Designing And Analysing Data

By Author: Anthony W Bills
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Designing and Analysing Data
Introduction
A model is any representation of something else. It may be physical e.g. a toy car representing a real car or it can be conceptual, only represented on paper to bring a good description of the operation of the real object. "A data model is a way finding tool for both business and IT professionals, which uses a set of symbols and text to precisely explain a subset of real information to improve communication within the organization and thereby lead to a more flexible and stable application environment." (Singh 2009) .In software engineering a data model is an abstract representation of facts and how those facts can be accessed. By examining a data model a certain structure comes out, depending on the type of data being represented and retrieved; all this leads to structured data. Data modeling structure is used in design of database models and information systems. We have different types of data model structures in relation to their contribution in data base development; these are relational model, hierarchical model and the network model (Kimbal 2005).
A data model ...
... is a representation of a system used by professionals using a set of symbols and text to explain real information to improve communication within the organization and thereby lead to a more flexible and stable application environment.(Singh 2009) .In software development, a data model is a representation of information and how that information can be can be accessed. By looking at a data model a certain structure comes out, depending on the type of information being represented and retrieved; all this leads to structured data. Data modeling structure is used in design of database models and data models. We have different types of data model structures in relation to their contribution in data base development; these are relational model, hierarchical model and the network model (Kimbal 2005). A model can be defined as a representation of a real object of system. It may be physical or conceptual, i.e. only represented on paper to bring a good description of the operation of the real object or system.
Data Mining
This is the process of obtaining patterns from a data set in a bid to transform the data into processed information. It is commonly used in a wide range of profiling practices. Data mining can be used to uncover patterns in data sets but is often carried out only on samples of data within a population. The mining process will be ineffective if the samples are not a good representation of the larger body of data. Data mining cannot discover patterns that may be present in the larger body of data if those patterns are not present in the sample being considered. Inability to find patterns may become a cause for some disputes between customers and service providers. Therefore data mining is not foolproof but may be useful if sufficiently representative data samples are collected. The discovery of a particular pattern in a particular set of data does not necessarily mean that a pattern is found elsewhere in the larger data from which that sample was drawn. An important part of the process is the validation of patterns on other samples of data. Other terms used to refer to data mining are data dredging, data fishing and data snooping. These terms are however used mostly to refer to samples which are too small for statistical inference.

Data Models
Data models are used to support information and computer systems in providing the definition and format of raw information. If this is done consistently across systems then the information obtained can be compatible. If the same data structures are used to store and access data then different applications can share the same set of data in the database storage. The results of this are indicated in the diagram above. However, systems and often cost more than they should, to build, operate, and maintain. They may also constrain the business rather than support it. In business term, information is 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.
Conceptual scheme describes the operation of a domain in a data model such a model of the interest area of an organization or industry. This consists of category classes, representing kinds of things of significance in the domain, and relationships assertions about associations between pairs of entity classes. A conceptual scheme specifies the kinds of facts or propositions that can be expressed using the model. In that sense, it defines the allowed expressions in a language used in the model.
Logical Schema
This describes the semantics, as represented by a particular data manipulation technology. This consists of descriptions of tables and columns, object oriented classes, and XML tags, among other things.
Physical Schema
This describes the physical means by which data are stored. This is concerned with partitions, CPUs, tablespaces, and the like. The significance of this approach, according to ANSI, is that it allows the three perspectives to be relatively independent of each other. Storage technology can change without affecting either the logical or the conceptual model. The table/column structure can change without (necessarily) affecting the conceptual model. In each case, of course, the structures must remain consistent with the other model. The table/column structure may be different from a direct translation of the entity classes and attributes, but it must ultimately carry out the objectives of the conceptual entity class structure. Early phases of many software development projects emphasize the design of a conceptual data modeling. Such a design can be detailed into a logical data modeling. In later stages, this model may be translated into physical data modeling. However, it is also possible to implement a conceptual model directly.

Conclusion
The design and implementation of Sport_Doping_Therapy database was a major success and was completed within the appropriate time. Despite the fact of gathering the appropriate software and researching on relevant technology this exercise was completed well. The database was implemented using a relational model and normalized to the 3rd Normal form. A good relationship between the entities was developed fulfilling the business objectives of the project. The use of structured query language and PLSQL aided in the testing of the database to ensure it meets all the integrity tests. Therefore it can be seen clearly through proper planning and using the appropriate design and testing techniques database designers will be able to deliver high quality databases, which is core to any application development.
Works Cited
Connolly, H. 2005, Database systems: a practical approach to design, implementation, Carswel, Toronto.
Inmon, W. 2006, Building the Data Warehouse, ACM, New York.
Kimball, R. 2005, The Data Warehouse Toolkit: The Complete Guide, Addison-Wesley, New York.
Nagabhushana, S. 2006, Data Warehousing Olap and Data Mining, Apple Press Ltd, London.
Pratt, P. 1985, A relational approach to database design, ACM, New York.
Singh, K. 2009, Database Systems: Concepts, Design and Application, American Pie, London.

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