Free tutorial

2012-08-21T00:34:00.001+05:30

no help from any one ... useless to put posts :S

What is a Data warehouse? What is the purpose of a data warehouse?

2012-07-23T23:02:00.001+05:30

A data warehouse can define as a repository that can be accessed to retrieve information of an organization's electronically stored data, designed to facilitate reporting and analysis.

History:

When we go to the history of data warehouse we can define the concept of data warehousing dates back to the late 1980s .The concept of data warehousing was reviled when IBM researchers Barry Devlin and Paul Murphy developed the business data warehouse. In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments.

Overview:

Data warehouse is a subject-oriented, integrated, time-variant, non-volatile collections of data used to support analytical decision making. When we further explore we can identify that data warehouses are physically separated from operational systems, even though the operational systems feed the Warehouse with source data. Anywhere Data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. We can demonstrates how the data warehouse serves as a facilitator to retrieve data from any source such as independent databases and their external interfaces as follows.

Purpose:

When we go though the data warehouse we can identify following purposes,

Multiple angles:This allows viewing data from multiple angles (dimensions). We can identify this as one of the best aspects of the Data warehouse.

Providing an adaptive and flexible source of information: Data Warehouse has the capabilities to adapt quickly to the changing requirements. So it is really easy to handle the necessary changers when it needed. That’s why we can say that it is adaptive and flexible metherod for the source of information

Establish the foundation for Decision Support: We all know that decisioning process of an organization will involve analysis, data mining, forecasting, decision modeling etc. So by having a common point that allows to provide consistent, quality data with high response time provides the core enabler for making fast and informed decisions.

All systems into one environment: We all know that in enterprise environment which may have many systems of record. The data warehouse allows to aggregate all systems into one environment.

Keeping Analysis/Reporting and Production Separate: Data ware aids to keep analysis/reporting (non-production use data) separate from production data. So this can identify as one of purpose of implementing a data warehouse

Data Consistency and Quality :It is common that organizations are riddled with different types of important systems from which their information comes. Each of these systems may carry the information in different formats and also may be having out of synch information. So by bringing the data from these disparate sources at a common place, one can effectively undertake to bring the uniformity and consistency in data

Operational database

2012-07-23T22:59:00.003+05:30

Operational Database can simply define as a database of record, consisting of system specific reference data and event data belonging to a transaction update system which contains enterprise data which are up to date and modifiable.So this is the source of data for the data warehouse. When we further go more detail study about the optional database we can identify that operational database contains detailed data used to run the day to day operations of the business. That's why we can say that it contain data which as up to data as the name implies.

So, operational database, capturing real time data and supplying data for real time computations and other analyzing processes.

--Advantages

There are number of advantages of using an operational database. We can identify those advantages as follows,

System-specific reference data and event data belonging to a transaction-update system
Contain system control and detailed data used to run the day-to-day operations
Data are up to date and data are modifiable
Enterprise data

What is Data mining?

2012-07-23T22:59:00.001+05:30

Data mining can define as a process of extracting patterns from data. So it is the drawing out of hidden predictive data from huge databases. Data mining derives its name from the similarities between searching for valuable information in a large database.

We can identify data mining is as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. Because it is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Long process of research and product development Data mining techniques are become results. Data mining has become increasingly common in both the public and private sectors. Organizations use data mining as a tool to survey customer information, reduce fraud and waste, and assist in medical research.

Data mining commonly involving tasks:

When we move for a detail study about the data mining, we can identify that data mining commonly involves four classes of tasks as follows,

Clustering :

It is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.

Classification:

It is the task of generalizing known structure to apply to new data.

Regression:

This is the attempts to find a function which models the data with the least error.

Association rule learning :

Searches for relationships between variables.

Mixed strategy database design approach

2012-07-23T22:57:00.005+05:30

This database design approach uses both the bottom-up and top-down approach intend of following any particular approach for various parts of the data model before finally combining all parts together. So the requirements are portioned according to a top-down approach and part of the schema is designed for each partition according to a bottom-up approach. Then after as the final phase all the schema parts are combined together.

Inside-out database design approach

2012-07-23T22:57:00.003+05:30

The inside-out database design approach can define as a special case of a bottom-up approach. This approach starts with the identification of set of major entities. Then after it spreading out to consider other entities, relationships and attributes associated with those first identifies. In this approach attention is focused at a central set of concepts that are most evident and then spreading outward by considering others in the vicinity of existing ones.

Why top-down database design approach is important?

2012-07-23T22:57:00.001+05:30

In top-down approach, it helpful to visualize the system. This visualization of the system use to represent the idea behind the particular system and this will really helpful to identify the system and understand the related system. That's why we can say that top down approach is a really good way for the representation.

Why bottom-up database design approach is important?

2012-07-23T22:56:00.001+05:30

The bottom-up approach starts with the data and goes on to develop the structure around it. Here the approach can use to avoid the duplicate and redundant data. We all know that if some piece of data is duplicated several places in the database, there is the risk that it is updated in one place but not the other. This will lead for data corruption. In the same time this will aids to prevent such anomalies while ensuring the integrity of data in the database. So we can say that bottom-up database design approach is really important.

Bottom-up database design approach:

2012-07-23T22:48:00.001+05:30

When we go through the different approaches available to design a database, we can identify bottom-up database design approach as the other main approach available for database design. In bottom-up database design first defines the required attributes and then groups the attributes to form entities.

So this diagram represent the bottom-up approach database design approach starts at the fundamental level of attributes(or abreactions) that is properties of entities and relationships. Then after we can see that it combines or add to these abreactions which are grouped into relations that represent types of entities and relationship between entities.

Example:

We can identify the normalization process as an example for bottom up approach to database design. Here in this process of generalizing entity types into higher-level generalized supper classes is amount example of bottom-up approach. We can further describe by taking a table, we can use the normalization steps to split the one table up to multiple tables

--Advantages

When we go through the data model we can identify different types of advantages. We can summarized the main advantages of this as follows,

Prevent anomalies.
reduce the redundancies
Ensure the integrity

Top-down database design approach

2012-07-23T22:47:00.001+05:30

When we go through the different approaches available to design a database, we can identify top-down
design as a one main approach available for database design. When we go through the different approaches available to design a database, we can identify top-down
design as a one main approach available for database design. This approach begins by identifying the different entity types and the definition of each entity's attributes.

Mainly top-down database approach starts with the development of data model or schemas that contains high-level abstractions. This approach helps database designers to get a good overview over the customers business, the business practices, business rules and reporting requirements.

Example:

We can say that Entity relationship diagram or model(ERD) is an example of top-down approach and is more suitable for the design of compiled databases. The process of specialization to refine an entity type into subclasses is another example of the top-down database design approach

--Advantages

When we go through the data model we can identify different types of advantages. We can summarized the main advantages of this as follows,

visualization of the system

Aids to identify the system

Aids to represent the idea behind the system

Database development life cycle

2012-07-23T22:46:00.001+05:30

Database development life cycle is a part of the software development life cycle. Here in database development life cycle there are number of stages that we can identify. We can present those stages as follows,

Initial database study:

This phase that we identify the requirements that are related to the implementation of database. So here in this phase we determine what is required, what kind of a database is needed for the organization, what is the volume of the data must be handled, how much data is to be stored in the master files etc.

Database design:

In this phase database designers make decision on the database model that is perfectly suited for the organizations requirements. To meet this database designers study the documents prepared by the analysts in the requirements analysis stage and then start developing a system that fulfills the needs.

When we go for the further detail study of the Database design we can identify that it starts with a conceptual data model and produces a specification of a logical schema. However the relational representation is still independent of any specific Database management system (DBMS). So it is another conceptual data model

Implementation and data model:

This is the phase that the database system is implemented. This involves a series of steps leading to operational information system that includes creating database definitions, developing applications, testing the system, developing operational procedures and documentation, training the users and populating the database

Testing and evaluation:

Here in this phase the
aim of testing is to uncover errors in the design and implementation of the database, its structure, constraints and associated user and management support.

This considered involving validation and verification. Without adequate testing users will have little confidence in their data processing.

Operation:

In this phase database is accessed by the users and application programs, new data is added as well as the existing data is modified and some obsolete data is deleted also database delivers its usefulness as a critical tool in management decision making and help in the smooth and efficient functioning of the organization.

Maintenance and monitoring:

We can identify this phase as one of the ongoing phases in database development life cycle. Database backup and recovery, performance tuning, design modifications, access management and audits, usage monitoring, hardware maintenance and upgradation can identify as major tasks that are carryout during this process.

The documentations produced for each stage of the database development life cycle

2012-07-23T22:45:00.001+05:30

When we go through the detail study of the database development life cycle we can identify that throughout the database system development lifecycle the database developer needs to capture facts about the current and or future system.

So we can see that to full this requirement number of documentations are produced for each stage during the database development life cycle. We can summarize the documentations that are implement during each pages as follows,

Phase	Documentation
Initial database study	Mission statements and objectives of database system Definition of scope and boundry of database application Definition of user views to be supported User and system requiremets specification
Database design	Conceptual/logical database design ER model(s) Data dictionary Relational schema, Physical database design
Implementation and data model	DBMS evaluation and recommendations
Testing and evaluation	Testing strategies used, anlysis of test results
Operation	User manual
Maintenance and monitoring	Analysis of performance results Modified user's requirements and systems specifications

Why database management system is important?

2012-07-23T22:44:00.003+05:30

Database management systems (DBMS) are normally perform a multitude of functions including providing security, multi-user access while transforms data into information. These database management systems manage (DBMS) data stored and contain programming languages. So we can say that Database management system (DBMS) is the system of computer software that is aimed to provide a managing tool for maintaining the data.

Normally database management systems (DBMS) are used to provide better service to the users. We all know that changes are often necessary to the contents of data stored in any system. Here doing necessary changes are more easily made. Not only that since all access to the database must be through the database management system (DBMS) standards are easier to enforce. Likewise there are lot of advantages of using a database management system (DBMS).That's why we can say that using database management systems are really important.

Database management system

2012-07-23T22:44:00.001+05:30

A Database Management System (DBMS) can define as a system software package that aids the use of integrated collection of data records and files known as databases. It allows organizations to place control of database development in the hands of database administrators (DBAs) and other specialists.

As the ajor purpose of a database system we can define as to provide users with an abstract view of the data. These database management systems, generally facilitates the processes of Defining, Constructing and Manipulating of data. It also provides background services including transaction management, disaster recovery and security by defining access rights the data table level. If the user does not have access to a specific table, the particular user is not allowed to access to the particular query that uses data from that table.

--Advantages

When we go through the database management system we can identify different types of advantages. We can summarized the main advantages of this as follows,

Redundancies and inconsistencies can be reduced
Data is stored and accessed efficiently including the support of very large files and index structures
Integrity can be improved and standards can be enforced
Let users feel the data is accessed only by him at that time
Provides necessary security to protect the data stored and centralize administration

Why entity relationship diagram is important?

2012-07-23T22:43:00.003+05:30

This entity relationship diagram drawn when designing a database system. It is normal that, after the systems specification, this diagram is drawn showing the conceptual design of the database. ERD or entity relationship diagram shows the type of information that is to be stored in the system and how these information associate with each other.When creating, the entity relationship diagram this will give a perfect view of all database elements. This visual representation is really easy to understand and in the same time this diagram can be shown and explained to user representatives for confirmation and approval.

ERD (Entity-relationship diagram)

2012-07-23T22:43:00.001+05:30

It can define as an abstract and conceptual representation of data. This was first described in the year 1976 by Dr. Peter Chen Properly and it was appeared in a paper in the first issue of the ACM publication, "Transactions on Database Systems". When concern about the Entity-Relationship Approach it consists of an analytical method and consists with modeling technique. In today's world it recognized as one of the most important tools in the data administration tool kit. When we go through the Entity relationship diagram we can identify that, E-R Diagrams use mainly three symbols to represent the relationship among entities.

Entity: A real-world item or concept that exists on its own. Here we can define an entity type as a rectangle containing the type name

Attribute: An Attribute
is a characteristic of an Entity; they are properties used to distinguish one entity instance from another. Attribute is represented by an clipcy.

Relationship: As an association between two entities to which all of the occurrences of those entities must conform which is represented by a diamond

--Advantages

We can summarized the main advantages of this entity relationship diagram can be define as follows,

All the major components of the information environment are formally represent
The major relationships of data have been formally recognized
Aids to maintain good and consistent naming standards
Visualize representation

Why database design is important?

2012-07-23T22:42:00.003+05:30

In database design it is in consistent form. So when the database is designed only relevant and required data will be stored. Because of that the layout of the table allows the data to be consistent. In the other hand to keep the data consistent this can be handle through implementation of primary keys and unique key constraints.

Also overall performance of the application is also dependent on the design of the database. Not only that, database design improve the store and management of data and its structures. So we can say that database design is so important.

Why data mining is important for the university setting?

2012-07-23T22:42:00.001+05:30

Simply data mining refers to extracting or mining knowledge from large amount of data. So data mining is searching, analyzing and sifting through large amounts of data to find relationships, patterns, or any significant statistical correlations. When we go through data mining in a university setting we can identify number of positive impact.

Data mining can use to identify students progress within the university. So this will helpful to identify who are the week students that need to pay more attention. That's' why it is really easy to select the weak students to pay more attentions to help to improve their studies. Not only that data mining can use to identify the requirements of the students as well as the new patters. In the same time this will helpful to allocate resources and staff more effectively while considering the number of students and their requirements. While concerning all these things we can say that data mining is really important for the university setting.

Why data mining is important?

2012-07-23T22:41:00.001+05:30

In data mining concept main purpose is to used for analysis of collected information. So here unknown interactions between data are researched in a variety of ways to ascertain critical relationships between subjects and aggregated information. That's why data mining is largely used in several applications such as understanding consumer research marketing, product analysis, demand and supply analysis, e-commerce, investment trend in stocks & real estates, telecommunications and so on.In global market data mining has great importance in highly competitive business environment. Because it is a good method to manage latest information and used for competition analysis, market research, economical trends, consume behavior, industry researches and so on. While considering all these things we can identify that data mining is really important.

Difference between data warehouse and an operational database

2012-07-23T22:40:00.003+05:30

When we go though the data warehouse and operational database we can identify number of differences. We can define those differences as follows,

Operational database	Data warehouse
Stored with a functional or process orientation	Stored with a subject orientation

In operational database data are stored with a functional or process orientation. But when we move to the data warehouse we can identify that data are stored with subject orientation that facilitates multiple views for data and decision making.

Operational database	Data warehouse
Different representation or meanings	Unified view of all data elements with a common definition and representation

Data warehouse concepts it unified view of all data elements with a common definition and representation.In operational database similar data are allowed different representations

Operational database	Data warehouse
Represent current transactions	Historic in natural

Data are historic in nature in data warehouse. Here a dimension is added to facilitate data analysis and time corporations. But if we turn our eye to the operational database we can recognize that in operational database model data represent in current transactions. So there is a big difference in this perspective

Operational database	Data warehouse
Update and delete	Add only periodically from operational system

In datawarehouse data are changed. But here data only added periodical from operational systems. But when we turn our eyes to the operational database perspective we can recongniz that data update and deleted are really common in operational database.

Why data warehouse is important?

2012-07-23T22:40:00.001+05:30

We all know that data warehouse is a collection of an organization's data stored in an electronic format which most commonly organized and stored in a structure conducive to high speed analysis and summary reporting. In this new millennium it is a great solution where organizations and other businesses factors can have their applications made that will contribute to their global market success. The applications contain all the information required by analyzing how the industry runs and use it to draw their strategy to keep the particular company or organization on top of the competition as much as possible.

Data warehouse always provide fast, effective reporting without the burden of significant added overhead to the organization's operational databases. So it is really easy as well as it save time. Not only that normally data warehouse is thought of as a database of meta data while data warehouse system include components to load, transform, and extract data, as well as tools used to retrieve, analyze, and report on the data and it improve the standardization of data collected from multiple sources.Also when we go further details study of the data warehouse importness we can identify that processing queries and reports from the data warehouse leaves the databases free to provide the transactional processing for which they were designed. While concerning all these factors we can identify that data warehouse is really important.

Why relational approach to data modeling produces database more flexible to change compare to other hierarchical and network data models?

2012-07-23T22:39:00.003+05:30

When we go through the data models we can identify that relational database model is naturally scalable and extensible, providing a flexible structure to meet changing requirements and increasing amounts of data when comparing to other data models.This model permits changes to a database structure to be implemented easily without impacting the data or the rest of the database.So it is really easy for the database analysis to make necessary changers in existing database. That's why we can say that relational databases offer structural flexibility for the applications written for those databases are easier to maintain than similar applications written for hierarchical or network databases. That same structural flexibility enables to retrieve combinations of data that may not have anticipated needing at the time of the database's design.In theoretically there is no limit on the number of rows, columns or tables. But in the real world growth and change are limited by the relational database management system and physical computing hardware, and changes may impact external applications designed for a specific database structure.

Comparison between hierarchical model, network model and relational model

2012-07-23T22:39:00.001+05:30

Data models, Data structures, Data manipulation, Data integrity comparison

When we move with the data models such as hierarchical model, network model, relational model we can identify number of difference in terms of data structures, Data manipulation and Data integrity.

Characteristic	Hierarchical model	Network model	Relational model
Data structure
When we go through the structure of the hierarchical model we can identify that it used a method for storing data in a database that looks like a family tree with one root and a number of branches or subdivisions. That's why we can say that record types are organized in the form of a rooted tree. But in network model we can identify multiple branches emanating from one or more nodes. So some times it is like a like several trees which share branches. In relational model is that of a relation. Relation is a two-dimensional table. So the table can be used to represent some entity information or some relationship between them
Data structure	One to many or one to one relationships	Allowed the network model to support many to many relationships	One to One, One to many, Many to many relationships
In hierarchical model only one-to-many or one-to-one relationships can be exist. But in network data model makes it possible to map many to many relationships In relational each record can have multiple parents and multiple child records. In effect, it supports many to many relationships
Data structure	Based on parent child relationship	A record can have many parents as well as many children.	Based on relational data structures
In hierarchical model relationship based in terms of parent child. So a child may only have one parent but a parent can have multiple children. But in network data model a record can have many parents as well as many children. Relational data model is based on relational data structures
Data manipulation	Does not provide an independent stand alone query interface	CODASYL (Conference on Data Systems Languages)		Relational databases are what brings many sources into a common query (such as SQL)
In relational database it use powerful operations such as SQL languages or query by example are used to manipulate data stored in the database But in hierarchical data model it does not provide an independent stand alone query interface while network model uses CODASYL
Data manipulation	retrieve algorithms are complex and asymmetric	Retrieve algorithms are complex and symmetric			Retrieve algorithms are simple and symmetric
In hierarchical data model and network model retrieve algorithms are complex and symmetric But in relational data model retrieve algorithms are simple and symmetric
Data integrity	Cannot insert the information of a child who does not have any parent.	Does not suffer form any insertion anomaly.			Does not suffer from any insert anomaly.
In hierarchical data model we cannot insert the information of a child who does not have any parent. But in network model does not suffer form any insertion anomaly. relational model does not suffer from any insert anomaly
Data integrity	Multiple occurrences of child records which lead to problems of inconsistency during the update operation	Free from update anomalies.			Free form update anomalies
In network model it is free from update anomalies because there is only a single occurrence for each record set.In relational model it also free form update anomalies because it removes the redundancy of data by proper designing through normalization process. But in hierarchical model there are multiple occurrences of child records. which lead to problems of inconsistency during the update operation
Data intergirty	Deletion of parent results in deletion of child records	Free from delete anomalies			Free from delete anomalies
In hierarchical model it is based on parent child relationship and deletion of parent results in deletion of child records .But in network model and in relational model it is free from deletion anomalies. Because information is stored in different tables.

Stages in Decision Making

2012-07-23T22:36:00.001+05:30

Stages in a decision making process can simply define as follows chart, This steps are really effective pattern for making decisions and solving problems.

State the Problem

The first step of this decision making is to identify the problem. Because clear understand of the problems is really helpful when come to a particular desecition . Until have a clear understanding of the problem or decision to be made, it is meaningless to proceed. So when we concern about the Capital bank situation also we can identify the loss of customers as a main problem as well as the completion among other banks.

Identify Alternatives

Most of the time we can identify there are number of several feasible alternatives. So that's why it is really important to research and identify the alternatives. In Capital banking also before they come to an decision about online banking system 1 st they needs to identify the problem and then they needs to identify the alternatives.

Evaluate the Alternatives

Identifying the alternatives is not enough. As well as identifying the alternatives, it is really import to evaluate those alternatives. Capital banks also need to identify the alternative and evaluate it to get some idea about the new implementation

Make a decision

After evaluations will, have some good knowledge about what actually going. So it is really easy to come to an decision.

Implement decision

We all know a decision has no value unless implement. This reminds that Great decisions are only great when they are carried into action and the action achieves the desired result. In capital bank also they needs to come to an decision and they needs to implement their decision.

Risk Analysis & Risk management

2012-07-23T22:35:00.003+05:30

Risk analysis is a technique to identify and assess for mitigation of risks at project initiation and during task execution. It is the systematic study of uncertainties and risks encounter in business, engineering, public policy, and many other areas. Risk analysis is really important to determined determined the various impacts and identify the risks which could lead . Risk managers start with risk analysis, then seek to take actions that will mitigate or hedge these risks.

In the same time this approach helps to define preventive measures to reduce the probability of occurrence and identify methods to avert possible negative effects on the effectiveness of the company to compete in this particular market.

As the advantage of this approach we can define it is clear to all sides, vendor, contractors and purchaser, what the risks are and who has ownership of each one. According to this approach 1^st they identify the threats, Secondly to work out the likelihood of the threat being realized and to assess its impact. Thirdly start to look at ways of managing them. Finally once carried out a risk analysis and management exercise, it is worth to carrying out regular reviews