Senet Microchip LoRaWAN.

Found out about this from a fortuitous meeting with Steve Ball from Senet at Xively Xperience 2015. Great presentation by Senet and Microchip regarding LoRaWAN on November 2, 2015. Outstanding technicals on how LoRaWAN works. And great demostration of the Senet network capabilities. Especially liked the hands on work with the Microchip RN2903 mote (not to be confused with the RN2843).

When I took my RN2903 kit home, got it working in downtown Boston against the overall Senet and AT&T M2M network. Very straightforward. Kudos Senet. Intrigued by the idea of use of motes in either public (ISP style) networks and private (open) networks. Was then prime for the pitch that the following group is making for TTN The Things Network.

Have a couple of ideas about how to use LoRaWAN to bridge old applications and organizations into the new world of IoT.

Cr... Time. Foods.

Anna and Charlie coming onto textural interest in foods.
Crusty bread. Crunchy apples. Crispy chips.

FutureM 2015 Boston.

Came to FutureM15 for IoT. And not disappointed by David Rose, Eric Snow of PTC, and panel on smart cities. Look for them on Twitter under #futureM15 and #IoT. Interesting to see latest in marketing... and relation to IoT. Background FutureM schedule .

Hayzlett - be bold (perhaps bolder than polite company would require).
Berners-Lee - times they are a changin'.

And okay - this piece is not from futureM - but similar viewpoint.

Xively Xperience 2015 Boston.

Attended Xively Xperience 2015 at Boston Seaport. Usually would run my own notes. But Jeff Engel at Xconomy (noted via Scott Kirsner on Twitter) has a great run down of the Xively gala.

Three scary keynotes:

• Diamantis - little things make a difference.
• James Lyne - security - no one has learned from the past.
• Ray Kurzweil - the exponential future.

Overall themes: Security. Forethought.

The Seven C's of Analysis.

Feeling adrift in the topic of analytics and data science?

There are two types of people in the world - Those who categorize and those who do not. There are many categorizations around analytics. Model based, Predictive, etc...

Five cases or levels of analysis - Decriptive, Diagnostic, Discovery, Predictive and Prescriptive - are often listed. Analyses are done on Big Data, Small Data, Rich Data and so on. Data gets classified by Velocity, Volume, Variety, Veracity and Value (5V).

What is the definition of analysis?
:Examination of structure as a basis for interpretation: is a fair working definition. The process or action definition is tantamount to taking inputs and ideas and turning them into decision and deliverables.

So how does analysis work? In the real and theory world? Past, present and future? Note that even asking the question involves a bit of analysis - deciding that time, space, quality and value are important parameters in our discussion.

Back to the seven C's. (three Chi's and four K's). Three are old:

Cheat: Know the answer for certain apriori. Like the Sting.  Or like doing the Kaggle Titanic competition from a download of the historical list of survivors. Or edX or Coursera students who use a shadow account to get the answers.

Chance: Guess the answer randomly... Not based on any "knowledge".

Chestnuts: Use rules of thumb, experts and one's gut. Experts can be consultants, practitioners, or highest paid person opinions (HIPPO). 

The next four are actually what most would call based in data science.

Coordinations: Statistics and regressions against well known dependent variables like space, time and value. f(t,x,y,z).. Note the nuance versus correlation below.

Correlations: f(v) for any vector v. - emergence - and not just related to time and space connections. Anything can be connected to anything else... in any way.

Crowd: Gather wisdom from many actors and models. Maybe using rule of thumb, or gut or any of the others, but getting it right(er) by law of averaging. Boosting might even be clumped into this crowd - so to speak.

Continuous: Iterate and apply the above in real world. Not quite like the others, this is about how the analytics get generated and acted upon. Most of the above answer the call when the need arises. They do not have "instinct" building (aka a built-in instinct) nor a process per se.

Nova Scotia Omiyage Gift Items.

Trip to Nova Scotia generally means bringing back gift items characteristic of area. Usually go with Coffee Crisp or Aero bars. Or Smarties. Sometimes even Cherry Blossom, Crispy Crunch, or Crunchie.
But if you do not have a sweet tooth there are alternatives:

Green Tomato Chow Chow.

One that has recently come up - good for almost anyone - Dulse.

Ketchup flavoured potato chips are often a favourite.
There are also local specialties like gouda from That Dutchman.
Items from Jost Vineyards.

All in spirit of omiyage.

Anna Talk. Charlie Thoughts.

The development of the kids is always interesting. Anna, two, can repeat almost everything we say (even long complicated sentences with long complicated words). She has memorized the text of books, and can "read"  them. She knows the numbers up to four or five, and can point them out (as the cardinality of a group) or count them out.

  
Charlie, four, is interested in superheros, remote control, and trains. He has started to ask about hard concepts like death, jealousy and social exclusion. He recalls events and dreams, and recounts them understandably.