Machine to machine protocol from NY Times

The "Internet of Things" where any thing is connected to the network, is one of the most influential trends today.  Cisco in its vision about the "Internet of Everything" based on the "Internet of Things"  predicts that the economical impact of the "Internet of Everything"  is  14.4 Trillion Dollars.   There is a lot of work about the infrastructure, one of it is MQTT (Message Queue Telemetry Transport), a protocol used to support Machine-to-Machine communication.    The NY times had  recent article within its blogs on MQTT as an open standard.  In the article there is a link to a talk by my IBM colleague Andy Stanford-Clark.  
One of the mentioned applications occur in the automobile industry, of putting sensors in cars, e.g. on the car battery.

Andy is working on these topics for a long time,  in fact the chapter in our EPIA book that deals with event consumers described some of Andy's application and the ambient orb's picture appears in the book was  taken at Andy's office.  Andy has famous talks and video clips on his house where he uses MQTT to control the power.  A recent video clip that Andy posted entitled "the house that twitters" demonstrates the idea.  There are other presentations and video clips on this topic over the years. 

Internet of Things will create most of the world's events of the future, and will be a major factor in making the world event-driven.    I'll write soon about the synergy between the old world and the new world. 

Event processing for autonomous robot programming

This video clip demonstrates an autonomous robot programming frameworks based on ETALIS.

This video clip comes from University of Luxembourg, presented by Pouyan Ziafati.   I am thinking for a while that event processing is a good candidate to describe the logic of robots, which are by nature event-driven.   ETALIS is open source event processing platform based on logic programming, developed in Karlsruhe  Institute of Technology.  I'll try to get more details about this work, and look at other work that connects the robotics world to the event processing world.

Statistical reasoning and event processing tutorial

Streambase has posted a video tutorial on combination of their EP product with statistical reasoning based on MATLAB.  The idea of combining event processing and statistical reasoning is becoming part of the big data offerings, and no wonder that statistical reasoning vendors are adding event processing to their portfolio, for example the introduction of event processing within SAS, 

Streambase takes it from the other side -event processing vendor that combines statistical reasoning.

Interesting tutorial to watch.  

Progress Apama announces a version which compiles to native machine code

Progress Software announced today on the release of a new version that compiles the Apama EPL into native machine code, claiming to improve the performance of the previous version by 2000%.   They don't mention what they actually measure.   The big data era renews the investment in scalable event processing solutions with various ways of optimizations.   We may start to see specialized event processing hardware. 

I think that it will be useful to establish a set of benchmarks, since it was seen in some works that there are huge differences in performance between types of event processing application - for example: those doing mainly filtering, those doing mainly aggregations, and those doing pattern matching.  It will be good to have a set of benchmarks that fit different types of applications, and a method to map application characteristics to a specific benchmark - to avoid the phenomenon that vendors cite numbers that cannot be compared.  More -later. 

DEBS 2013 - keynotes and tutorials were published

DEBS 2013 will take place in the campus of the University of Texas Arlington, in June 29- July 3.

Today the keynotes and tutorials were published on the conference's website:

The keynote speakers this time will be: Roger Barga from Microsoft, one of the first persons in Microsoft Research who worked on event processing, and crossed the line to the product organization and deals now with product management.  Roger will talk about "the rise of the velocity pipeline in enterprise computing" that will focus on the velocity part of big data that makes batch solutions like Hadoop as inadequate.

The second keynote speaker is David Wollman who manage the smart grids standard activities in  NIST  will talk about smart grids.

There will be four tutorials - all of them of known people active in the area.

The first one will be given by my IBM colleagues from the System S team who will talk about "stream processing optimizations".  

The second one will be given by Christoph Emmersberger  an Florian Springer (both of them I know from their past association with Rainer von Ammon), who will talk about the event processing capabilities of Apache Camel.

The third one will also be given in German accent, by Boris Koldehofe and  Frank Dürr from University of Stuttgart. They will talk about "Software defined networks".

Last -- keeping the tradition, I'll be giving a tutorial this year also, this time together with Jeff Adkins, on a topic that we are both dealing - "why is event driven thinking different from regular thinking about computing". I'll write about this tutorial at a later phase (well, we have to prepare it first), meanwhile you can read the short abstract on the site.  Hope to meet old friends and colleagues in Arlington.   

More - later.

On the right technology for decisions

I came across a (not new) discussion by my IBM colleague Jean Francois Puget  published on the IBM developerworks entitled  "What is the difference between SPSS and ILOG".  Actually, while Jean Francois colors it in blue and discusses it within specific IBM products, he says that he is more interested to discuss the generic question about what is the right decision technology, as there are various technologies today that are labelled as decision technologies, decision management and other kind of decision oriented names. 

Jean Francois makes the distinction between  "single decision at a time" and "doing a group of decisions together" and asserts that for "single decision at a time" BRMS and/or predictive analytics is the right kind of technology, while for "doing a group of decisions together" optimization techniques are appropriate.  

This has some truth to it,  but I am not sure that it is the ultimate differentiation between these two types, so let's look at this issue.   When there is a need to do a decision, there are several approaches:

1. Get a person all relevant data and let this person do the decision
2. Make automated decision (or recommendation)- when the way to do the decision can be  codified as decision trees/decision tables/rules
3. Make automated decision (or recommendation) --  when the decision needs to find the best alternative according to a quantified criteria. 

For each of these cases, the data obtained can be deterministic or stochastic, existing or predicted, and there are various ways to achieve this type of data, but this is true regardless of the three cases.    It seems that approach 1 does not require any decision technology - although some people call requested data that uses some kind of inference technique also a decision technology, but I think that it might be taking the term decision to non-intuitive place;  approach 2 requires some kind of rule technology, and approach 3 requires some kind of optimization technology. 

Now,  there are cases in which single decision requires optimization. For example, a person wins the lottery and needs to get a decision where to invest the money. This is a single decision with a lot of alternatives, it requires also predictions on these alternatives,  and the person has some objective function and constraints on types of investments.   There are cases in which there are multiple decisions that have to be done at the same time -- for example:  who should receive bonus, however the amount of bonus recipients is fixed, and the criteria are very simple, so no optimization should be done, just a lot of rules applied to all candidates to rank each of them, and then sort by the ranking.  So there is selection between alternatives, but optimization is not really required.   While there is some correlation between the criteria specified by Jean Francois and what I have written here, but it seems to me that the main distinction is what is the kind of decision, and the way alternatives are compared...   More on this - later.

Real-time fraud detection from IBM

IBM has recently published a Redbook on its solution in Real-time fraud detection. 

Fraud detection is a very important application, since every computerized system attracts people who attempt to hit the system.  While much of today's effort is dedicated to fraud in financial transaction systems, I guess that future efforts will be dedicated to those who are trying to sabotage your computerized car, or even bionic cells in your body.   The interesting fact about this IBM solution that two of the IBM products that participate in the solution are CICS and SPSS.   I worked a lot in SPSS during my first couple of years as a programmer in the Israeli air-force and this was in 1976.  At the same time CICS was used as the transaction processing system of the operational part of the air-force.   .   The SPSS modeler has a data-flow view from the family of event processing networks.  Interesting to see how old guys learn new tricks and handle very different applications relative to their original ones.