I found it in the book store next to my university. Bought it.

Read the introduction, and browsed through the first chapter (Ruby). Failed to do the exercises (a mixture of feeling I already know a bit of Ruby, and lack of time and motivation to really get my hands dirty).

Stopped reading the book.

I will start over now, I promise.

Datastax now offers Brisk:

• CassandraFS has the same interface as HDFS. So, in particular, you should be able to use most Hadoop add-ons with Brisk.
• CassandraFS has comparable performance to HDFS on sequential scans. That’s without predicate pushdown to Cassandra, which is Coming Soon but won’t be in the first Brisk release.
• Brisk/CassandraFS is much easier to administer than HDFS. In particular, there are no NameNodes, JobTracker single points of failure, or any other form of head node. Brisk/CassandraFS is strictly peer-to-peer.
• Cassandra is far superior to HBase for short-request use cases, specifically with 5-6X the random-access performance.

Checkout their white paper.

Google announced that the new Google Analytics (currently in beta and only rolled out to selected accounts) supports event goals.

So, if you have been using GA to track events within your Flash game, Social Network app or anything else, you can now interpret them directly as goals and don’t need to fake Pageview-calls of “virtual” (non-existing) pages in a hidden div or iframe (which is what I have been using before).

Interestingly, no mentioning of event funnels so far – which is strange, since funnels can be defined for “normal” goals anyway.

It seems that the normal (i.e. “free”) , subtype-based variant needs to be managed by the application itself, to define ratios, select a variant to show/use, etc. and Kontagent only tracks the resulting metrics, whereas their full fledged “Viral Optimizer” (only in premium) lets you define variants, ratios, etc. within the Kontagent web interface itself, generating code to copy/paste into your app.

Their A/B REST API documentation is lacking though, so we will see if my assumptions are true.

In the meantime, I’m also testing several versions of my home page using the Google Website Optimizer.

My background is twofold – I started out as a software developer for Alcatel, but switched into some project management, development lead, CTO and managing director positions, both inside and outside of the games industry (namely Rockstar Games, VeriSign, and a small german startup called plazz entertainment).

From my perspective, BI or data mining (if you can call it that) in the “traditional” retail-oriented games industry (think Rockstar shipping Manhunt 2 or Grand Theft Auto) back in 2005 has been pretty basic, and mainly focused on usability and play testing, like generating 2D heat maps for level architectures from people playtesting the game – with that, the game designers and level designers could determine which were the most and least frequently visited areas in a level, and be use that information to change the level structure and/or level elements accordingly to guide the player better.

Of course, Microsoft/Bungie (Halo) has been very active in that field, doing much more than that, and also talking about their approach and findings quite openly at conferences.

Things changed for me when I became CTO of a german startup, doing their own “Club Penguin” clone in Germany called “Atlantis“. Initially, we had a freemium (free basic access with premium subscription as option) business model. this was changed to the now typical microtransactions for virtual item sales.

Since we were building the complete platform from scratch, we had a lot of different areas to tackle, so BI was mainly seen as a “hobby” of mine, rather than the strategic necessity for commercial success I thought it is or should be.
Basically, we did aggregated log events from the database (a MySQL single read/write master, multiple read-only slaves setup) per time slot – like number of chat messages per day, time spent online per character per day. We did this using an open source tool called ART.

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On the website, Google Analytics (and Piwik) provided the standard metrics like Visits, Average Time on Site, Bounce Rate. I thought about using Ajax-calls into Google Analytics to track ingame events, but decided against it, not to mix web traffic stats and ingame events (that need to be stored in the database, anyway).

My employment there ended before I had time to research and implement something more elaborate and detailed. I’d be very interested to talk about what others did, what tools or methods you use, and so on.

As I have posted before, I think this is a very interesting topic, but somehow there does not seem to be a lot of information available – so let’s talk about how you approach it, what kind of tools and frameworks do you use, how you integrate the various data sources, what kind of (automated or manual) decisions and optimizations you feed back into your game design and/or virtual economy parameters, and so on.

So, whether you already have vast experience in that area, or you just starting out, join now and let everybody know!

At a glance it seems that a majority of Vienna’s finest game developers are all creating social or web games or frameworks –  spielwerk, mipumi, platogo, Team Vienna, fatfoogoo …

Special gratulations to socialspiel for winning the Austrian Staatspreis for Push!

EasyRec enables you to include item recommendations based on the behaviour of your website users.

From their website:

User Actions are sent to the easyrec using the REST API. Possible actions are viewing, buying or rating an item.

These user actions are stored in the database of the Recommendation Engine.

The provided Analyzers periodically analyze all recorded data for identifying patterns to generate recommendations.

These Recommendations can be accessed through calls to the easyrec webservice API and presented to a user.

I left VeriSign in 2008, so it will be quite interesting for me to see how it evolved.

Founded in 2009, and they won the “Best Startup” prize at last year’s Game Developer Conference, but even now they still appear to be in stealth phase.

Interestingly, I have been thinking about data mining since I was working on Atlantis at plazz entertainment. Having talked with a lot of developers and publishers, it seems that most of them rely on basic data aggregations and averages as a basis for their decisions to optimize, while obviously it would be very rewarding to segment your customers into groups and and target them more specifically, predicting revenue curves and lifetime value, presenting them items to purchase that other, “similar” users have bought, etc.

If you are game developer (or publisher) using data mining techniques, or are interested in this topic, please contact me via Xing or LinkedIn.

Although I have not been able to talk a lot about my current work or employer, chances are that this will change soon.

Expect more frequent updates soon.