RESOURCE MANAGEMENT DATA - DATABASES - DESIGN - MODELS DATA - DATA DICTIONARY - TRENDS PART 2.
MODELS OF DATABASES.
Just as there are many ways to structure business organizations, there are many ways to structure data that these organizations need. The administrator database system separates the logical view of physical data, which means that the developer and the end user does not need to know where and how data is actually stored. In the logical structure of a data Dase, businesses need to consider the characteristics of the data and how to access that same will have.
There are three basic models for structuring a database:
HIERARCHICAL MODEL:
Rigidly structuring related data on a “tree” invested in the records contain two elements:
A simple root or a golf teacher, often called key.
A variable number of subordinate fields defining other record data.
As a rule, the all fields only have a “father”, every third parent can many “children.” The biggest advantage is the speed and efficiency with which the data search is performed; disadvantage.
NETWORK MODEL
Create relationships between data through a list structure in which subordinates records can be linked to more of a “father”. The ratio is called set. Its advantage is that no restrictions on the number of relationships for a field, but in turn the network databases are very complex because when increasing the number of design and implementation relationships are more complicated.
Relational Model.
Traditional organization charts of columns and rows. Commonly used for accounting and financial data. The tables are called relations, called TUPLA row and column attribute. Its advantage is the conceptual simplicity therefore is flexible for end users. Unlike the hierarchical and network models, all data within a table and between tables can be linked, linked and compared.
DATABASE object-oriented.
They are also called MULTIMEDIA DATABASES and managed by special managed systems. Here data including pictures, drawings, documents, maps, video, and photos and others are handled.
They are especially used in the industries of newspapers, television and computer integrated manufacturing.
CREATION OF DATABASES
CONCEPTUAL DESIGN: Abstract model of the database from the perspective of the user or business.
PHYSICAL DESIGN: It shows how the database is really organized storage devices Shortcut
DATA DICTIONARY
It is the third element of a database administrator System, it is a file that stores definitions of data elements and features as the use, physical representation, ownership, authorization and security.
Also it refers to the data element (representing a field) on specific systems and identifies individuals, business functions, applications and reports that use the item.
CURRENT TRENDS IN DATABASES
For current applications database functions that are required to store, retrieve and process various means and not only text and numbers.
DATA WAREHOUSE: Data warehouses. Useful for companies that store increasing amounts of information, building huge data warehouse organized to allow access to end users.
MULTIMEDIA DATABASES Additional database that allows end users to quickly retrieve and present complex data including many dimensions.
DATA WAREHOUSE: Its benefits are the ability to obtain data quickly and easily, since they are located in one place.
The AD, allow storage of metadata, including data summaries that are easier to find, especially with Web tools
It handles a subset of them called duplicate data market and is dedicated to a functional or regional area in AD.
DATA STORE FEATURES:
Organization
Consistency
Variability in time.
No Volatility
Relationality
Client / Server.
DATA MINING
It relates to the general term as mining process requires choosing between a huge amount of material or intelligent inquiry to find exactly where the value of information in the data resides.
Other names given to this method are knowledge extraction, data Paleo Archaeology data processing data patterns, data dredging and Harvest Information
The technology of data mining can generate new business opportunities by providing these features:
Automatic prediction of trends and behaviors. Data mining automates the process of determining predicted information in large databases. For example, data mining can take past data and identify targets that are more likely to respond to developments now; this in the case of marketing.
Automated discovery of previously unknown patterns. The data mining tools identify previously hidden patterns in one step. For example, in this process patterns are discovered in the analysis of retail sales data to identify seemingly unrelated products.
CHARACTERISTICS AND OBJECTIVES OF DATA MINING
Usually the data is in the depths of the databases, which sometimes is there for several years.
The data mining tools help to extract the ore from the corporate information buried in files or archived public records.
The “mining” is often an end user with little or no programming skills empowered mining tools that allow you to investigate for ad hoc questions and get answers quickly.
“Scavenging and Shake” means the discovery of new data and information.
Data mining produces 5 types of information: associations, sequences, classifications, groupings and forecasts.
TECHNICAL DATA MINING
CBR: This method uses historical cases to recognize patterns.
Neural Computation: It is a machine learning method through which historical data is examined to recognize patterns which must be used for forecasting and decision support.
Intelligent Agents: It is the technique used to retrieve information from the Internet or intranets databases.
APPLICATIONS
Retail and Sales, Banking, Manufacturing & Production, Brokerage and securities trading, insurance, computer hardware and software, research, state functions, Airlines, Health, Transmission and Marketing.