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Monday, 26 August 2019

Oracle Data Mining - An Introduction


Oracle Data Mining is one of the most impressive features of the new Oracle Database system. It has been designed with the purpose of discovering new functionality within the data on the system and thus provides new and unique alternatives to creating an application. Oracle Data Mining helps data analysts work directly with the data and help them explore the data in a graphic format and create new application models by providing predictions and insights on the data.

There are many advantages to using Oracle Data Mining for mining by data analysts. The most important among them, is the fact that there is no need for any data movement from the database and conversion into the apt format for discovery and analysis of data. This not only saves time and cost, it helps curb the errors which plagued the analysts due to conversion.

Because there is no need for conversion Oracle Data Mining also provides updated data in real time from the database as soon as changes are made and recorded. This helps the data analysts act more quickly and saves time due to unnecessary updates in the information. It also provides extra security by regulating which user or data analyst gets access to which data knowledge or mining model. This ensures that sensitive information is not accessed by everyone. 

The best part about Oracle Data Mining is its auto learning AI feature. As soon as certain parameters and instructions are provided, Data Mining automatically starts applying all possible combinations of the parameters and provides the analysts with different options for the most optimal and effective data mining models. This helps the analysts because it saves them the time and effort of actually working on developing the said parameters and helps them save time and work directly on the functionality of the said application. Thus, Oracle Data Mining uses AI like systems to aid and boost the development of applications by companies and increases their revenue while decreasing costs.

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