based Expert System (KBES),
Enterprise Management System (EMS), Decision Support
Management System (DSMS).
MODEL- DRIVEN DSS
includes system that use accounting, financial models, and representational models.
2. DATA DRIVEN DSS
file drawer & management reporting system, data warehousing, geographical information.
This document discusses management information systems (MIS) and related topics. It defines MIS as a computer-based system that processes data into information to support organizational operations, management, and decision-making. It notes that MIS needs to be business-driven, management-oriented, flexible, use common databases, integrate systems, and avoid redundant data storage. The document also discusses decision support systems, expert systems, components of each, and advantages and disadvantages of expert systems.
The document provides information on the syllabus for the Decision Support System & MIS course for the MBA 3rd semester at M.D.U. Rohtak. It outlines the course content over 4 units that cover topics such as decision support systems, information systems for strategic advantage, information system analysis and design, and specific types of information systems for marketing, manufacturing, accounting, and finance. It also provides sample questions and answers that further explain concepts covered in Unit 1 such as defining decision support systems, explaining their characteristics and needs, components and classification, and the steps involved in constructing a DSS.
A Decision Support System (DSS) is a computer-based information system that supports business or organizational decision-making. It can help managers at different levels of an organization make decisions by providing tools to access and analyze data. A DSS combines data, documents, knowledge, and models to help identify and solve problems. It has evolved over time from focusing on data and models to also include knowledge and documents. A DSS is made up of a database, models, and a user interface. It aims to support semi-structured and unstructured decision-making.
A Decision Support System is a computer-based information system that supports business or organizational decision-making activities.
A DSS is a collection of integrated software applications and hardware that form the backbone of an organization’s decision making process and help to make decisions, which may be rapidly changing and not easily specified in advance.
Management information system (MIS) is
Integrated collection of people, procedures, databases, and devices Provides managers and decision makers with information to help achieve organizational goals.
GDSS and DSS both provide decision support but GDSS focuses on group decisions using networking and technology while DSS focuses on individual decisions without networking. Groupware tools like communication, conferencing and collaboration tools can facilitate knowledge sharing and creation when used with knowledge management systems. Data marts contain subset of data warehouse data for specific teams and are used for business intelligence applications while data warehouses contain enterprise-wide data. Data manipulation languages like SQL are used to insert, delete and update data in databases. Expert systems use knowledge bases and inference engines to provide answers to problems like a human expert.
The document discusses various topics related to management information systems (MIS) and decision support systems (DSS). It provides an overview of DSS, describing them as computerized systems that analyze and synthesize data to produce reports assisting decision-making. It outlines different types of DSS, including text-oriented, data-oriented, spreadsheet-oriented, and rules-oriented systems. The document also discusses group decision support systems (GDSS) and their role in facilitating collaborative decision-making. Finally, it covers key aspects of implementing an MIS, such as creating a schedule, identifying bottlenecks, selecting hardware vendors, and integrating the new system.
1. Decision support systems (DSS) help support managerial decision making through analysis of large amounts of data in a heuristic fashion without updating the underlying data.
2. DSS have two major components - a data warehouse containing current and historical internal and external data, and decision support software tools for data analysis like OLAP and data mining.
3. Model-driven DSS use techniques like what-if analysis and goal seek analysis to understand the impact of changes, while data-driven DSS use techniques like OLAP and data mining.
A GDSS is an interactive, computer based system that facilitates solution of unstructured problems by a set of decisions makers working together as a group. A GDSS is superior then DSS because in GDSS the decisions are taken by a group of DSS. So it is superior to the DSS." There are three types of GDSS: decision networks, decision rooms, and teleconferencing. The advantages of GDSS include taking better decisions, solving problems, minimizing risk, collecting large amounts of information, providing interactive communication, improving decision making processes, and coordinating activities.
Decision support n system management www.it-workss.comVarunraj Kalse
A GDSS is an interactive, computer based system that facilitates solution of unstructured problems by a set of decisions makers working together as a group. A GDSS is superior then DSS because in GDSS the decisions are taken by a group of DSS. So it is superior to the DSS." There are three types of GDSS: decision networks, decision rooms, and teleconferencing. The advantages of GDSS include taking better decisions, solving problems, minimizing risk, collecting large amounts of information, providing interactive communication, improving decision making processes, and coordinating activities.
Management Information Systems (MIS) are systems that focus on providing efficient and effective strategic decision making through the integration of hardware, software, data, processes, and people. Decision Support Systems (DSS) are interactive software systems intended to help managers access large data volumes from various systems to help decision making. Key differences are that MIS focuses on information processing and control while DSS focuses on planning, analysis and decision support. DSS also allows direct data access and is more dependent on management judgement.
Intelligent decision support systems a frameworkAlexander Decker
This document discusses intelligent decision support systems (IDSS). It begins by defining decision support systems (DSS) and how they have evolved with advancements in technology to become more intelligent through the inclusion of artificial intelligence techniques. The document then presents a framework for IDSS, outlining its key components like the knowledge management subsystem. Finally, it discusses various tools and technologies that can help capture, transform, store, and disseminate information and knowledge to support IDSS, such as document management systems, the internet, operational databases, data warehouses, and data marts.
Management information systems (MIS) collect, process, store, and distribute data to support decision-making and control in organizations. Decision support systems (DSS) are a type of information system that supports business or organizational decision-making activities. DSS provide analysis of information to help decision makers choose among alternative solutions. There are different types of DSS, including communication-driven, data-driven, document-driven, knowledge-driven, model-driven, spreadsheet-based, and web-based systems. DSS use analytical tools like what-if analysis, sensitivity analysis, goal-seeking analysis, and optimization analysis to help decision makers evaluate alternatives.
DSS:Conceptos, metodologias y Tecnologiasluzenith_g
This document provides an overview of decision support systems (DSS), including their key components, characteristics, and capabilities. It describes the main components of a DSS as the data management subsystem, model management subsystem, user interface subsystem, and optional knowledge-based subsystem. It also outlines some important DSS classifications, configurations, and application areas.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
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Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
2. INTRODUCTION OF
DATABASE
First used by William Inmon in the early
1980s.
A data warehouse is a subject
oriented,integrated,time variant, and volatile
collection of data in support of management
decision making process.
3. Supports Decison Supports System(DDS).
It is more than just data,it is also the
processes involved in getting that data from
source to table and in getting the data from
table to analysts.
It is the data and the process
managers(load/query/warehouse) that make
information available enabling people to make
informed decisions.
5. STRUCTURE OF DATA
WAREHOUSE
TIME –VARIANT
• Contain information collected over time.
• Decisions are made by analyzing past trends in
companies performance.
NON VOLATILE
• Data never updated but used only for queries.
• Change,update,delete,etc is done to only
operational data.
• i.e it is filled only with the historical data.
6. INTEGRATED
• Contains various types of data and
database integrated to make it consistent.
SUBJECT-ORIETNED
• Provides simple and concise collection of data
• Is built around all the existing applications of
operational data.
7. COMPONENTS OF DATA
WAREHOUSE
• Data sources
• Data transformations
• Reporting
• Metadata
• Operations
• Other components
8.
9. • Data Sources
Data Sources refers to any electronic repository of
information where data is passed from these systems to
the data ware house either on a transaction-by
transaction basis for real time data warehouses or on a
regular cycle.
• Data Transformation
The Data Transformation layer receives data from the
data sources,cleans and standarizes it, and loads it in the
data repository.
•
. Data Warehouse
The data warehouse is a relational database organized to
hold information in a structure that best supports reporting
and analysis.
10. • Reporting
The data in the data ware house must be available to all the
users if the data warehouse is to be useful.
• Metadata
Metadata or “DATA ABOUT DATA” is used to inform users
of the data warehouse about its status ans the information
held within the data warehouse.
• Operations:
Data warehouse operations comprises of the
processes of loading, manipulating and extracting data
from the data warehouse. Operations also covers user
management, security, capacity management and related
functions.
11. In addition, the following components also
exist in some data warehouses:
1. Dependent Data Marts: A dependent data mart
is a physical database (either on the same hardware as
the data warehouse or on a separate hardware
platform) that receives all its information from the data
warehouse.
2. Logical Data Marts: A logical data mart is a
filtered view of the main data warehouse but does not
physically exist as a separate data copy.
3. Operational Data Store: An ODS is an
integrated database of operational data. Its sources
include legacy systems and it contains current or near
term data.
12. APPLICATIONS OF DATA
WAREHOUSE
Safety
• under the control of data ware house
users so information can be stored
safely for past.
Fast retrieval of data
•Separate from operational systems,so it
provide retrieval of data without slowind down
operation.
13. Facilitate decision support system
Facilitate DSS such as trend reports,exception
reports, and reports that show actual performance
versus goals.
Common data model
Provide common data model for all data of interest
regardless of the data’s source .
Easy to report and analyze information such as
sales invoices,order receipts,general ledger etc
14. Analytical processing
multidimensional analysis of data warehouse data
supports basic OLAP operations, slice-dice, drilling, pivoting
Information processing
supports querying, basic statistical analysis,
and reporting using crosstabs, tables, charts
and graphs
Data mining
knowledge discovery from hidden patterns
supports associations, constructing analytical models,
performing classification and prediction, and
presenting the mining results using visualization tools
16. Slide 16
Chapter
10 Decision Support Systems
How do we define a ‘decision’?
A position or opinion or judgment reached
after consideration
The act of making up your mind about
something
The commitment to irrevocably allocate
valuable resources. A decision is a
commitment to act. Action is therefore
the irrevocable allocation of valuable
resources.
A determination of future action
The main function of a manager
17. Slide 17
Chapter
10 Decision Support Systems
What Types of decisions are there?
Structured Decisions: For ax + bx + c = 0, the value of x is given by:
2
Situations where the procedures to follow when a decision is
needed can be specified in advance
Semi-structured Decisions:
Decision procedures that can be pre-
specified, but not enough to lead to a
definite recommended decision
Unstructured Decisions:
Decision situations where it is not possible to
specify in advance most of the decision
procedures to follow
18. Slide 18
Chapter
10 Decision Support Systems
What is a decision support system?
Computer-based information systems that supports business or
organizational decision making activities.
DSSs serve the management, operations and planning levels of
an organization and help to make decisions, which may be rapidly
changing and not easily specified in advance.
DSS include knowledge-based systems. A properly designed DSS
is and interactive software –based system intended to help
decision makers compiled useful information from a combination of
raw data documents or personal knowledge or business model to
identify and solve problem and make decision.
19. Slide 19
Chapter
10 Decision Support Systems
What doesn’t a decision support system do?
Provide the solution (it is only tool)
Be used over and over again (It was designed for unique decision
making)
Always use the same
analytical models and tools
(The decision maker
chooses the models and
tools based on the problem
at hand)
20. Slide 20
Chapter
10 Decision Support Systems
What types of DSS analysis are there?
What-if Analysis:
User make changes to variables, or relationships among
variables, and observe the resulting changes
Sensitivity Analysis:
The value of only one variable is changed repeatedly and the
resulting changes in other variables are observed
Goal-Seeking:
The value of only one variable is changed repeatedly and the
resulting changes in other variables are observed
Optimization:
Find the optimum value for target variables given certain
constraints
21. Slide 21
Chapter
10 Decision Support Systems
What are the components of a DSS?
22. Slide 22
Chapter
10 Decision Support Systems
Database Concept
The database concept has evolved since the 1960s to ease increasing
difficulties in designing, building, and maintaining complex information
systems (typically with many concurrent end-users, and with a large amount
of diverse data).
Database is a collection of interrelated data that are organized so that it’s
contents can be easily managed accessed and updated.
A database contains a collection of related items or facts arranged in a
specific structured. The simple example of non computerized database is a
telephone directory.
23. Slide 23
Chapter
10 Decision Support Systems
Model Management System
A model management subsystem contains completed models
and the building blocks necessary to develop DSS applications.
This includes standard software with financial, statistical,
management science, or other quantitative models.
An example is Excel, with its many mathematical and statistical
functions.
24. Slide 24
Chapter
10 Decision Support Systems
The User Interface
The term user interface covers all aspects of the communications
between a user and the DSS.
Some DSS experts feel that the user interface is the most
important DSS component because much of the
power, flexibility, and ease of use of the DSS are derived from this
component.
For example, the ease of use of the interface in the Guinness DSS
enables, and encourages, managers and sales people to use the
system.
25. Slide 25
Chapter
10 Decision Support Systems
Characteristics of DSS
1. Facilitation. DSS facilitate and support specific decision-
making activities and/or decision processes.
2. Interaction. DSS are computer-based systems designed for
interactive use by decision makers or staff users who control the
sequence of interaction and the operations performed.
3. Ancillary. DSS can support decision makers at any level in an
organization. They are NOT intended to replace decision makers.
26. Slide 26
Chapter
10 Decision Support Systems
4. Repeated Use. DSS are intended for repeated use. A specific DSS may be used
routinely or used as needed for ad hoc decision support tasks.
5. Task-oriented. DSS provide specific capabilities that support one or more tasks
related to decision-making, including: intelligence and data analysis;
identification and design of alternatives; choice among alternatives; and decision
implementation.
6. Identifiable. DSS may be independent systems that collect or replicate data
from other information systems OR subsystems of a larger, more integrated
information system.
7. Decision Impact. DSS are intended to improve the accuracy, timeliness, quality
and overall effectiveness of a specific decision or a set of related decisions.
27. Slide 27
Chapter
10 Decision Support Systems
Functions of DSS
1. Information Retrieval:
Information retrieval in DSS environment refers to the act of
extracting information from a database for the purpose of making
decisions.
2. Data Reconfiguration:
Often managers using a DSS want information in a form other
that in which the data are logically represented within the
computer system.
a) Sorting
b) Joining
28. Slide 28
Chapter
10 Decision Support Systems
3. Calculator activities:
Calculator activities refer to the set of tasks that normally can be
done with a calculator.
a) Functions
b) Analysis
c) Statistical Tool
d) Optimizing Tools
e) What-if analysis(Sensitivity Analysis)
29. OLAP (online analytical processing)
OLAP (online analytical processing) is computer processing that enables a user to
easily and selectively extract and view data from different points of view.
OLAP can be used for data mining or the discovery of previously undiscerned
relationships between data items.
An OLAP database does not need to be as large as a data warehouse, since not all
transactional data is needed for trend analysis
31. OLTP (online transaction process)
• OLTP (online transaction process) System deals with operational data. Operational data
are those data involved in the operation of a particular system.
• Example: In a banking System, you withdraw amount through an ATM. Then account
Number, ATM PIN Number, Amount you are withdrawing, Balance amount in account
etc. are operational data elements.
Operational Data
Operational data are local relevance
Frequent Updates
Normalized Tables
Point Query
• Examples for OLTP Queries:
• What is the Salary of Mr.John?
• What is the address and email id of the person who is the head of maths
department?
33. Data mining
• Data mining (the analysis step of the "Knowledge Discovery in Databases"
process, or KDD), is a field at the intersection of computer
science and statistics, is the process that attempts to discover patterns in
large data sets.
• It utilizes methods at the intersection of artificial intelligence, machine
learning, statistics, and database systems
• The overall goal of the data mining process is to extract information from a
data set and transform it into an understandable structure for further use
• The actual data mining task is the automatic or semi-automatic
analysis of large quantities of data to extract previously unknown
interesting patterns such as groups of data records (cluster analysis),
unusual records (anomaly detection) and dependencies
34. Application of data mining
Data understanding
Data preparation
Modeling
Evaluation
Deployment
35. Data warehouse
• A data warehouse (DW or DWH) is a database used
for reporting and data analysis.
• The data stored in the warehouse are uploaded from the operational
systems (such as marketing, sales etc., shown in the figure to the right).
The data may pass through an operational for additional operations before
they are used in the DW for reporting
• A data warehouse constructed from integrated data source systems does
not require ETL, staging databases, or operational data store databases.
The integrated data source systems may be considered to be a part of a
distributed operational data store layer