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

Gannon,

 

What are you talking about?  Was that a poem or a poetic “tweet”?


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?


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?

 


 

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