And, I could give Wordster the terse text above and have it parsed and scrubbed. After it vomits, Wordster would make semantically-useful recommendations for correction. In theory, it could turn that stuff into high-class chatting. I don’t research this space. This is just my personal rambling. I did pass on to my smarter folks here at Gartner and we will see what they say from a RESEARCH perspective.

Can you believe this was the lunch program at a Rotary meeting? Can you imagine the shock when you tell your guest presenter, “Hi! I’m from Gartner.” He did well. He was once a Tech man, so that was a given. This was cool stuff.

I will talk more about this, but here is a link to the program.  Also, I believe this will be webcast a bit later.

http://www.computing-margins.org/

Feeling altruistic?  Good.  And by the way.  This conference spawned from work going on in a hot bed of computer scientists.  Remember them?  They once did compiler design, fractals and artificial intelligence.  They still do, but their mandate has taken on a much wider meaning over the past years.  Just like most everything else, they have seen a shift from theoretical to applied research.  Just keep the proper balance, as they do.

Disclaimer: I am on the board of advisors for this particular college of computing.  My pride is not without vested interest.

I met with Thad last week, and he gave me an update on one of the most “out there” aspects of the interview – being able to read imagined sign language through the use of Functional Magnetic Resonance Imaging (FMRI). It turns out that you can do it – read imagined sign language.  This new discovery was only an idea at the time of the interview.

In the original interview, Thad said:

The new crazy thing is that, since sign language is actually distributed throughout the motor cortex, much more than say speech is with the larynx and the throat and tongue muscles, we might literally be able to pull off sign language from the brain, which would be astonishing if we can do it…

The movement for the larynx and your tongue and your jaw is in a very small section of the brain. The movement for your shoulders, elbows, and fingers is scattered much more throughout the motor cortex. And we think we can actually pull out signed phrases through FMRI (functional magnetic resonance imaging).

I asked for clarity:

To clarify, you would not actually have to go through the physical sign language movements. You could just think about the specific movements, and that would be enough to trigger the motor cortex?

Thad responded in the positive:

It turns out that imagined movements also activate those regions… How much can we distinguish? Currently, we’re going to try our first experiments just on pairs of signs that are maximally different, like cold versus hot, because the motion should be very distinctive in the motor cortex. If that works, we’re going to go to phrases, and – this is all in FMRI machines – if that works, then we’ll start going to mobile interfaces like functional near-infrared imaging.

Well, I can tell you that they actually have been able to read imagined sign language from the brain.  While the full details are yet to be released – this is really hot information, fresh from the lab – the researchers have been able to distinguish between certain “maximally different” signs. 

I understand that Thad spent about 3 hours inside an FMRI machine during the test.  These researchers are serious about their craft.  More on this will be forthcoming as they publish the results, but I wanted to give you an quick update on this fascinating area of research. 

I like undergrads.  When I guest lectured in Hong Kong a few years ago, I met some very sophisticated kids who were the cream of the Asian crop.  Even so, as I lectured, they were busy with cell phones, instant messaging, sleeping, etc. – typical undergrad fare.  Tonight’s group had its share of typical undergrads too – there were a few drooping eyelids, and a lot of screen-gazing going on as I spoke.  I have come to expect this kind of audience behavior when I lecture to undergrads.  As a disclaimer, I too was a typical undergrad.  I got some of my best snoozes in Political Science – sometimes I even went to class to do my snoozing.  I imagine myself as a 20-year-old in a lecture hall, hearing some geezer go on and on about integration brokers, message warehouses, event correlation, batch file transfer…  opps, sorry.  I just put myself to sleep.  Even with a topic as dry as dust – it was still a lively session and we had a lot of fun.

I have a few observations from tonight’s venture:

1. College kids know the economy is in trouble and they are concerned about the job market and making money when they graduate.  Some of them see BPM and “process stuff” in general as a nice potential for making a difference – and a buck.
2. Some colleges are doing a great job preparing undergrads for a career in BPM.  This group had been working with BPMN, building models, learning simulation, gathering insight that some enterprises have not even started working on.  Not every college has tackled the BPM challenge.  GSU is one that has.
3. These guys are going to be tomorrow’s process owners, process analysts, process consultants.  They will be buying process tools, extolling process virtues and generally, carrying the process torch.

As I have mentioned before in this blog, there is new blood coming to market every graduation season.  You should check out what they are learning.  Imagine hiring someone who doesn’t have to ask, “Ugh… just what is a process?”  Imagine hiring someone who cut her teeth on BPMN as an undegrad…

When did you first touch BPMN?  Yeah.  Me too.  Good luck with your degrees guys.  We need you out here.  Hurry up and graduate and get those resumes on the street.  And catch a good snooze in PoliSci if you can – for old time’s sake.

New Term Warning – “Methodological syncretism” currently has 282 hits on Google.com.  Using a comparative trick similar to one which John Pescatore is famous for, I can also tell you that “whale pie” has about the same number of hits.  So, basically, this term is not well-known.  However, “methodological syncretism” is mentioned in a well-respected SAGE publication, so I can’t claim any ownership interest here (if it’s mentioned in SAGE, it must be real).  But, I do feel that I can co-opt the term for my own use in BPM.

Methodological syncretism within BPM – I’m defining this concept as, “The process by which two or more methodologies are blended to create an über-methodology that uses the best of each donor methodology toward a more effective delivery of process excellence.”  I believe this is an important concept.  In 2006, I did a case study where the enterprise was blending aspects of Six Sigma, traditional project management methodologies, and traditional SDLC.  It seemed to work for them.  Further, isn’t Lean Six Sigma a clear example of methodological syncretism?  I think it is.  I think this concept is very real and it’s probably pervasive.

Note on Methodology Engineering - There is a much more well-known concept called methodology engineering (ME) – or method engineering as it is known in Europe – which is very similar and certainly pre-dates my term.  I’m guest lecturing tomorrow night for its creator, Dr. Richard Welke – my very good friend.  Richard and I will be discussing this topic before the lecture, and we’ve been discussing it in email for the last week.  We see similarities between the two concepts but believe they are not the same.  I specifically wanted to use a term that was similar in intent to ME, but one that relaxed the definition – a lot.  ME is much more prescriptive as to how the methodologies are linked, requiring an underlying meta-model.  ME is a more well-defined approach.  My term is much looser and much less prescriptive, by design.  If ME is “the book on brain surgery,” methodological syncretism is merely “brain tinkering.”  Richard agrees with my analogy (and no, he did not suggest the analogy).  As I gather more insight, I will certainly discuss this topic further.

Call for Input – Do you see merit in methodological syncretism?  Don’t fret over the term – I don’t want to enter it into a beauty contest.  Just let me know which methodologies you’re blending.  As a thank you, I can send you a nice recipe for whale pie.

I just searched for “without winning Missouri” on Google.com.  You don’t have to be smart to search nowadays – all you have to do is enter the key snippet.  Lots of results, most saying effectively the same thing: “Wow! How did Obama win without Missouri?”  Apparently, a Democrat being elected president has always coincided with Missouri’s going Democratic (but not the other way around – Missouri has predicted a Democrat and been wrong, as several sites bemoan).  Whoopee!  A way to predict the winner!   If a Democrat won then Missouri must have gone Democrat.  But this time, it failed.

Failed indeed. This is a fine example of pure statistical hype.  Good stats classes teach ways to avoid this kind of math mistake.  Missouri was never a validated predictor of the Democratic wins, and the so-called “American opinion.”  “What?  It aligns so perfectly.  How can you say that?”  Even if the next 10 Democrats carry Missouri and win the presidency, it is still an invalid predictor in my book.  I can’t even muster a reasonable case for real covariance. This was just the same bad math that has sportscasters saying:

“Well Tom… following a line-drive single, this batter has never hit the second pitch from a left-handed pitcher on a Tuesday night in the third inning”

Richard Feynman would have loved this one.  It’s the same mentality that makes people see faces in cookies and pancakes.  The brain wants to spot patterns – it is desperate for pattern detection.  When you take a big ol’ bunch of data – like all the state-by-state election results since who-knows-when – and you start to look for patterns, you know what?  You are going to find a few patterns.  Some of those patterns may hide a great predictive model – this is what well-cross-validated discriminant analysis is all about.  However, just digging in the data to find a pattern – any pattern – is a fool’s errand.  You will always find some alignment that looks like it’s predictive, but in reality, it’s just the randomness of life at work.  What real predictive capability takes is a lot of work on defining the precise criterion you are measuring, a lot of construct validity checking, and a lot of work on cross-validation against the criterion, etc.  Oh, and a little solid theory on the relationship between the independent and dependent variables wouldn’t hurt either.  In other words, sometimes what you see is just a nice reflection in the mirror, but there is nothing on the other side.

Missouri is a cool place.  And Missourians may be so close to the pulse of America that their collective voice echoes the blended wishes of the other 49 states.  But I don’t see the proof.  I don’t see the validity that I would want to see, and, in my opinion, Missouri is not a valid oracle for the American pulse, even if it names the right presidential party for as long as I live.   And that pancake that you keep locked in that safe is not a spitting image of Barney Fife.  Sorry, it’s just not real.  Come back to me with a rock-solid, statistically sound case on why Missouri is “the bellwether” – so called – against the loose criterion “How America Will Decide” and we can talk.  Do the hard work, show me how you cross-validated your predictive, discriminant model, and prove your point, and I will back down.  My stats are rusty – you may be able to take me.  But, don’t come to me with some interesting presidential election patterns and say that proves Missouri is the guide to all-things-American. Until then, just enjoy the pretty pictures randomness can draw.

P.S. Don’t get me started on “historically the stock market has always outperformed other investments” statements being used to predict a rosy future either… not that you would anytime soon.

My usual research response has been to remind you that your tax code and all your laws are written in natural language (”oh… I see your point.  I never could understand that deduction for the profits on excess mineral rights that can be claimed by part-time fishermen who install solar panels on their second homes in July.”)  No, I won’t go there.  Instead, let me tell you about some tires I bought.

The tires are great!  My wife and I love them.  And we thought we loved the financing scheme the company gave us.  Here is what it said on the contract:

“6 months no interest/minimum payment required”

Wow!  Six months and we don’t have to pay interest or make a minimum payment.  I like that rule.  Well, we threw away the first bill and when we got the second bill, there was a charge for failing to make the minimum payment.  Come again?  I called the bank behind the promotion and they said the rule should be read as (a) 6 months no interest but (b) a minimum payment is required.  This is the natural language pitfall – semantic interpretation.

Folks, where I come from – a slash tends to be read as an “or” and distributes the verb uniformly.  This is equivalent to an airport warning, “no knives/handguns allowed.”   No one in his/her (I had to do it) right mind would interpret that to mean: (a) no knives but (b) handguns are allowed.  Try explaining the logic behind that interpretation as you wait for your orange jumpsuit and a new roommate at the nearest prison.

In a rare stroke of customer service luck, the agent immediately agreed with my interpretation.  Perhaps he is a closet linguist.  He waived the fees and in return, I am just going to send in the whole payment and pretend this event never happened.  I love those tires.  I no longer love the financing scheme.

In defense of NL, if you allow poor or ambiguous grammar you are asking for trouble.  The NL rule tools do a great job at clarifying the ontology and enforcing proper grammar.  And NL has its place. But my point is simple.  If you think natural language is a universal panacea, you are in for a surprise. We learned too much from the days of AI to forget that. 

Gartner clients: watch for our research in Q1. NL is just one of the rule representation approaches we will be reviewing in detail.

Say you have been hired to design and develop a new management system to be marketed to homeowners’ associations.  Pretty nice job, eh?  Every association on the planet is a potential customer. So you start analyzing the requirements and applying process management techniques, just like a good little analyst.

Scenario 1: "Yeah! I want a system that lets us enforce the rules.  I want to be able to track violations, archive photos of violations, issue fines and track legal proceedings.  I want this system to give me an air-tight case against any violators.  Our homeowners have rights, and those rights must be protected.  Anyone violating those rights must be punished. That’s all I care about!"

Scenario 2: "Fantastic!  I would love to have a system that lets us keep track of homeowner needs.  I want to be able to schedule the block parties, pool use, and all the other fun events we do.  I think it would be cool if we had an interactive site where we could share ideas, talk about our hobbies, our kids and our great neighborhood.  Our homeowners want a sense of community.  Anyone moving into our community is to be cherished. That’s all I care about!"

Welcome to Weltanschauung: two vastly different worldviews, two vastly different sets of expectations to meet.  Good luck building a satisfactory root definition.  While the homeowners’ association example is 100% mine,  I will credit Trevor Wood-Harper (and Lyn Antill and D.E. Avison) and their Multiview methodology research legacy as the inspiration.  Back in 1986/87, Trevor and I discussed his published example of prison systems, Weltanschauung, and root definitions.  His story was eye-opening and it stuck with me.  In fact, it would be unfair not to credit that example for the inspiration.  Sometimes, there is little difference between a prison and a homeowners’ association. It just depends on your Weltanschauung, doesn’t it.

P.S.  I love my homeowners’ association. They have the right Weltanschauung.  Now if we only had a cool system like the one in scenario 2.

I think many of us are still guilty of applying industrial disdain to our software development activities.  We tried to fix our carelessness, starting seriously in the 1980s. We tried lots of things: Participation, socio-technical analysis, etc.  We made good progress, but too much software and too many business applications still feel like they were designed by people who will never have to use the applications.  They reek of industrial disdain. And they are painful.

In my previous post, I issued a challenge to CEOs and process owners.  This challenge also extends to software developers and to system designers.  In a future post, I will talk about BPM – Business Process Management – and the concept of Lived-In Processes.  Lived-in processes are the antithesis of industrial disdain.  Lived-in processes, brought about through process discovery, validation, design, simulation, optimization, and continuous improvement, force the designer to act like users and experience the "to-be" process first-hand.  As some of Bill Rosser’s most recent research shows, empathy is a powerful partner for BPM success.  It is also the antidote to industrial disdain.

This is garbage with a twist.  Dr. Circeo – a Georgia Tech Research Institute Principal Research Scientist, and Director of Plasma Research – gave a presentation entitled: "Plasma Arc Gasification of Municipal Solid Waste." This black compound is the solid output (slag) that results when you blast municipal waste with a 7000 degree Celsius plasma beam.   Check your home oven… you know how you get all that black junk that has to be cleaned?  Well, if you are heating past 500 degrees Fahrenheit, I will be surprised. The plasma beam – ionized air passing hundreds-of-kilowatts of energy at a temperature that could slice a Honda into sandwich singles – does all kinds of things to the garbage.  It converts the wet solids into useful gasses like hydrogen, kills any pathogens deader than dead, locks-up heavy metals, and reduces all the remaining solids into a black glassy compound, 1/20th its original size.  According to Dr. Circero, the French are using this compound as road gravel and the Japanese are grinding it up to make sand.  And I was holding it in the same hand I was using for my iced tea.

If you ever get depressed, wondering how we are going to deal with all the garbage our culture has created, consider these two points from Dr. Circeo:

1. The entire process of blasting garbage with a plasma arc torch is a net-producer of energy. This is not black magic. The secret is that garbage is fuel, storing 11.31 million BTUs of heating potential per ton (according to Dr. Circeo’s presentation).  The plasma gasification process uses a comparably smaller amount of energy that releases the stored energy trapped in the garbage.  Think of it this way: A match plus a log equals a huge fire.  The energy of the log is what drives the fire. The match is just a small part of the equation.
2. Garbage is fuel.  Dr. Circeo believes it actually may be profitable one day to mine old landfills for the waste.  The net energy output might just make it attractive.  Can you imagine that?  Your neighbor discovers his house is build on an old landfill… instead of suing, he buys a new Mercedes like some 21st century Jedd Clampett.

Of course, we must address the naysayers. Yes, there are pros and cons to using plasma beams on garbage.  It’s expensive, it’s new, and it’s not how we do things.  Cons aside,  I was encouraged with this new approach to solving the problem of too much trash.  I once worked in a similar field.  I designed software for bio-reactors that would turn sewage (yeah, that really nasty stuff that you flush) into industrial-use potting soil.  Our reactors mixed de-watered, belt-pressed sludge with a carbon source (usually ground-up pine trees) and produced truck-loads of the nicest potting soil you ever put on a golf course.  Think about that the next time you are on the links.  Also, consider yourself lucky you weren’t eating when those samples were being passed around.