The data modeling process. The figure illustrates the way data models are developed and used today. A is developed based on the data for the application that is being developed, perhaps in the context of an. The data model will normally consist of entity types, attributes, relationships, integrity rules, and the definitions of those objects. This is then used as the start point for interface or database design.Data modeling in is the process of creating a for an by applying certain formal techniques.
About the Modeling & Analysis in Software Engineering Group Computer Science is a fascinating field. In a relatively short amount of time, computers have impacted almost every aspect of society and enabled applications and uses thought impossible just a few years ago.
Data modeling is a used to define and analyze data needed to support the within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the information system.There are three different types of data models produced while progressing from requirements to the actual database to be used for the information system. The data requirements are initially recorded as a which is essentially a set of technology independent specifications about the data and is used to discuss initial requirements with the business stakeholders. The is then translated into a, which documents structures of the data that can be implemented in databases. Implementation of one conceptual data model may require multiple logical data models. The last step in data modeling is transforming the logical data model to a that organizes the data into tables, and accounts for access, performance and storage details.
Data modeling defines not just data elements, but also their structures and the relationships between them.Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. How data models deliver benefit.Data models provide a framework for to be used within by providing specific definition and format.
If a data model is used consistently across systems then compatibility of data can be achieved. If the same data structures are used to store and access data then different applications can share data seamlessly. The results of this are indicated in the diagram. However, systems and interfaces are often expensive to build, operate, and maintain. They may also constrain the business rather than support it.
This may occur when the quality of the data models implemented in systems and interfaces is poor.Some common problems found in data models are:. Business rules, specific to how things are done in a particular place, are often fixed in the structure of a data model.
This means that small changes in the way business is conducted lead to large changes in computer systems and interfaces. So, business rules need to be implemented in a flexible way that does not result in complicated dependencies, rather the data model should be flexible enough so that changes in the business can be implemented within the data model in a relatively quick and efficient way. Entity types are often not identified, or are identified incorrectly.
This can lead to replication of data, data structure and functionality, together with the attendant costs of that duplication in development and maintenance.Therefore, data definitions should be made as explicit and easy to understand as possible to minimize misinterpretation and duplication. Data models for different systems are arbitrarily different. The result of this is that complex interfaces are required between systems that share data. These interfaces can account for between 25-70% of the cost of current systems. Required interfaces should be considered inherently while designing a data model, as a data model on its own would not be usable without interfaces within different systems. Data cannot be shared electronically with customers and suppliers, because the structure and meaning of data has not been standardised.
To obtain optimal value from an implemented data model, it is very important to define standards that will ensure that data models will both meet business needs and be consistent.Conceptual, logical and physical schemas. The ANSI/SPARC three level architecture. This shows that a data model can be an external model (or view), a conceptual model, or a physical model. This is not the only way to look at data models, but it is a useful way, particularly when comparing models.In 1975 described three kinds of data-model instance:.: describes the semantics of a domain (the scope of the model). For example, it may be a model of the interest area of an organization or of an industry. This consists of entity classes, representing kinds of things of significance in the domain, and relationships assertions about associations between pairs of entity classes. A conceptual schema specifies the kinds of facts or propositions that can be expressed using the model.
In that sense, it defines the allowed expressions in an artificial 'language' with a scope that is limited by the scope of the model. Simply described, a conceptual schema is the first step in organizing the data requirements.: describes the structure of some domain of information. This consists of descriptions of (for example) tables, columns, object-oriented classes, and XML tags. The logical schema and conceptual schema are sometimes implemented as one and the same.: describes the physical means used to store data. This is concerned with partitions, CPUs, and the like.According to ANSI, this approach allows the three perspectives to be relatively independent of each other. Storage technology can change without affecting either the logical or the conceptual schema. The table/column structure can change without (necessarily) affecting the conceptual schema.
In each case, of course, the structures must remain consistent across all schemas of the same data model.Data modeling process. Data modeling in the context of Integration.In the context of (see figure), data modeling complements, and ultimately results in database generation.The process of designing a database involves producing the previously described three types of schemas - conceptual, logical, and physical. The database design documented in these schemas are converted through a, which can then be used to generate a database. A fully attributed data model contains detailed attributes (descriptions) for every entity within it. The term 'database design' can describe many different parts of the design of an overall. Principally, and most correctly, it can be thought of as the logical design of the base data structures used to store the data.
In the these are the. In an the entities and relationships map directly to object classes and named relationships. However, the term 'database design' could also be used to apply to the overall process of designing, not just the base data structures, but also the forms and queries used as part of the overall database application within the or DBMS.In the process, system account for 25% to 70% of the development and support costs of current systems. The primary reason for this cost is that these systems do not share a common data model.
If data models are developed on a system by system basis, then not only is the same analysis repeated in overlapping areas, but further analysis must be performed to create the interfaces between them. Most systems within an organization contain the same basic data, redeveloped for a specific purpose. Therefore, an efficiently designed basic data model can minimize rework with minimal modifications for the purposes of different systems within the organization Modeling methodologiesData models represent information areas of interest. While there are many ways to create data models, according to (1997) only two modeling methodologies stand out, top-down and bottom-up:. Bottom-up models or View Integration models are often the result of a effort. They usually start with existing data structures forms, fields on application screens, or reports. These models are usually physical, application-specific, and incomplete from an.
They may not promote data sharing, especially if they are built without reference to other parts of the organization. Top-down, on the other hand, are created in an abstract way by getting information from people who know the subject area. A system may not implement all the entities in a logical model, but the model serves as a reference point or template.Sometimes models are created in a mixture of the two methods: by considering the data needs and structure of an application and by consistently referencing a subject-area model.
Unfortunately, in many environments the distinction between a logical data model and a physical data model is blurred. In addition, some tools don't make a distinction between logical. Entity relationship diagrams. Example of an Entity relationship diagrams used to model IDEF1X itself. The name of the view is mm. The domain hierarchy and constraints are also given.
The constraints are expressed as sentences in the formal theory of the meta model.There are several notations for data modeling. The actual model is frequently called 'Entity relationship model', because it depicts data in terms of the entities and relationships described in the. An entity-relationship model (ERM) is an abstract conceptual representation of structured data. Entity-relationship modeling is a relational schema method, used in to produce a type of (or ) of a system, often a, and its requirements in a fashion.These models are being used in the first stage of design during the to describe information needs or the type of that is to be stored in a. The technique can be used to describe any (i.e.
An overview and classifications of used terms and their relationships) for a certain i.e. Area of interest.Several techniques have been developed for the design of data models. While these methodologies guide data modelers in their work, two different people using the same methodology will often come up with very different results. Most notable are:.Generic data modeling. Example of a Generic data model.Generic data models are generalizations of conventional.
They define standardized general relation types, together with the kinds of things that may be related by such a relation type.The definition of generic data model is similar to the definition of a natural language. For example, a generic data model may define relation types such as a 'classification relation', being a between an individual thing and a kind of thing (a class) and a 'part-whole relation', being a binary relation between two things, one with the role of part, the other with the role of whole, regardless the kind of things that are related.Given an extensible list of classes, this allows the classification of any individual thing and to specify part-whole relations for any individual object. By standardization of an extensible list of relation types, a generic data model enables the expression of an unlimited number of kinds of facts and will approach the capabilities of natural languages. Conventional data models, on the other hand, have a fixed and limited domain scope, because the instantiation (usage) of such a model only allows expressions of kinds of facts that are predefined in the model.Semantic data modeling. Semantic data models.Therefore, the need to define data from a conceptual view has led to the development of techniques.
That is, techniques to define the meaning of data within the context of its interrelationships with other data. As illustrated in the figure the real world, in terms of resources, ideas, events, etc., are symbolically defined within physical data stores. A semantic data model is an which defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world.A semantic data model can be used to serve many purposes, such as:. planning of data resources. building of shareable databases.
evaluation of vendor software. integration of existing databasesThe overall goal of semantic data models is to capture more meaning of data by integrating relational concepts with more powerful concepts known from the field. The idea is to provide high level modeling primitives as integral part of a data model in order to facilitate the representation of real world situations. ^ Matthew West and Julian Fowler (1999). The European Process Industries STEP Technical Liaison Executive (EPISTLE). ^ Simison, Graeme.
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The Data Model Resource Book. Wiley, 1997. Accessed November 1, 2008. ^ December 3, 2013, at the released of IDEF1X by the Computer Systems Laboratory of the National Institute of Standards and Technology (NIST). December 21, 1993. Amnon Shabo (2006).
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Matthew West (2011) Developing High Quality Data Models.
Definition: The Rapid Application Development (or RAD) model is based on prototyping and iterative model with no (or less) specific planning. In general, RAD approach to software development means putting lesser emphasis on planning tasks and more emphasis on development and coming up with a prototype. In disparity to the waterfall model, which emphasizes meticulous specification and planning, the RAD approach means building on continuously evolving requirements, as more and more learnings are drawn as the development progresses.Description: RAD puts clear focus on prototyping, which acts as an alternative to design specifications. This means that RAD works well wherever there's a greater focus on user interface rather than non-GUI programs.
The RAD model includes agile method and spiral model.Below phases are in rapid application development (RAD) model:1. Business modeling: The information flow is identified between different business functions.2. Data modeling: Information collected from business modeling is used to define data objects that are required for the business.3. Process modeling: Data objects defined in data modeling are converted to establish the business information flow to achieve some specific business objective process descriptions for adding, deleting, modifying data objects that are given.4. Application generation: The actual system is created and coding is done by using automation tools. This converts the overall concept, process and related information into actual desired output. This output is called a prototype as it’s still half-baked.5.
Testing and turnover: The overall testing cycle time is reduced in the RAD model as the prototypes are independently tested during every cycle.However, the overall flow of information, user interfaces and other program interfaces, and coaxials between these interfaces and the rest of data flow need to be tested as per acceptance process. Since most of the programming components have already been tested, it reduces the risk of any critical issue. Definition: Software maintenance is a part of Software Development Life Cycle. Its main purpose is to modify and update software application after delivery to correct faults and to improve performance. Software is a model of the real world. When the real world changes, the software requires alteration wherever possible.Description: Software maintenance is a vast activity which includes optimization, error correction, deletion of discarded features and enhancement of existing features. Since these changes are necessary, a mechanism must be created for estimation, controlling and making modifications.
The essential part of software maintenance requires preparation of an accurate plan during the development cycle. Typically, maintenance takes up about 40-80% of the project cost, usually closer to the higher pole. Definition: Software engineering is a detailed study of engineering to the design, development and maintenance of software. Software engineering was introduced to address the issues of low-quality software projects. Problems arise when a software generally exceeds timelines, budgets, and reduced levels of quality. It ensures that the application is built consistently, correctly, on time and on budget and within requirements. The demand of software engineering also emerged to cater to the immense rate of change in user requirements and environment on which application is supposed to be working.Description: A software product is judged by how easily it can be used by the end-user and the features it offers to the user.
An application must score in the following areas:-1) Operational: -This tells how good a software works on operations like budget, usability, efficiency, correctness,functionality, dependability, security and safety.2) Transitional: - Transitional is important when an application is shifted from one platform to another. So, portability, reusability and adaptability come in this area.3) Maintenance: - This specifies how good a software works in the changing environment. Modularity, maintainability, flexibility and scalability come in maintenance part.Software Development Lifecycle or SDLC is a series of stages in software engineering to develop proposed software application, such as:1) Communication2) Requirement Gathering3) Feasibility Study4) System Analysis5) Software Design6) Coding7) Testing8) Integration9) Implementation10) Operations and maintenance11) DispositionSoftware engineering generally begins with the first step as a user-request initiation for a specific task or an output. He submits his requirement to a service provider organization. The software development team segregates user requirement, system requirement and functional requirements.
The requirement is collected by conducting interviews of a user, referring to a database, studying the existing system etc. After requirement gathering, the team analyses if the software can be made to fulfil all the requirements of the user. The developer then decides a roadmap of his plan. System analysis also includes an understanding of software product limitations. As per the requirement and analysis, a software design is made.
The implementation of software design starts in terms of writing program code in a suitable programming language. Software testing is done while coding by the developers and thorough testing is conducted by testing experts at various levels of code such as module testing, program testing, product testing, in-house testing and testing the product at user’s engagement and feedback. Definition: The Rapid Application Development (or RAD) model is based on prototyping and iterative model with no (or less) specific planning. In general, RAD approach to software development means putting lesser emphasis on planning tasks and more emphasis on development and coming up with a prototype. In disparity to the waterfall model, which emphasizes meticulous specification and planning, the RAD approach means building on continuously evolving requirements, as more and more learnings are drawn as the development progresses.Description: RAD puts clear focus on prototyping, which acts as an alternative to design specifications.
This means that RAD works well wherever there's a greater focus on user interface rather than non-GUI programs. The RAD model includes agile method and spiral model.Below phases are in rapid application development (RAD) model:1.
Business modeling: The information flow is identified between different business functions.2. Data modeling: Information collected from business modeling is used to define data objects that are required for the business.3. Process modeling: Data objects defined in data modeling are converted to establish the business information flow to achieve some specific business objective process descriptions for adding, deleting, modifying data objects that are given.4. Application generation: The actual system is created and coding is done by using automation tools. This converts the overall concept, process and related information into actual desired output. This output is called a prototype as it’s still half-baked.5. Testing and turnover: The overall testing cycle time is reduced in the RAD model as the prototypes are independently tested during every cycle.However, the overall flow of information, user interfaces and other program interfaces, and coaxials between these interfaces and the rest of data flow need to be tested as per acceptance process.
Since most of the programming components have already been tested, it reduces the risk of any critical issue. Definition: Software maintenance is a part of Software Development Life Cycle.
Its main purpose is to modify and update software application after delivery to correct faults and to improve performance. Software is a model of the real world.
When the real world changes, the software requires alteration wherever possible.Description: Software maintenance is a vast activity which includes optimization, error correction, deletion of discarded features and enhancement of existing features. Since these changes are necessary, a mechanism must be created for estimation, controlling and making modifications. The essential part of software maintenance requires preparation of an accurate plan during the development cycle. Typically, maintenance takes up about 40-80% of the project cost, usually closer to the higher pole.