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      December 31, 2014

      Notes on machine-generated data, year-end 2014

      Most IT innovation these days is focused on machine-generated data (sometimes just called “machine data”), rather than human-generated. So as I find myself in the mood for another survey post, I can’t think of any better idea for a unifying theme.

      1. There are many kinds of machine-generated data. Important categories include:

      That’s far from a complete list, but if you think about those categories you’ll probably capture most of the issues surrounding other kinds of machine-generated data as well.

      2. Technology for better information and analysis is also technology for privacy intrusion. Public awareness of privacy issues is focused in a few areas, mainly: Read more

      October 10, 2014

      Notes on predictive modeling, October 10, 2014

      As planned, I’m getting more active in predictive modeling. Anyhow …

      1. I still believe most of what I said in a July, 2013 predictive modeling catch-all post. However, I haven’t heard as much subsequently about Ayasdi as I had expected to.

      2. The most controversial part of that post was probably the claim:

      I think the predictive modeling state of the art has become:

      • Cluster in some way.
      • Model separately on each cluster.

      In particular:

      3. Nutonian is now a client. I just had my first meeting with them this week. To a first approximation, they’re somewhat like KXEN (sophisticated math, non-linear models, ease of modeling, quasi-automagic feature selection), but with differences that start: Read more

      July 12, 2013

      More notes on predictive modeling

      My July 2 comments on predictive modeling were far from my best work. Let’s try again.

      1. Predictive analytics has two very different aspects.

      Developing models, aka “modeling”:

      More precisely, some modeling algorithms are straightforward to parallelize and/or integrate into RDBMS, but many are not.

      Using models, most commonly:

      2. Some people think that all a modeler needs are a few basic algorithms. (That’s why, for example, analytic RDBMS vendors are proud of integrating a few specific modeling routines.) Other people think that’s ridiculous. Depending on use case, either group can be right.

      3. If adoption of DBMS-integrated modeling is high, I haven’t noticed.

      Read more

      Feed: DBMS (database management system), DW (data warehousing), BI (business intelligence), and analytics technology Subscribe to the Monash Research feed via RSS or email:

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