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These needs are automatic summarization of data, extraction of the "essence" of information stored, and the discovery of patterns in raw data.
We have been collecting a myriad of data, from simple numerical measurements and text documents, to more complex information such as spatial data, multimedia channels, and hypertext documents.
Information retrieval is simply not enough anymore for decision-making.
Confronted with huge collections of data, we have now created new needs to help us make better managerial choices.
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases.
This initial chaos has led to the creation of structured databases and database management systems (DBMS).
The efficient database management systems have been very important assets for management of a large corpus of data and especially for effective and efficient retrieval of particular information from a large collection whenever needed.
Initially, with the advent of computers and means for mass digital storage, we started collecting and storing all sorts of data, counting on the power of computers to help sort through this amalgam of information.
We are in an age often referred to as the information age.
In this information age, because we believe that information leads to power and success, and thanks to sophisticated technologies such as computers, satellites, etc., we have been collecting tremendous amounts of information.