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Posted by Carl Pulley
Mon, Mar 23, 2015

In this post, we monitor real-time streams of event data looking for pattern matches. Monitoring is performed using a novel and expressive query language based on linear dynamic logic (a generalisation of linear time temporal logic), with modern SMT provers (e.g. CVC4 and Z3) defining the pattern matching workhorse.

Posted by Carl Pulley
Wed, Mar 18, 2015

In this post we investigate how Akka streaming can be used to define flexible and programmable workflows that successfully integrate ML classifiers (such as decision trees, Bayesian networks, SVMs, etc.) to build complex classification pipelines.

Posted by Carl Pulley
Sun, Mar 15, 2015

Lift uses Akka streaming workflows to define a flexible and generic exercise classification pipeline. The classification pipeline is able to modularly include any machine learning classifier and is able to monitor the real-time streams of classification results using a linear dynamic logic.

This post provides a summary overview of this classification pipeline with future posts introducing the implementation details.

Posted by Petr Zapletal
Mon, Nov 10, 2014

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