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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 Martin Zapletal
Sun, Mar 8, 2015

Concepts such as event sourcing and CQRS allow an application to store all events that happen in the system using a persistence mechanism. The events can not be mutated and current state of the system in any point in history can be reconstructed by replaying all the events until that point. For performance reasons obviously the state can be cached using a snapshot. But the undisputable advantage of this approach is that the whole history of events (including user actions, behaviour or system messages - anything we decide to store) is available to us rather than just the current state. Event sourcing was thoroughly discussed before for instance in [1] or [2] and CQRS in [3], [4] or [5]

In this post we will discuss how we can store and further use these data by connecting Akka, Cassandra and Spark, focusing mostly on the configuration, Akka serialization and Akka-analytics project. Later I will follow up with another blog post building on top of this with an example of using machine learning techniques to obtain some insights to help optimize future decisions and application workflow.

Posted by Carl Pulley
Mon, Jan 26, 2015

In this post we demonstrate how machine learning (specifically SVMs) may be used to identify gesture events, such as taps, in data steams produced by accelerometers in devices such as Pebble watches.

We start by developing prototype classification models in R and then port those models into Scala.

Applying the trained SVM models to unseen data, we successfully demonstrate an ability to punctuate exercise sessions by identifying taps to tokenise those exercise steams into separate activity periods.

Posted by Jan Machacek
Fri, Jan 23, 2015

In the next few posts, we will describe the journey of collecting (tagged) data, experimenting with potential classification models, and then finally implementing these models. It was revealing to experience the challenges of implementing truly reliable and near real-time analysis system in a world of unerliable networks and users who cannot tolerate interruptions.

We ended up performing principal component analysis on a type II DCT of the sensor data we receive: this then formed basis for the training set of a support vector machine. We have done the modelling in R, then exported the libsvm settings, and loaded this code in Scala, where we perform the classification on the incoming stream of data.

Posted by Marios Papasofokli
Mon, Apr 28, 2014

Welcome to another edition of #ThisWeekInScala!

After last week's release for Scala 2.11.0 many projects have been published for it, check the list out!

Lets catch-up on the latest Scala happenings...

Posted by Marios Papasofokli
Mon, Apr 14, 2014

Welcome to another edition of #ThisWeekInScala!

An exciting week since we have new releases for Scala, Play and Akka!

Since I will be away next week, my colleague Mario Arias (@dh44t) will be producing the next roundup which will be released on a Tuesday because of Monday's holiday.

Lets catch-up on the latest Scala happenings...

Posted by Marios Papasofokli
Mon, Apr 7, 2014

Welcome to another edition of #ThisWeekInScala!

Let's start with a new video from Typesafe, where Martin Odersky introduces the Typesafe Reactive Platform!

Lets catch-up on the latest Scala happenings ...

Posted by Marios Papasofokli
Mon, Mar 31, 2014

Welcome to another edition of #ThisWeekInScala!

Lets catch-up on the latest Scala happenings ...

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