Re: The DATA Act, Department of the Treasury, and Machine Learning Technologies

Point well taken.
Just don't sell the DATA Act as de facto Peer Review, that role is not sustainable de jure.
That was my reference to: http://datacommunitydc.org/blog/2013/08/selling-data-science-common-language/

--Gannon


----- Original Message -----
From: Hudson Hollister <hudson.hollister@gmail.com>
To: Gannon Dick <gannon_dick@yahoo.com>
Cc: Adam Sobieski <adamsobieski@hotmail.com>; "public-egovernance@w3.org" <public-egovernance@w3.org>
Sent: Saturday, September 14, 2013 10:09 AM
Subject: Re: The DATA Act, Department of the Treasury, and Machine Learning Technologies

Then it's fortunate that the DATA Act only mandates standardization within federal financial reporting - an area Treasury does understand.

Sent from my iPhone

On Sep 14, 2013, at 10:03 AM, Gannon Dick <gannon_dick@yahoo.com> wrote:

> 
> What are your concerns about semantic or graph-based data and medical publication libraries such as PubMed (http://www.ncbi.nlm.nih.gov/pubmed/)?  Which topics differ there with regard to graph-based data and articles and publications at other scientific libraries?
> ---------------
> Engineering and Technology can not reduce the classification complexity of Science and Math.
> Librarians at both the NLM and the LOC understand graph-based data, and that the limitations
> of the methods are on the data, not on the graphs.
> 
> Experiment:
> Search the LOC Cultural Heritage Code List for "Red Cross"
> http://www.loc.gov/marc/organizations/org-search.php
> result 5 hits: 3 International Red Cross, the American Red Cross, and an Elementary School
> in Kentucky (nobody's perfect).
> 
> Search Google for "Red Cross"
> About 808,000,000 results (0.40 seconds) 
> 
> 
> There are no data processing race conditions affecting ethical issues and 
> 
> to sell large graphs as an "epidemic of transparency" is wrong and will end very badly.
> 
> 
> The Department of the Treasury likely does not understand this at all. 
>  
> 
> ====================
> 
> What do the email addresses from scientific articles and representational state transfer (http://en.wikipedia.org/wiki/Representational_state_transfer) have to do with one another?
> 
> ---------------------------------
> 
> The key word is "transfer".  It renders REST just a little bit off center in relation
> to the Scientific Method.  For example, when you say the Earth is round instead
> of flat, you mean the Earth has *never, not for one second ever* been flat.
> 
> For all the good it does, REST can not know science from superstition.  But
> it also can not know superstition from science either.  For this reason, an email
> address is not a URI or even a top level domain identifier.
> 
> 
> =====================
> Kind regards,
> 
> Adam
> 
> 
> From: Gannon Dick
> Sent: ‎Friday‎, ‎September‎ ‎13‎, ‎2013 ‎2‎:‎33‎ ‎PM
> To: Brand Niemann, 'Adam Sobieski', public-egovernance@w3.org
> 
> Tears fill my eyes when I think of the Department of the Treasury
> poking around in Medline.  I hope the NLM took hostages against
> the possible use of left directed graphs ... email addresses
> for example ... which do not play well at all with RESTful identifiers.
> 
> but you knew that, right?
> http://datacommunitydc.org/blog/2013/08/selling-data-science-common-language/
> 
> --Gannon 
> 
> 
> 
> 
> ________________________________
> From: Brand Niemann <bniemann@cox.net>
> To: 'Adam Sobieski' <adamsobieski@hotmail.com>; public-egovernance@w3.org 
> Sent: Thursday, September 12, 2013 11:34 PM
> Subject: RE: The DATA Act, Department of the Treasury, and Machine Learning Technologies
> 
> 
> 
> Adam and others, You may be interested in the work with Semantic Medline on the new Cray/Yarc Graph Computer at our recent conference:
> 
> http://semanticommunity.info/Data_Science/Cloud_SOA_Semantics_and_Data_Science_Conference
> 
> and my recent Post for Data Science DC:
> 
> http://semanticommunity.info/Data_Science/Data_Science_DC#Story_2
> 
> Dr. Brand Niemann
> Director and Senior Data Scientist
> Semantic Community
> http://semanticommunity.info
> http://breakinggov.com/author/brand-niemann/
> http://datacommunitydc.org/blog/2013/08/cloud-soa-semantics-and-data-science-conference/
> 703-268-9314
> 
> From:Adam Sobieski [mailto:adamsobieski@hotmail.com] 
> Sent: Thursday, September 12, 2013 12:41 AM
> To: public-egovernance@w3.org
> Subject: The DATA Act, Department of the Treasury, and Machine Learning Technologies
> 
> e-Governance Community Group,
> 
> At the nation‘s first federal open data policy conference, hosted by the Data Transparency Coalition, over 450 registrants had a first-ever opportunity to glimpse the future of open data policy at Data Transparency 2013 on September 10, 2013, in Washington - and see crucial reforms happening in real time.
> 
> On May 21, 2013, the Digital Accountability and Transparency Act, or DATA Act, was simultaneously introduced in the U.S. House (H.R. 2061) and Senate (S. 994).  The DATA Act modernizes the United States government, the computerization of the United States government, its processes, documents and data storage, and empowers the Department of the Treasury to set government-wide structured data formats for reports on awards, budget actions, payments, and financials.
> 
> Analysis of government documents and processes, towards formulating new efficient and optimal systems and data architectures, of use throughout the federal government, can be enhanced by machine learning heuristics and technologies.  Throughout the federal government are models, systems, taxonomies and ontologies pertinent to data transparency and pertinent to transactions and expenditures.  The efficient and optimal cataloguing and indexing of public data, efficient and optimal taxonomies and ontology, can facilitate numerous software features and uses of public data, for instance the indexing, search and retrieval of public data as well as data routing systems such as pubsub systems.  Machine learning technologies can enhance the analyses of government processes and data for formulating solutions including as aforementioned at the Department of the Treasury.
> 
> 
> 
> Kind regards,
> 
> Adam Sobieski
> 
> 
> P.S.: Some machine learning hyperlinks:
> 
> Machine Learning Systems 
>     * Supervised Learning 
>     * AODE 
>     * Artificial Neural Network 
>     * Backpropagation 
>     * Bayesian Statistics 
>     * Naive Bayes Classifier 
>     * Bayesian Network 
>     * Bayesian Knowledge Base 
>     * Case-based Reasoning 
>     *                          
>

Received on Saturday, 14 September 2013 15:40:30 UTC