A pitch for introducing bigdata “system recognition”

The following is written for circulation in the “data science” research communities, on some advances in scientific methods of system recognition I’d like to share.  It starts with mention of the very nice 9 year old work published by Google on “Detecting Influenza Epidemics using search engine query data”  taken from a letter to that paper’s authors.  Take the reference to be to your own work, though, as it involves system recognition either in life or exposed by streams of incoming data.

empirical evidence of systemization

I expect a lot of new work has followed your seminal paper on detecting epidemics as natural systems.

But are there people starting to focus on more general “system recognition”,
studying “shapes of data” that expose “design patterns” for the systems producing it?

Any individual “epidemic” is a bit like a fire running it’s course, and sometimes innovating the way it spreads.   That change in focus directs attention to how epidemics operate as emergent growth systems, with sometimes shifting designs that may be important and discoverable, if you ask the right questions.  You sometimes hear doctors talking about them that way.   In most fields there may be no one thinking like doctors, even though in a changing world it really would apply to any kind of naturally changing system.

Turning the focus to the systems helps one discover transformations taking place, exposed in data of all sorts.  One technique allows data curves to be made differentiable, without distortion.  That lets you display evidence of underlying systems perhaps entering periods of convergence, divergence or oscillation, for example, prompting questions about what evidence would confirm it or hint at how and why.

Focusing on “the system” uses “data” as a “proxy” for the systems producing it, like using a differentiable “data equation” to closely examine a system’s natural behavior.  In the past we would have substituted a statistic or an equation instead.    By prompting better questions that way it makes data more meaningful, whether you find answers right away or not.   I think over the years I’ve made quite a lot of progress, with new methods and recognized data signatures for recurrent patterns, and would like to find how to share it with IT, and collaborate on some research.

Where it came from is very briefly summarized with a few links below.  Another quick overview is in 16 recent Tweets that got a lot of attention this past weekend, collected as an overview of concepts for reading living systems with bigdata.

I hope to find research groups I can contribute to.  If you’re interested you might look at my consulting resume too.  If you have questions and want to talk by phone or Skype please just email a suggested time.

Thanks for listening!    –     Jessie Henshaw

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fyi – 350 words Continue reading A pitch for introducing bigdata “system recognition”

16 Tweets on Reading #BigData for Life

Working with BigData, especially learning how to read the designs and behavioral patterns of the earth’s natural systems, its living cultures of all kinds, and to sense our roles in them, opens up a tremendous new field of understanding.  It of course also opens up very new kinds of perspectives to puzzle over, both offering to show us new paths and making it clear various reasons to question what we’ve been doing.  

This series of Tweets came out in a group somehow, mostly in this sequence today, seeming to build a framework of interconnecting points, like tent stakes and poles maybe, a design for hosting ways to do it.    ……Jessie

  1. What we talk about becomes society’s reality, so we can read #BigData for what’s happening #following_all_cultures and #resources_on_earth.
  2. And what may matter most in #BigData is going from reading abstract patterns to reading naturally occurring ones. http://synapse9.com/jlhCRes.pdf
  3. Then add the magic of learning to read the patterns #BigData reveals, as exposing the designs of the natural systems producing it.
  4. Reading #BigData for natural patterns shows you even the best data doesn’t show what systems are producing it. 
  5. No degree in #data_science will neglect pattern recognition for understanding the natural systems creating the data.http://www.synapse9.com/pub/2015_PURPLSOC-JLHfinalpub.pdf
  6. If our world #economy is causing trouble for the #earth, why do we think it helps to speed it up? #Get_real_people!

    Escher
  7. Are @google, @IBM or other #BigData #research teams learning how to read design patterns of natural systems?? http://synapse9.com/jlhCRes.pdf
  8. To start reading natural systems in #bigdata look for cultures made individually, clustering or growing from seeds.

    from PURPLSOC 2016 http://www.synapse9.com/pub/2015_PURPLSOC-JLHfinalpub.pdf
  9. Then follow recognizing nature’s cultures with learning from them, going back and forth between models

    from PURPLSOC 2016 http://www.synapse9.com/pub/2015_PURPLSOC-JLHfinalpub.pdf
  10. When reading #bigdata for behaviors of cultures also note contradictions in the news, like #jobs_going_to_Mexico and #refugees_escaping_too.
  11. #BigData exposes surprising whole system views too, #professionals managing systems of growing inequity, disruptive change and impacts too.
  12. #BigData reveals living cultures: business, economic, social, biological or ecological, etc. all either: homeless, home seeking or enjoying.
  13. As you see their forms you realize two things:1) our world is very #alive and 2) most #bigdata is too “big”, making you look for other views
  14. To read #bigdata as views of shifting cultures, alone or together, pushes a #whole_system_view for units of measure. https://synapse9.com/signals/2014/02/26/whats-scope-4-and-why-all-the-tiers/
  15. A #whole_system_view, like #studying_the_camera not what’s in its view, is how to start seeing ourselves in the data!http://www.synapse9.com/jlhpub.htm#ns
  16. Sixteen Tweets on reading our world in #BigData, it’s many moving parts, units of measure & big recognitions required.

ed note: One tweet, that became #11, was rephrased and put in a more logical location a few hours after the first posting.

jlh