Wednesday, January 15, 2020

MLOps and Data Mining in Oracle 19c

Machine learning operations or aka MLOps is getting quick traction even in database arena and Oracle is not behind. They are heavily using it in their data mining techniques and introducing new alogs and other frameworks.

Data mining is a technique that discovers previously unknown relationships in data. Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and to predict the likelihood of future events based on past events. Data mining is also known as Knowledge Discovery in Data (KDD).

This is especially very much pertinent when it comes to OLAP. On-Line Analytical Processing (OLAP) can be defined as fast analysis of multidimensional data. OLAP and data mining are different but complementary activities. Data mining and OLAP can be integrated in a number of ways. OLAP can be used to analyze data mining results at different levels of granularity. Data Mining can help you construct more interesting and useful cubes.

Data mining does not automatically discover information without guidance. The patterns you find through data mining are very different depending on how you formulate the problem. Each data mining model is produced by a specific algorithm. Some data mining problems can best be solved by using more than one algorithm. This necessitates the development of more than one model. For example, you might first use a feature extraction model to create an optimized set of predictors, then a classification model to make a prediction on the results.

In Oracle Data Mining, scoring is performed by SQL language functions. Understand the different ways involved in SQL function scoring. Oracle Data Mining supports attributes in nested columns. A transactional table can be cast as a nested column and included in a table of single-record case data. Similarly, star schemas can be cast as nested columns. With nested data transformations, Oracle Data Mining can effectively mine data originating from multiple sources and configurations.

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