Main Article Content
Over the past two decades there has been a huge increase in the amount of data being stored in databases as well as the number of database applications in business and the scientific domain. This explosion in the amount of electronically stored data was accelerated by the success of the relational model for storing data and the development and maturing of data retrieval and manipulation technologies. While technology for storing the data developed fast to keep up with the demand, little stress was paid to developing software for analysing the data until recently when companies realized that hidden within these masses of data was a resource that was being ignored. The huge amount of stored data contains knowledge about a number of aspects of their business waiting to be harnessed and used for more effective business decision support. Database Management Systems used to manage these data sets at present only allow the user to access information explicitly present in the databases i.e. the data. Contained implicitly within this data is knowledge about a number of aspects of their business waiting to be harnessed and used for more effective business decision support. This extraction of knowledge from large data sets is called Data Mining or Knowledge Discovery in Databases and is defined as the non-trivial extraction of implicit, previously unknown and potentially useful information from data. The obvious benefit of Data Mining has resulted in a lot of resources being directed towards its development.
How to Cite
, R. R. W. P. R. T. (2018). Brief Introduction of Data Mining and Data Warehousing. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(1), 397–400. Retrieved from https://ijfrcsce.org/index.php/ijfrcsce/article/view/1028