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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

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