10-08-2011, 12:59 PM
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ABSTRACT
Data mining has become a popular buzzword but, in fact, promises to revolutionize commercial and scientific exploration. Databases range from millions to trillions of bytes of data. Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve.
A data warehouse is a relational database that is designed for query and analysis rather than transaction processing. It usually contains historical data that is derived from transaction Data in the warehouse can be seen as materialized views generated from the underlying multiple data sources. Materialized views are used to speed up query processing on large amounts of data. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources. These views need to be maintained in response to updates in the source data. This is often done using incremental techniques that access data from underlying sources. In the data-warehousing scenario, accessing base relations can be difficult; sometimes data sources may be unavailable, since these relations are distributed across different sources.
This paper provides an introduction to the basic technologies of data mining. As well as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end-users.
According to my view to install a new information source by DATAMINING & WAREHOUSING in order to serve the people is introduced as follows.
Overview
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified.
Data mining architecture:
How does data mining work?
Classes: Stored data is used to locate data in predetermined groups.
Clusters: Data items are grouped according to logical relationships or consumer preferences.
Associations: Data can be mined to identify associations.
Sequential patterns: Data is mined to anticipate behavior patterns and trends.
The Data Mining Process:
Data mining consists of five major elements:
Extract, transform, and load transaction data onto the data warehouse system.
Store and manage the data in a multidimensional database system.
Provide data access to business analysts and information technology professionals.
Analyze the data by application software.
Present the data in a useful format, such as a graph or table.
Different levels of analysis are available:
Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
What technological infrastructure is required?
Size of the database: The more data being processed and maintained, the more powerful the system required.
Query complexity: The more complex the queries and the greater the number of queries being processed, the more powerful the system required.
Data Mining Infrastructure:
Ability to access data from many sources & consolidates
Ability to score customers based on existing models
ABSTRACT
Data mining has become a popular buzzword but, in fact, promises to revolutionize commercial and scientific exploration. Databases range from millions to trillions of bytes of data. Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve.
A data warehouse is a relational database that is designed for query and analysis rather than transaction processing. It usually contains historical data that is derived from transaction Data in the warehouse can be seen as materialized views generated from the underlying multiple data sources. Materialized views are used to speed up query processing on large amounts of data. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources. These views need to be maintained in response to updates in the source data. This is often done using incremental techniques that access data from underlying sources. In the data-warehousing scenario, accessing base relations can be difficult; sometimes data sources may be unavailable, since these relations are distributed across different sources.
This paper provides an introduction to the basic technologies of data mining. As well as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end-users.
According to my view to install a new information source by DATAMINING & WAREHOUSING in order to serve the people is introduced as follows.
Overview
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified.
Data mining architecture:
How does data mining work?
Classes: Stored data is used to locate data in predetermined groups.
Clusters: Data items are grouped according to logical relationships or consumer preferences.
Associations: Data can be mined to identify associations.
Sequential patterns: Data is mined to anticipate behavior patterns and trends.
The Data Mining Process:
Data mining consists of five major elements:
Extract, transform, and load transaction data onto the data warehouse system.
Store and manage the data in a multidimensional database system.
Provide data access to business analysts and information technology professionals.
Analyze the data by application software.
Present the data in a useful format, such as a graph or table.
Different levels of analysis are available:
Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
What technological infrastructure is required?
Size of the database: The more data being processed and maintained, the more powerful the system required.
Query complexity: The more complex the queries and the greater the number of queries being processed, the more powerful the system required.
Data Mining Infrastructure:
Ability to access data from many sources & consolidates
Ability to score customers based on existing models
