{"id":3380,"date":"2016-03-24T19:37:11","date_gmt":"2016-03-25T00:37:11","guid":{"rendered":"http:\/\/synapse9.com\/signals\/?p=3380"},"modified":"2016-03-24T19:38:52","modified_gmt":"2016-03-25T00:38:52","slug":"pitch-for-system-recognition","status":"publish","type":"post","link":"https:\/\/synapse9.com\/signals\/pitch-for-system-recognition\/","title":{"rendered":"A pitch for introducing bigdata “system recognition”"},"content":{"rendered":"

The following is written for circulation in the “data science” research communities, on some advances in scientific methods of system recognition\u00a0I’d like to share. \u00a0It starts with mention of the very nice 9 year old work published by Google on “Detecting Influenza Epidemics using search engine query data<\/a>”\u00a0 taken from a letter to that paper’s authors. \u00a0Take the reference\u00a0to be to\u00a0your own work, though, as it involves system recognition either in life or exposed by\u00a0streams of incoming data.<\/em><\/p><\/blockquote>\n

\"\"<\/a>
empirical evidence of systemization<\/figcaption><\/figure>\n

I expect a lot of new work has followed your seminal paper on detecting epidemics as natural systems.<\/h3>\n

But are there people starting to focus on more general \u201csystem recognition\u201d,
\nstudying \u201cshapes of data\u201d that expose \u201cdesign patterns\u201d for the systems producing it?<\/h4>\n

Any individual \u201cepidemic\u201d is a bit like a fire running it\u2019s course, and sometimes innovating the way it spreads.\u00a0 \u00a0That 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.\u00a0 You sometimes hear doctors talking about them that way.\u00a0\u00a0 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.<\/p>\n

Turning the focus to the systems helps one discover transformations taking place, exposed in data of all sorts.\u00a0 One technique allows data curves to be made differentiable, without distortion.\u00a0 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.<\/p>\n

Focusing on \u201cthe system\u201d uses \u201cdata\u201d as a \u201cproxy\u201d for the systems producing it, like using a differentiable \u201cdata equation\u201d to closely examine a system\u2019s natural behavior.\u00a0 In the past we would have substituted a statistic or an equation instead.\u00a0 \u00a0\u00a0By prompting better questions that way it makes data more meaningful, whether you find answers right away or not.\u00a0 \u00a0I think over the years I\u2019ve 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.<\/p>\n

Where it came from is very briefly summarized with a few links below.\u00a0 Another quick overview is in 16 recent Tweets<\/a> that got a lot of attention this past weekend, collected as an overview of concepts for reading living systems with bigdata.<\/p>\n

I hope to find research groups I can contribute to.\u00a0 If you\u2019re interested you might look at my consulting resume<\/a> too.\u00a0 If you have questions and want to talk by phone or Skype please just email a suggested time.<\/p>\n

Thanks for listening! \u00a0 \u00a0– \u00a0 \u00a0\u00a0Jessie Henshaw<\/p>\n

___________________________________________<\/p>\n

fyi \u2013 350 words<\/p>\n

To start from the beginning, in the 70\u2019s I made some fundamental discoveries about the physics of individual natural systems (1), recognizing a design pattern \u201chidden in the tails\u201d of data that are normally discarded\u2026\u00a0 In this case I\u2019m referring to the *actual tails* of the data, not statistical ones, all those curious periods of \u201cregular non-linear change\u201d you find almost anywhere things are beginning or ending. \u00a0\u00a0I found it often offered verifiable suggestions regarding patterns of individually developing natural system designs.<\/p>\n

Once you identify that pattern of regular non-linear tails as a signature of beginning or ending\u2026 when seeing it you then look for what system is either \u201cpicking up\u201d or \u201ctrailing off\u201d, and often find it.\u00a0 \u00a0\u00a0There is useful technique one can use, but just asking the question often results in finding great leads by itself, and gives hints to what other viewpoints one could learn more from.\u00a0 \u00a0In that way it\u2019s just a new variation on familiar forensic techniques, a \u201cpattern search\u201d method, like: see a footprint => look for the person.<\/p>\n

When this work was first done it seemed so odd to other scientists it was completely dismissed.\u00a0 That also continues to be the case in most fields, that scientists often can only discuss theory and statistics.\u00a0 What\u2019s new today is that business people really need their mountains of data to become more meaningful, which makes a lot of technically very capable people available, familiar with some of Alexander\u2019s pattern language, and likely to be less stuck on abstract theories, \u2026as a possibly better audience.<\/p>\n

In the 90\u2019s I developed a physics theorem for it, confirming the shape of data tails I\u2019d identified intuitively before, a sign of progressive organizational change indicating the beginning or ending of individual natural system development(2).\u00a0\u00a0 To demonstrate refined data analysis methods I did various system recognition studies using historical data sets (3) using LISP algorithms I developed (4).\u00a0 A paper on it was published and included in a pattern recognition book(5) but didn\u2019t generate any interest.\u00a0 \u00a0The software platform used also changed faster than I could update my routines, so all that work stopped.\u00a0 In the 00\u2019s I shifted to more empirical studies, focusing on sustainability accounting problems. patterns of energy use and cultural organization and transformational change.\u00a0 The methods led to my other important research paper, on energy accounting for businesses as whole systems(6), all of which led to some years of related work at the UN.<\/p>\n

For the UN I made proposals on the design of world business accounting systems, using bigdata for system recognition and a model for inclusive sustainability accounting to create a true guidance system for the UN\u2019s SDG\u2019s(7).\u00a0 \u00a0To make a better record of the work I turned to presenting it as a general pattern language for working with natural systems, producing two good papers each seemingly quite well received at the 2015 meetings of PURPLSOC and PLoP (8).<\/p>\n

For a different kind of quick overview of concepts my collection of recent Tweets on BigData, that got a lot of response recently, as 16 Tweets on Reading #BigData for Life<\/a>(9),<\/p>\n

    \n
  1. http:\/\/debategraph.org\/Details.aspx?nid=360233<\/a><\/li>\n
  2. http:\/\/www.synapse9.com\/drtheo.pdf<\/a><\/li>\n
  3. http:\/\/www.synapse9.com\/drwork.htm<\/a><\/li>\n
  4. http:\/\/www.synapse9.com\/drstats.htm<\/a><\/li>\n
  5. http:\/\/www.synapse9.com\/pub\/1999_FeaturesOfDerivativeCont.pdf <\/a><\/li>\n
  6. http:\/\/www.mdpi.com\/2071-1050\/3\/10\/1908\/<\/a><\/li>\n
  7. http:\/\/www.synapse9.com\/jlhpub.htm#un<\/a><\/li>\n
  8. http:\/\/www.synapse9.com\/jlhpub.htm#pl<\/a><\/li>\n
  9. http:\/\/synapse9.com\/signals\/2016\/03\/20\/16-tweets-on-reading-bigdata-for-life\/<\/a><\/li>\n<\/ol>\n

    _________<\/p>\n

    jlh<\/p>\n","protected":false},"excerpt":{"rendered":"

    The following is written for circulation in the “data science” research communities, on some advances in scientific methods of system recognition\u00a0I’d like to share. \u00a0It starts with mention of the very nice 9 year old work published by Google on “Detecting Influenza Epidemics using search engine query data”\u00a0 taken from a letter to that paper’s … Continue reading A pitch for introducing bigdata “system recognition”<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_crdt_document":"","footnotes":""},"categories":[3,6,36,8,35,11,12,13,14,16],"tags":[],"class_list":["post-3380","post","type-post","status-publish","format-standard","hentry","category-among-best-2","category-mail","category-natural-patterns","category-theory","category-pattern-language","category-research","category-scitheory","category-stories","category-syn9","category-whattodo"],"_links":{"self":[{"href":"https:\/\/synapse9.com\/signals\/wp-json\/wp\/v2\/posts\/3380","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/synapse9.com\/signals\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/synapse9.com\/signals\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/synapse9.com\/signals\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/synapse9.com\/signals\/wp-json\/wp\/v2\/comments?post=3380"}],"version-history":[{"count":3,"href":"https:\/\/synapse9.com\/signals\/wp-json\/wp\/v2\/posts\/3380\/revisions"}],"predecessor-version":[{"id":3383,"href":"https:\/\/synapse9.com\/signals\/wp-json\/wp\/v2\/posts\/3380\/revisions\/3383"}],"wp:attachment":[{"href":"https:\/\/synapse9.com\/signals\/wp-json\/wp\/v2\/media?parent=3380"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/synapse9.com\/signals\/wp-json\/wp\/v2\/categories?post=3380"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/synapse9.com\/signals\/wp-json\/wp\/v2\/tags?post=3380"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}