Cloud Event Processing - Analyze, Sense, Respond

Colin Clark

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Top Stories by Colin Clark

Lately, I’ve been working on some interesting projects involving not just the usual suspects of stream processing, but data mining within high velocity time series.  In conjunction with that effort, I’ve been doing a lot of research in the areas of symbolic representation, dimension reduction, clustering, indexing, classification, and anomaly detection.  A prolific  researcher in this area is Dr. Eamonn Keogh – I’ll be applying some of his team’s ideas so some interesting customer problems and telling you all about it here.  Let’s get started! TOO MUCH DATA In dealing with real time streaming numerical data, there is just too much of it sometimes to do anything meaningful with it in real time.  For example, in pattern recognition, trying to compute nearest neighbors using continuous, highly dimensional data is a compute nightmare.  Or, once you’ve identified a pat... (more)

Event Processing in the Cloud – DataSift is a Big Proof Point

In the past year or so, I’ve heard from many skeptics – people who didn’t believe that Event Processing could be successfully deployed in the cloud.  Granted, most of these folks represented firms actively engaged in providing the High Frequency Trading (Algo Trading) industry with tools.  And in that arena, cloud deployment probably doesn’t make sense.  Yet. Close to Home Though Ask people in Capital Markets about Twitter and the most common response you’ll get is, “What do people use it for?”  This is because most of the people in Capital Markets can’t use things like Twitter, i... (more)

Building a Back Testing Platform for Algorithmic Trading

On this continuing series, I am examining thoughts and specific implementation details around building a back-testing platform for algo trading.  Eventually, we’ll see where complex event processing plays and how to implement it. Appendix to Part One – The Data Format Rather than looking at various database solutions first and then trying to define the problem in terms of those solutions, let’s first examine what market data looks like.  In its most simple form, market data looks like this (there’s usually a little more, but this is fine for our purposes): Date: The date of the ... (more)

My Top Five Cloud Predictions for 2011: Colin Clark

Every year, I like to decompress a bit and take a break. Usually, I like to go scuba diving – the dive sites I like are usually far removed from email, Twitter, Facebook, etc. and it gives me a chance to actually unplug, defrag, and think a bit. This year, the family went to Grand Cayman to experience some of the world’s best diving. Within Grand Cayman is Hell, a small township dedicated to tourism and aptly named given the attached photo. We visited Hell in between dives, and rather than make my ex-wife room reservations or send out postcards, I thought I’d amplify a few predic... (more)

Erlang, RabbitMQ, & Redis

VMWare has been on a buying spree lately. In the last month, they’ve announced both Redis and RabbitMQ. Here’s VMware’s take on Redis, and spring source’s on RabbitMQ.  RabbitMQ is built with Erlang. Much Rejoicing in the Village We use both of these technologies at Cloud Event Processing.  And we love Erlang too.  VMware’s acquisition of these technologies not only validates our decisions, which we are very selfishly pleased about, but also sends an interesting message. The Message Please Everyone’s busy abstracting resources in the cloud – making resources like compute, stora... (more)