Q What is mean by Decision support system?
Ans:-
A decision support system (DSS) is a computer-based information system 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.
DSSs include knowledge-based systems. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, personal knowledge, or business models to identify and solve problems and make decisions.
Typical information that a decision support application might gather and present are:
inventories of information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),
comparative sales figures between one period and the next,
projected revenue figures based on product sales assumptions
Q 2) History of DSS:-
According to Keen (1978),[1] the concept of decision support has evolved from two main areas of research: The theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology in the 1960s. It is considered that the concept of DSS became an area of research of its own in the middle of the 1970s, before gaining in intensity during the 1980s. In the middle and late 1980s, executive information systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS.
According to Sol (1987)[2] the definition and scope of DSS has been migrating over the years. In the 1970s DSS was described as "a computer based system to aid decision making". Late 1970s the DSS movement started focusing on "interactive computer-based systems which help decision-makers utilize data bases and models to solve ill-structured problems". In the 1980s DSS should provide systems "using suitable and available technology to improve effectiveness of managerial and professional activities", and end 1980s DSS faced a new challenge towards the design of intelligent workstations.[2]
In 1987 Texas Instruments completed development of the Gate Assignment Display System (GADS) for United Airlines. This decision support system is credited with significantly reducing travel delays by aiding the management of ground operations at various airports, beginning with O'Hare International Airport in Chicago and Stapleton Airport in Denver Colorado.[3][4]
Beginning in about 1990, data warehousing and on-line analytical processing (OLAP) began broadening the realm of DSS. As the turn of the millennium approached, new Web-based analytical applications were introduced.
The advent of better and better reporting technologies has seen DSS start to emerge as a critical component of management design. Examples of this can be seen in the intense amount of discussion of DSS in the education environment.
DSS also have a weak connection to the user interface paradigm of hypertext. Both the University of Vermont PROMIS system (for medical decision making) and the Carnegie Mellon ZOG/KMS system (for military and business decision making) were decision support systems which also were major breakthroughs in user interface research. Furthermore, although hypertext researchers have generally been concerned with information overload, certain researchers, notably Douglas Engelbart, have been focused on decision makers in particular.
[edit] Taxonomies
As with the definition, there is no universally-accepted taxonomy of DSS either. Different authors propose different classifications. Using the relationship with the user as the criterion, Haettenschwiler[5] differentiates passive, active, and cooperative DSS. A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. An active DSS can bring out such decision suggestions or solutions. A cooperative DSS allows the decision maker (or its advisor) to modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. The system again improves, completes, and refines the suggestions of the decision maker and sends them back to him for validation. The whole process then starts again, until a consolidated solution is generated.
Another taxonomy for DSS has been created by Daniel Power. Using the mode of assistance as the criterion, Power differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS.[6]
A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove[7]
A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data.
A document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of electronic formats.
A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures.[6]
A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data-intensive. Dicodess is an example of an open source model-driven DSS generator.[8]
Using scope as the criterion, Power[9] differentiates enterprise-wide DSS and desktop DSS. An enterprise-wide DSS is linked to large data warehouses and serves many managers in the company. A desktop, single-user DSS is a small system that runs on an individual manager's PC.
Q Explain Diffenrt Types of Decision Support system?
Ans:-
Decision Support Systems (DSS) are a class of computerized information system that support decision-making activities. DSS are interactive computer-based systems and subsystems intended to help decision makers use communications technologies, data, documents, knowledge and/or models to complete decision process tasks.
A decision support system may present information graphically and may include an expert system or artificial intelligence (AI). It may be aimed at business executives or some other group of knowledge workers.
Typical information that a decision support application might gather and present would be, (a) Accessing all information assets, including legacy and relational data sources; (b) Comparative data figures; (c) Projected figures based on new data or assumptions; (d) Consequences of different decision alternatives, given past experience in a specific context.
There are a number of Decision Support Systems. These can be categorized into five types:
Communication-driven DSS
Most communications-driven DSSs are targetted at internal teams, including partners. Its purpose are to help conduct a meeting, or for users to collaborate. The most common technology used to deploy the DSS is a web or client server. Examples: chats and instant messaging softwares, online collaboration and net-meeting systems.
Data-driven DSS
Most data-driven DSSs are targeted at managers, staff and also product/service suppliers. It is used to query a database or data warehouse to seek specific answers for specific purposes. It is deployed via a main frame system, client/server link, or via the web. Examples: computer-based databases that have a query system to check (including the incorporation of data to add value to existing databases.
Document-driven DSS
Document-driven DSSs are more common, targeted at a broad base of user groups. The purpose of such a DSS is to search web pages and find documents on a specific set of keywords or search terms. The usual technology used to set up such DSSs are via the web or a client/server system. Examples:
Knowledge-driven DSS:
Knowledge-driven DSSs or 'knowledgebase' are they are known, are a catch-all category covering a broad range of systems covering users within the organization seting it up, but may also include others interacting with the organization - for example, consumers of a business. It is essentially used to provide management advice or to choose products/services. The typical deployment technology used to set up such systems could be slient/server systems, the web, or software runnung on stand-alone PCs.
Model-driven DSS
Model-driven DSSs are complex systems that help analyse decisions or choose between different options. These are used by managers and staff members of a business, or people who interact with the organization, for a number of purposes depending on how the model is set up - scheduling, decision analyses etc. These DSSs can be deployed via software/hardware in stand-alone PCs, client/server systems, or the web.
Q explain details History of Decision Support System?
Ans:-Decision Support Systems Origins
In the 1960s, researchers began systematically studying the use of computerized quantitative models to assist in decision making and planning (Raymond, 1966; Turban, 1967; Urban, 1967, Holt and Huber, 1969). Ferguson and Jones (1969) reported the first experimental study using a computer aided decision system. They investigated a production scheduling application running on an IBM 7094. In retrospect, a major historical turning point was Michael S. Scott Morton's (1967) dissertation field research at Harvard University.
Scott Morton’s study involved building, implementing and then testing an interactive, model-driven management decision system. Fellow Harvard Ph.D. student Andrew McCosh asserts that the “concept of decision support systems was first articulated by Scott Morton in February 1964 in a basement office in Sherman Hall, Harvard Business School” (McCosh email, 2002) in a discussion they had about Scott Morton’s dissertation. During 1966, Scott Morton (1971) studied how computers and analytical models could help managers make a recurring key business planning decision. He conducted an experiment in which managers actually used a Management Decision System (MDS). Marketing and production managers used an MDS to coordinate production planning for laundry equipment. The MDS ran on an IDI 21 inch CRT with a light pen connected using a 2400 bps modem to a pair of Univac 494 systems.
The pioneering work of George Dantzig, Douglas Engelbart and Jay Forrester likely influenced the feasibility of building computerized decision support systems. In 1952, Dantzig became a research mathematician at the Rand Corporation, where he began implementing linear programming on its experimental computers. In the mid-1960s, Engelbart and colleagues developed the first hypermedia—groupware system called NLS (oNLine System). NLS facilitated the creation of digital libraries and the storage and retrieval of electronic documents using hypertext. NLS also provided for on-screen video teleconferencing and was a forerunner to group decision support systems. Forrester was involved in building the SAGE (Semi-Automatic Ground Environment) air defense system for North America completed in 1962. SAGE is probably the first computerized data-driven DSS. Also, Professor Forrester started the System Dynamics Group at the Massachusetts Institute of Technology Sloan School. His work on corporate modeling led to programming DYNAMO, a general simulation compiler.
In 1960, J.C.R. Licklider published his ideas about the future role of multiaccess interactive computing in a paper titled “Man-Computer Symbiosis.” He saw man-computer interaction as enhancing both the quality and efficiency of human problem solving and his paper provided a guide for decades of computer research to follow. Licklider was the architect of Project MAC at MIT that furthered the study of interactive computing.
By April 1964, the development of the IBM System 360 and other more powerful mainframe systems made it practical and cost-effective to develop Management Information Systems (MIS) for large companies (cf., Davis, 1974). These early MIS focused on providing managers with structured, periodic reports and the information was primarily from accounting and transaction processing systems, but the systems did not provide interactive support to assist managers in decision making.
Around 1970 business journals started to publish articles on management decision systems, strategic planning systems and decision support systems (cf., Sprague and Watson 1979).. For example, Scott Morton and colleagues McCosh and Stephens published decision support related articles in 1968. The first use of the term decision support system was in Gorry and Scott-Morton’s (1971) Sloan Management Review article. They argued that Management Information Systems primarily focused on structured decisions and suggested that the supporting information systems for semi-structured and unstructured decisions should be termed “Decision Support Systems”.
T.P. Gerrity, Jr. focused on Decision Support Systems design issues in his 1971 Sloan Management Review article titled "The Design of Man-Machine Decision Systems: An Application to Portfolio Management". The article was based on his MIT Ph.D. dissertation. His system was designed to support investment managers in their daily administration of a clients' stock portfolio.
John D.C. Little, also at Massachusetts Institute of Technology, was studying DSS for marketing. Little and Lodish (1969) reported research on MEDIAC, a media planning support system. Also, Little (1970) identified criteria for designing models and systems to support management decision-making. His four criteria included: robustness, ease of control, simplicity, and completeness of relevant detail. All four criteria remain relevant in evaluating modern Decision Support Systems. By 1975, Little was expanding the frontiers of computer-supported modeling. His DSS called Brandaid was designed to support product, promotion, pricing and advertising decisions. Little also helped develop the financial and marketing modeling language known as EXPRESS.
In 1974, Gordon Davis, a Professor at the University of Minnesota, published his influential text on Management Information Systems. He defined a Management Information System as "an integrated, man/machine system for providing information to support the operations, management, and decision-making functions in an organization. (p. 5)." Davis's Chapter 12 was titled "Information System Support for Decision Making" and Chapter 13 was titled "Information System Support for Planning and Control". Davis’s framework incorporated computerized decision support systems into the emerging field of management information systems.
Peter Keen and Charles Stabell claim the concept of decision support systems evolved from "the theoretical studies of organizational decisionmaking done at the Carnegie Institute of Technology during the late 1950s and early '60s and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology in the 1960s. (Keen and Scott Morton, 1978)". Herbert Simon’s books (1947, 1960) and articles provide a context for understanding and supporting decision making.
In 1995, Hans Klein and Leif Methlie noted “A study of the origin of DSS has still to be written. It seems that the first DSS papers were published by PhD students or professors in business schools, who had access to the first time-sharing computer system: Project MAC at the Sloan School, the Dartmouth Time Sharing Systems at the Tuck School. In France, HEC was the first French business school to have a time-sharing system (installed in 1967), and the first DSS papers were published by professors of the School in 1970. (p. 112).”
III. Theory Development
In the mid- to late 1970s, both practice and theory issues related to DSS were discussed at academic conferences including the American Institute for Decision Sciences meetings and the ACM SIGBDP Conference on Decision Support Systems in San Jose, CA in January 1977 (the proceeding were included in the journal Database). The first International Conference on Decision Support Systems was held in Atlanta, Georgia in 1981. Academic conferences provided forums for idea sharing, theory discussions and information exchange.
At about this same time, Keen and Scott Morton’s DSS textbook (1978) provided the first broad behavioral orientation to decision support system analysis, design, implementation, evaluation and development. This influential text provided a framework for teaching DSS in business schools. McCosh and Scott-Morton’s (1978) DSS book was more influential in Europe.
In 1980, Steven Alter published his MIT doctoral dissertation results in an influential book. Alter's research and papers (1975; 1977) expanded the framework for thinking about business and management DSS. Also, his case studies provided a firm descriptive foundation of decision support system examples. A number of other MIT dissertations completed in the late 1970s also dealt with issues related to using models for decision support.
Alter concluded from his research (1980) that decision support systems could be categorized in terms of the generic operations that can be performed by such systems. These generic operations extend along a single dimension, ranging from extremely data-oriented to extremely model-oriented. Alter conducted a field study of 56 DSS that he categorized into seven distinct types of DSS. His seven types include:
· File drawer systems that provide access to data items.
· Data analysis systems that support the manipulation of data by computerized tools tailored to a specific task and setting or by more general tools and operators.
· Analysis information systems that provide access to a series of decision-oriented databases and small models.
· Accounting and financial models that calculate the consequences of possible actions.
· Representational models that estimate the consequences of actions on the basis of simulation models.
· Optimization models that provide guidelines for action by generating an optimal solution consistent with a series of constraints.
· Suggestion models that perform the logical processing leading to a specific suggested decision for a fairly structured or well-understood task.
Donovan and Madnick (1977) classified DSS as institutional or ad hoc. Institutional DSS support decisions that are recurring. An ad hoc DSS supports querying data for one time requests. Hackathorn and Keen (1981) identified DSS in three distinct yet interrelated categories: Personal DSS, Group DSS and Organizational DSS.
In 1979, John Rockart of the Harvard Business School published a ground breaking article that led to the development of executive information systems (EISs) or executive support systems (ESS). Rockart developed the concept of using information systems to display critical success metrics for managers.
Robert Bonczek, Clyde Holsapple, and Andrew Whinston (1981) explained a theoretical framework for understanding the issues associated with designing knowledge-oriented Decision Support Systems. They identified four essential "aspects" or general components that were common to all DSS: 1. A language system (LS) that specifies all messages a specific DSS can accept; 2. A presentation system (PS) for all messages a DSS can emit; 3. A knowledge system (KS) for all knowledge a DSS has; and 4. A problem-processing system (PPS) that is the "software engine" that tries to recognize and solve problems during the use of a specific DSS. Their book explained how Artificial Intelligence and Expert Systems technologies were relevant to developing DSS.
Finally, Ralph Sprague and Eric Carlson’s (1982) book Building Effective Decision Support Systems was an important milestone. Much of the book further explained the Sprague (1980) DSS framework of data base, model base and dialog generation and management software. Also, it provided a practical, and understandable overview of how organizations could and should build DSS. Sprague and Carlson (1982) defined DSS as "a class of information system that draws on transaction processing systems and interacts with the other parts of the overall information system to support the decision-making activities of managers and other knowledge workers in organizations (p. 9).”
Q Explain Differnt Types of Componenet of Decision Support system?
Ans:-Components
Design of a Drought Mitigation Decision Support System.Three fundamental components of a DSS architecture are:[5][6][10][11][12]
1.the database (or knowledge base),
2.the model (i.e., the decision context and user criteria), and
3.the user interface.
The users themselves are also important components of the architecture.[5][12]
[edit] Development FrameworksDSS systems are not entirely different from other systems and require a structured approach. Such a framework includes people, technology, and the development approach.[10]
DSS technology levels (of hardware and software) may include:
1.The actual application that will be used by the user. This is the part of the application that allows the decision maker to make decisions in a particular problem area. The user can act upon that particular problem.
2.Generator contains Hardware/software environment that allows people to easily develop specific DSS applications. This level makes use of case tools or systems such as Crystal, AIMMS, and iThink.
3.Tools include lower level hardware/software. DSS generators including special languages, function libraries and linking modules
An iterative developmental approach allows for the DSS to be changed and redesigned at various intervals. Once the system is designed, it will need to be tested and revised for the desired outcome.
[edit] ClassificationThere are several ways to classify DSS applications. Not every DSS fits neatly into one category, but may be a mix of two or more architectures.
Holsapple and Whinston[13] classify DSS into the following six frameworks: Text-oriented DSS, Database-oriented DSS, Spreadsheet-oriented DSS, Solver-oriented DSS, Rule-oriented DSS, and Compound DSS.
A compound DSS is the most popular classification for a DSS. It is a hybrid system that includes two or more of the five basic structures described by Holsapple and Whinston.[13]
The support given by DSS can be separated into three distinct, interrelated categories[14]: Personal Support, Group Support, and Organizational Support.
DSS components may be classified as:
1.Inputs: Factors, numbers, and characteristics to analyze
2.User Knowledge and Expertise: Inputs requiring manual analysis by the user
3.Outputs: Transformed data from which DSS "decisions" are generated
4.Decisions: Results generated by the DSS based on user criteria
DSSs which perform selected cognitive decision-making functions and are based on artificial intelligence or intelligent agents technologies are called Intelligent Decision Support Systems (IDSS).[citation needed]
The nascent field of Decision engineering treats the decision itself as an engineered object, and applies engineering principles such as Design and Quality assurance to an explicit representation of the elements that make up a decision.
Q:- Explain Different Types of Application of the DSS?
ANs:-
As mentioned above, there are theoretical possibilities of building such systems in any knowledge domain.
One example is the clinical decision support system for medical diagnosis. Other examples include a bank loan officer verifying the credit of a loan applicant or an engineering firm that has bids on several projects and wants to know if they can be competitive with their costs.
DSS is extensively used in business and management. Executive dashboard and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources.
A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package,[15][16] developed through financial support of USAID during the 80's and 90's, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels. There are, however, many constraints to the successful adoption on DSS in agriculture.[17]
DSS are also prevalent in forest management where the long planning time frame demands specific requirements. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. A comprehensive list and discussion of all available systems in forest management is being compiled under the COST action Forsys
A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, CN managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.
[edit] Benefits1.Improves personal efficiency
2.Speed up the process of decision making
3.Increases organizational control
4.Encourages exploration and discovery on the part of the decision maker
5.Speeds up problem solving in an organization
6.Facilitates interpersonal communication
7.Promotes learning or training
8.Generates new evidence in support of a decision
9.Creates a competitive advantage over competition
10.Reveals new approaches to thinking about the problem space
11.Helps automate managerial processes
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Q ) Explain the Datawarehouse Definition?
This Data Warehousing site aims to help people get a good high-level understanding of what it takes to implement a successful data warehouse project. A lot of the information is from my personal experience as a business intelligence professional, both as a client and as a vendor.
This site is divided into five main areas.
- Tools: The selection of business intelligence tools and the selection of the data warehousing team. Tools covered are:
Database, Hardware
ETL (Extraction, Transformation, and Loading)
OLAP
Reporting
Metadata
- Steps: This selection contains the typical milestones for a data warehousing project, from requirement gathering, query optimization, to production rollout and beyond. I also offer my observations on the data warehousing field.
- Business Intelligence: Business intelligence is closely related to data warehousing. This section discusses business intelligence, as well as the relationship between business intelligence and data warehousing.
- Concepts: This section discusses several concepts particular to the data warehousing field. Topics include:
Dimensional Data Model
Star Schema
Snowflake Schema
Slowly Changing Dimension
Conceptual Data Model
Logical Data Model
Physical Data Model
Conceptual, Logical, and Physical Data Model
Data Integrity
What is OLAP
MOLAP, ROLAP, and HOLAP
Bill Inmon vs. Ralph Kimball
- Business Intelligence Conferences: Lists upcoming conferences in the business intelligence / data warehousing industry.
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Q) Explain the Business intelligence ?
Ans:-
Business intelligence is a term commonly associated with data warehousing. In fact, many of the tool vendors position their products as business intelligence software rather than data warehousing software. There are other occasions where the two terms are used interchangeably. So, exactly what is business inteligence?
Business intelligence is a term commonly associated with data warehousing. In fact, many of the tool vendors position their products as business intelligence software rather than data warehousing software. There are other occasions where the two terms are used interchangeably. So, exactly what is business inteligence?
Business intelligence usually refers to the information that is available for the enterprise to make decisions on. A data warehousing (or data mart) system is the backend, or the infrastructural, component for achieving business intellignce. Business intelligence also includes the insight gained from doing data mining analysis, as well as unstrctured data (thus the need fo content management systems). For our purposes here, we will discuss business intelligence in the context of using a data warehouse infrastructure.
This section includes the following:
Business intelligence tools: Tools commonly used for business intelligence.
Business intelligence uses: Different forms of business intelligence.
Business intelligence news: News in the business intelligence area.
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Q) Differnt Models While Designing a Database?
Ans:- After all business requirements have been gathered for a proposed database, they must be modeled. Models are created to visually represent the proposed database so that business requirements can easily be associated with database objects to ensure that all requirements have been completely and accurately gathered. Different types of diagrams are typically produced to illustrate the business processes, rules, entities, and organizational units that have been identified. These diagrams often include entity relationship diagrams, process flow diagrams, and server model diagrams. An entity relationship diagram (ERD) represents the entities, or groups of information, and their relationships maintained for a business. Process flow diagrams represent business processes and the flow of data between different processes and entities that have been defined. Server model diagrams represent a detailed picture of the database as being transformed from the business model into a relational database with tables, columns, and constraints. Basically, data modeling serves as a link between business needs and system requirements.
Two types of data modeling are as follows:
Logical modeling
Physical modeling
If you are going to be working with databases, then it is important to understand the difference between logical and physical modeling, and how they relate to one another. Logical and physical modeling are described in more detail in the following subsections.
Logical Modeling
Logical modeling deals with gathering business requirements and converting those requirements into a model. The logical model revolves around the needs of the business, not the database, although the needs of the business are used to establish the needs of the database. Logical modeling involves gathering information about business processes, business entities (categories of data), and organizational units. After this information is gathered, diagrams and reports are produced including entity relationship diagrams, business process diagrams, and eventually process flow diagrams. The diagrams produced should show the processes and data that exists, as well as the relationships between business processes and data. Logical modeling should accurately render a visual representation of the activities and data relevant to a particular business.
Typical deliverables of logical modeling include
Entity relationship diagrams
An Entity Relationship Diagram is also referred to as an analysis ERD. The point of the initial ERD is to provide the development team with a picture of the different categories of data for the business, as well as how these categories of data are related to one another.
Business process diagrams
The process model illustrates all the parent and child processes that are performed by individuals within a company. The process model gives the development team an idea of how data moves within the organization. Because process models illustrate the activities of individuals in the company, the process model can be used to determine how a database application interface is design.
User feedback documentation
Physical Modeling
Physical modeling involves the actual design of a database according to the requirements that were established during logical modeling. Logical modeling mainly involves gathering the requirements of the business, with the latter part of logical modeling directed toward the goals and requirements of the database. Physical modeling deals with the conversion of the logical, or business model, into a relational database model. When physical modeling occurs, objects are being defined at the schema level. A schema is a group of related objects in a database. A database design effort is normally associated with one schema.
During physical modeling, objects such as tables and columns are created based on entities and attributes that were defined during logical modeling. Constraints are also defined, including primary keys, foreign keys, other unique keys, and check constraints. Views can be created from database tables to summarize data or to simply provide the user with another perspective of certain data. Other objects such as indexes and snapshots can also be defined during physical modeling. Physical modeling is when all the pieces come together to complete the process of defining a database for a business.
Physical modeling is database software specific, meaning that the objects defined during physical modeling can vary depending on the relational database software being used. For example, most relational database systems have variations with the way data types are represented and the way data is stored, although basic data types are conceptually the same among different implementations. Additionally, some database systems have objects that are not available in other database systems.
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