3. Contrast the following terms: a. data dependence, data independence b. struct

Question

3. Contrast the following terms:

a. data dependence, data independence

b. structured data; unstructured data

c. data; information

d. repository; database

e. entity; enterprise data model

f. data warehouse; ERP system

g. two-tier databases; multitier databases

h. systems development lifecycle; prototyping

i. enterprise data model; conceptual data model

j. prototyping; agile software development

Explanation / Answer

a> data dependance mean the data is dependance upon application programme. when we change in data so also change in application program which we use Data independance mean data is indpendant on applicaton programme or vice versa . if we change in data so there is no change in application programme. b> For the most part, structured data refers to information with a high degree of organization, such that inclusion in a relational database is seamless and readily searchable by simple, straightforward search engine algorithms or other search operations; whereas unstructured data is essentially the opposite. The lack of structure makes compilation a time and energy-consuming task. It would be beneficial to a company across all business strata to find a mechanism of data analysis to reduce the costs unstructured data adds to the organization. c> Data is raw, unorganized facts that need to be processed. Data can be something simple and seemingly random and useless until it is organized. When data is processed, organized, structured or presented in a given context so as to make it useful, it is called Information. d>A repository is a special class of database which is designed to store meta-data, that is, data that describes other data. A database is an organized collection of data. The data is typically organized to model relevant aspects of reality (for example, the availability of rooms in hotels), in a way that supports processes requiring this information (for example, finding a hotel with vacancies). e>Enterprise Data Modeling is the practice of creating a graphical model of the data used by an enterprise or company. Typical outputs of this activity include Entity Relationship Diagrams (ERD), XML Schemas (XSD), and an enterprise wide data dictionary. Producing such a model allows for a business to get a 'helicopter' view of their enterprise. In EAI (Enterprise Application Integration) an EDM allows data to be represented in a single idiom, enabling the use of a common syntax for the XML of services or operations and the physical data model for database schema creation. Data Modeling Tools for Entity Relationship Diagrams that also allow the user to create a data dictionary are usually used to aid in the development of an EDM. The implementation of an EDM is closely related to the issues of data governance and data stewardship within an organization. An entity is something that exists by itself, although it need not be of material existence. In particular, abstractions and legal fictions are usually regarded as entities. In general, there is also no presumption that an entity is animate. f>Since the introduction of the term "data warehousing" in 1990, companies have explored the ways they can capture, store and manipulate data for analysis and decision support. At the same time, many companies have been instituting enterprise resource planning (ERP) software to coordinate the common functions of an enterprise. ERP software usually has a central database as its hub, allowing applications to share and reuse data more efficiently than previously permitted by separate applications. The use of ERP has led to an explosion in source data capture, and the existence of a central ERP database has created the opportunity to develop enterprise data warehouses for manipulating that data for analysis. So, ERP systems and data warehouse (DW) systems can be considered complementary environments. ERP vendors have started to include BI capabilities into their ERP systems in an attempt to capitalize on the need to analyze the data in an ERP in addition to or in conjunction with the data found in a company's non-ERP systems. g>1 tier application:All the processing is done on one machines and numbr of clients are attached to this machine (mainframe applications) 2 tier application: Clients and data base on different machines.Clinets are thich clinets i.e. processing is done at client side.Application layer is on Clients. 3 tier application.Client are partially thick.Apart from that there are two more layers application layer and database layer. 4 tier application: Some clients may be totally non thick clients some cliints may be partally thick and further there are 3 layers web layer, application layer and database layer. h> The systems development life cycle (SDLC), or software development process in systems engineering, information systems and software engineering, is a process of creating or altering information systems, and the models and methodologies that people use to develop these systems. In software engineering, the SDLC concept underpins many kinds of software development methodologies. These methodologies form the framework for planning and controlling the creation of an information system:[1] the software development process. Prototyping is the process of building a model of a system. In terms of an information system, prototypes are employed to help system designers build an information system that intuitive and easy to manipulate for end users. Prototyping is an iterative process that is part of the analysis phase of the systems development life cycle. During the requirements determination portion of the systems analysis phase, system analysts gather information about the organization's current procedures and business processes related the proposed information system. In addition, they study the current information system, if there is one, and conduct user interviews and collect documentation. This helps the analysts develop an initial set of system requirements. i>Enterprise Data Modeling is the practice of creating a graphical model of the data used by an enterprise or company. Typical outputs of this activity include Entity Relationship Diagrams (ERD), XML Schemas (XSD), and an enterprise wide data dictionary. Producing such a model allows for a business to get a 'helicopter' view of their enterprise. In EAI (Enterprise Application Integration) an EDM allows data to be represented in a single idiom, enabling the use of a common syntax for the XML of services or operations and the physical data model for database schema creation. Data Modeling Tools for Entity Relationship Diagrams that also allow the user to create a data dictionary are usually used to aid in the development of an EDM. The implementation of an EDM is closely related to the issues of data governance and data stewardship within an organization. A conceptual schema or conceptual data model is a map of concepts and their relationships used for databases. This describes the semantics of an organization and represents a series of assertions about its nature. Specifically, it describes the things of significance to an organization (entity classes), about which it is inclined to collect information, and characteristics of (attributes) and associations between pairs of those things of significance (relationships). j>Software Development Which Flows Alongside Your Ideas Used as an alternative or in addition to formal functional specifications, the prototyping method of requirements capture works well if your software product isn

Related Questions

Navigate

• Browse (All)
• Browse 3
• Subjects

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.