Drill is powerful in terms of accessing and joining data from heterogeneous sources, which is usually a cumbersome task when done in data mining libraries. On the other hand, Drill does not have any data mining capabilities. Developing data mining algorithms for Drill is time consuming and so would likely be limited to a handful of algorithms, nothing compared to those available in the well-established data mining libraries. The proposed QDrill with the Analytics Adaptor solves these issues by using Drill to load and join data from heterogeneous sources and using the pre-existing data mining algorithms of well-established data mining libraries to train and score data mining models.
The proposed Analytics Adaptor optimizes and provides access to various data mining libraries. The Analytics Adaptor works with Analytics Plugins that transform the data loaded by Drill to a data structure understandable by the data mining libraries. This way, algorithms from more than one library can be used together, leaving it to the Analytics Adaptor to resolve the inter-library data format conversion. In addition, the plugins invoke the APIs of the data mining library to train and score data mining models. All these details are hidden from users.