Data Warehouse & Data Mining e Description
In this application you find courses + exercises + correction in details on Data Waherouse and Data Mining
What is "Data Warehouse" first? :
It is a type of database that contains a huge amount of data to help make decisions within the organization. This type of database is characterized by the conformity of its internal structure with what the user needs from the indicators and axes of analysis in what is called the star-star model, and its applications: systems decision support and data mining.
Data warehouses usually contain historical data that has been derived and extracted from data in the usual databases used in applications on which many input and update operations take place, and data warehouses can also contain data from other sources such as text files and other documents.
what is "Data Mining"? :
It is a computerized and manual search for knowledge of the data without preliminary hypotheses on what this knowledge can be. Data mining is also defined as the process of analyzing a quantity of data (usually a large amount), to find a logical relationship that summarizes the data in a new way that is understandable and useful to the data owner . “Models” are called relationships and summary data obtained from data mining. Data mining generally deals with data that has been obtained for a purpose other than that of data mining (for example, a database of transactions in a bank), which means that the mining method of data does not affect the way the data itself is collected. This is one of the areas in which data mining differs from statistics, and for this reason the data mining process is called a secondary statistical process. The definition also indicates that the amount of data is generally large, but if the amount of data is small, it is best to use regular statistical methods to analyze it.
When dealing with a large volume of data, new problems arise such as how to identify distinct points in the data, how to analyze the data in a reasonable time and how to decide if an apparent relationship reflects a fact in the nature of the data. . Usually, data is extracted that is part of the data set, where the goal is usually to generalize the results to all of the data (for example, analyzing the current data of consumers of a product in order to anticipate future demands consumers). One of the goals of data mining is also to reduce or compress large amounts of data to express simple data without generalization.
What is "Data Warehouse" first? :
It is a type of database that contains a huge amount of data to help make decisions within the organization. This type of database is characterized by the conformity of its internal structure with what the user needs from the indicators and axes of analysis in what is called the star-star model, and its applications: systems decision support and data mining.
Data warehouses usually contain historical data that has been derived and extracted from data in the usual databases used in applications on which many input and update operations take place, and data warehouses can also contain data from other sources such as text files and other documents.
what is "Data Mining"? :
It is a computerized and manual search for knowledge of the data without preliminary hypotheses on what this knowledge can be. Data mining is also defined as the process of analyzing a quantity of data (usually a large amount), to find a logical relationship that summarizes the data in a new way that is understandable and useful to the data owner . “Models” are called relationships and summary data obtained from data mining. Data mining generally deals with data that has been obtained for a purpose other than that of data mining (for example, a database of transactions in a bank), which means that the mining method of data does not affect the way the data itself is collected. This is one of the areas in which data mining differs from statistics, and for this reason the data mining process is called a secondary statistical process. The definition also indicates that the amount of data is generally large, but if the amount of data is small, it is best to use regular statistical methods to analyze it.
When dealing with a large volume of data, new problems arise such as how to identify distinct points in the data, how to analyze the data in a reasonable time and how to decide if an apparent relationship reflects a fact in the nature of the data. . Usually, data is extracted that is part of the data set, where the goal is usually to generalize the results to all of the data (for example, analyzing the current data of consumers of a product in order to anticipate future demands consumers). One of the goals of data mining is also to reduce or compress large amounts of data to express simple data without generalization.
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