Key metrics separating real-time analytics from more traditional, batch, off-line analytics are latency and availability. At one end of the analytics spectrum are complex, long running batch analyses with slow response time and low availability requirements. At the other end are real-time, lightweight analytic applications with fast response time (O[ms]) and high availability (99.99..%). Another distinction is between the capability for responding to individual events and/or ordered sequences of events versus the capability for handling only event collections in micro batches without preservation of their ordered characteristics. The complexity of the analysis performed on the real-time data is also a big differentiator: capabilities range from simple filtering and aggregations to complex predictive procedures. The level of integration between the model generation and the model scoring functionalities needs also to be considered for real-time applications. Machine learning algorithms specially designed for online model building exist and are offered by some streaming data platforms but their number is small. Practical solutions could be built by combining an off-line model generation platform with a data streaming platform augmented with scoring capabilities.
In this blog we describe a new prototype for real time analytics integrating two components : Oracle Stream Explorer (OSX) and Oracle R Enterprise (ORE). Examples of target applications for this type of integration are: equipment monitoring through sensors, anomaly detection and failure prediction for large systems made of a high number of components.
The basic architecture is illustrated below:
Source:: r-bloggers.com