Transforming Healthcare Through Use of Clouds, …Transforming Healthcare Through Use of a Universal Exchange Language, Universal Semantics, and Universal Infrastructure for Big Data

Transforming Healthcare Through Use of a Universal Exchange

Language, Universal Semantics, and Universal Infrastructure

for Big Data and MetaCloud (Cloud of Clouds)

The UEXL Consortium Combining the Technologies

Quantal-UEL, Kodaxil, and Fluxology Barry Robson - Quantal Semantics,

Avner Levy - Kodaxil

Paul Peters - Fluxology

RETHINK UNIVERSALLY

FLUXOLOGY METACLOUD

BUSINESS INTEGRATION CLOUD OF CLOUDS

KODAXIL UNIVERSAL-

MEANING CODE FOR NATURAL

LANGUAGE

QUANTAL SEMANTICS

UNIVERSAL EXCHANGE LANGUAGE

DATA SECURITY AUTHORIY CONSENT

DATA MINING A.I. for DECISION SUPPORT

WORLD WIDE WEB 3

The Semantic Web

Common (not all the collective!) interests underlying the consortium:- Linguistics & Semantics World Wide Web Encoding by Prime Number Theory Network and Knowledge Topology Artificial Intelligence

Overview

• The U.S. Health Information Exchange Vision

• Problems with Interoperability Standards

• Future of Health Information Exchange

• Solutions to interoperability problems using Metadata and a Secure Health Data Cloud

• Opportunities resulting from use of Metadata

© Barry Robson and Quantal Semantic Inc.

In a nutshell : stop building a Tower of Babel for health Information!

The PCAST Report


U.S. President’s Council of Advisors on Science & Technology (PCAST) produced report entitled:

“Realizing the Full Potential of Health Information Technology to Improve Healthcare

for Americans: The Path Forward” Released in December 2010

• “In other sectors, universal exchange standards have resulted in new products that knit together fragmented systems into a unified infrastructure.”

• “The resulting ‘ network effect’ then increases the value of the infrastructure for all, and spurs rapid adoption.”

• “By contrast, health IT has not made this transition.”

• “The market for new products and services based on health IT remains relatively small and undeveloped compared with corresponding markets in most other sectors of the economy, and there is little or no network effect to spur adoption.”

The PCAST Concerns

and so they call for an XML-like

“Universal Exchange Language”


Problems with Standards Approach

Diverse Semantics (vocabulary + syntax complexity)

SNOMED LOINC

ICD-9

ICD-10 UMLS

MeSH

NextBio RxNorm

CPT

HCPCS NDC

HL7 CVX

GALEN


Complex Infrastructure to Protect Data

Nationwide Health Info

Exchange

Health Info

Provider 1

Health Info

Provider 2

Health Info

Provider N

Data Sharing

Agreement

Data Sharing

Agreement

Data Sharing

Agreement

Data Sharing

Agreement

Data Sharing

Agreement

Data Sharing

Agreement

Medical Information Comprises many types of “Rules” as Statements about the World

• They Need to be InterconvertIble or Combinable.

• A patient record is a collection of simple or joint incidence rules.

– They are observations (usually) counted once, in the sense of contrast with statistical summary rules drawn from many patients.

– An incidence rule may be probabilistic, notably for uncertainty in measurements (e.g. confidence interval, P(Systsolic_BP(mmHg):=140+/-5) = 0.95)

• A shred of disaggregated patient data is a simple or joint incidence rule.

• A chunk of data consented for data mining to an elected level of detail , precision, of resolution is a simple or joint incidence rule.

• A metric of Evidence Based Medicine, Comparative Effectiveness Research, or epidemiology is a probabilistic summary rule.

• A rule for a clinical decision support system is (ideally) a probabilistic rule.

• An OWL, or RDF representation from the Semantic Web is a rule.

• A relatively raw extract from an authoritative biomedical text is a rule.

• A statement in a universal natural language is a rule.


Improved Quality of

Care at Lower

Cost

Patient/PHR

Provider

UEL

Development of Safer & More

Effective Drugs

CDC Improved Public Health Surveillance

A Universal Exchange Language (UEL)

EHR

Patient-UEL

UEL-Rules

Pharma

Payer

Lower Costs

Use of Evidence-Based Medicine

Health Researcher

Secure Health Data Cloud


Build a Universal Exchange Language In pursuit of Universal Best Practice and in the green field approach that

PCAST appeared to imply, let us return to fundamental principles.

Dirac Notation happily looks like a natural semantic extension to XML - but it is an algebra for observations and measurements, and probabilistic inference from them.

The methods of theoretical physics should be applicable to all those branches of thought in which the essential features are expressible with numbers. Paul A. M. Dirac, Nobel Prize Banquet Speech, 1933


< SUBJECT LOGICAL EXPRESSION |RELATIONSHIP LOGICAL EXPESSION| OBJECT LOGICAL EXPRESSION>

operator expression <bra vector expression| |ket vector expression>

The trick in applying this to the everyday classical world is to use not wave mechanics but a broader class of imaginary algebra for quantum mechanics that Dirac also developed, so founding modern particle theory.

http://www.newworldencyclopedia.org/entry/Image:Dirac.gif

QS Inc. Prototype for Health Info Exchange

Health Info Producer

Disaggregated Data (shredded) as UEL in private or public Cloud

A common Second Language UEL Approach to Interoperability

Healthcare Analysts &

Researchers

Patient &

Care Providers

Fine-grained Consent Mechanisms

Mechanisms for Certification, Authentication & Need-to-Know-Basis

Clinical Decision Support Systems


UEL

UEL

UEL

UEL

DATA KET TAGS carry encrypted chunk of data,

need nothing else.

ENTRY ID BRA TAGS identify other entry ID bra tags

that are isomorphic to it

JOINER KETBRA TAGS join the above by a chain of

irreversible encryption, looking at very small parts of join strings that are different on the joiner + ID tags

QuantalUNSHRED Aggregates and decrypts tags to

generate individual record

QuantalMINE data mines

QuantalSHRED generates disaggregated and

encrypted tags for specific individual health information

SUMMARY RULE BRA-RELATOR-KET TAGS

are probabilistic semantic triples along with association value to

compute medical measures

CONSENTED INCIDENCE RULE BRA-RELATOR-KET

TAGS are “rules” about one patient,

obscured to extent determined by the consent

HEALTH INFORMATION CLOUD PATIENT RECORDS

may carry fine grained consent

QUERIES relevant to patient

QuantalTHINK inferences from rule tags and

generates new rule tags by inference from them

QuantalMASTER monitors, audits, curates, and

purges tags

The Current Prototype in More Detail


Grander Vision for Health Info Exchange

Disaggregated Data (shredded)

as UEL in multiple private and

public Clouds

Healthcare Analysts &

Researchers

Patient &

Care Providers

Fine-grained Consent Mechanisms

Clinical Decision Support Systems

© UEXL Consortium

PUB L I C I N T E R N E T

Quantal X-tracts

“Text Extract Search-& -

Spawn AutoSurfers”

Medical Semantic

Web

XML UEL for Certification, Authentication & Need-to-Know-Basis

UEL UEL

UEL

UEL

KODAXIL Universal-

Meaning Code for Natural Language

FLUXOLOGY METACLOUD (CLOUD OF CLOUDS) INTEGRATION

TB Dyspnea

Is Mother

Is Caused By

Exudative Effusions in X-rays

Indicates

Patient

Patient

has has

Bronchitis Lung Cancer Emphysema

Is Caused By

Is Caused By

Is Caused

By

has has

Age = 9

mo

Is

Smoking

Has

behaviour

Causes

Causes Causes

Asia Area X

Associated

with

Visited = 12 mo

Clinical Decision Support Need Probabilistic Semantic “Rules”.


Opportunities with Metadata Approach

• Summary Statistics Tags as “rules” with probabilities and association constants allow calculation of standard medical metrics, and can be used directly in inference networks

• A metadata language extending the idea of an XML Attribute is readily extended to refer to external definitions

<QuantalUEL3Wed context:=‘adverse drug reaction’ drug:=CODE:=SNOMED-CT:=‘=373270004/Penicillin - class of antibiotic - (substance)’:=www.uel.org/drug/ Pfwd:=0.05 | causes:=‘Causative agent’:=www.uel.org/causes/:=assoc:=1.5 | allergy:=CODE:=SNOMED-CT:=2.16.840.1.113883.6.96:= ’106190000/Allergy /:246075003’ :=www.uel.org/allergy/ Pbwd:=0.001?:=www.uel.org/allergy. prescription QuantalUEL3Wed>

Two-way probabilities based on data mining and text analytics. For example

“obesity causes type 2 diabetes” “type 2 diabetes causes obesity”

are both believed true, but have different probabilities.


A PROBLEM: THE CYCLIC TOPOLOGY OF WORDS

• <QSDFXtractor26 "`The human _brain |îs `the center of| `the human nervous _system

[0http://en.wikipedia.org/wiki/Nervous_system]; `The human _brain |^has `the `same `general _structure as| `the _brains |of| òther mammals [0http://en.wikipedia.org/wiki/Mammal]; `The human _brain |îs larger than êxpected on `the basis of| _body _size |among| òther primates [0http://en.wikipedia.org/wiki/Primate] [1(0)http://www.ncbi.nlm.nih.gov/pubmed/17148188] [2file:input.txt#cite_note-Brain-num-1]" (source:='http://en.wikipedia.org/wiki/Human_brain' time:='Wed Oct 3 14:02:19 2012' extract:=0) QSDFXtractor26>

• Phrases may be moved around from the source text so that each chained component A |relationship | B can be extracted as a semantic triple and hence as a bra-relator-ket. • X-tracts retain both direct links to other web pages, and links deduced from the reference papers and books cited.

– X-tracts automatically surf the Web and spawn more extracts. – The source and X-tract number from a source is included so that full context can always be recovered when

converting X-tracts to rules for automated inference.

• BUT MEANINGS STILL DEPEND ON CONNECTION TOPOLGY BETEEN TEXT


• We can generate hundreds of rules in seconds for A.I. style inference by automatically surfing the Web, and keep context by text-linkage topology and cross references, but as with OWL and RDF definitions, and in a dictionary, thesaurus, and encyclopedia, words are defined by words, and meanings of statements by human experts hold themselves up by each other’s bootstraps. Sometimes that is enough, but can it be more?

© Avner Gerard Levy 2006-2012 16

KODAXIL

KNOWLEDGE

OBJECTS

DATA

ACTIONS

eXTENSIBLE

INTEROPERABLE

LANGUAGE

KODAXIOM

KODAXIL

OBJECTS

DESCRIPTOR

AND

eXTENSIBLE

INTEROPERABLE

ONTOLOGY

MODELER

™

Challenges met while addressing shared medical data

Semantic alignment and matching, and knowledge extraction from heterogeneous medical data sources: converting to common vector

• Various input formats: HL7, SNOMED, LOINC, etc.

• Various representations of simple objects:

– 1 (212) 222-3345 212334

– 12/2/58, 02/12/1958

• Various DBs: Oracle, DB2, IDMS, SQL Server, other non SQL flat files

• Various measurement systems

• Same objects, different names, languages / idiosyncrasies.

• Synonyms, acronyms, abbreviations, misspellings

• Schemas (xsd): namespaces, schema layout, and terms

• Thousands of attributes

• Billions of records

© Avner Gerard Levy 2006-2012


Kodaxil: a generic natural language

A group of words is considered as a semantic entity if it has a one word representation in another language:

– English: 'Piece of furniture’ – French: 'Meuble‘ – Spanish: ‘Mueble’

It may comprise multiple morphemes or lexemes, presented together, or not: – “A piece of OLD furniture”

Kodaxil is multilingual by design, but it is free from natural language, and measurement system connotation: Words in various languages are mapped with the same identifier

ONE semantic entity = ONE word for all languages

A Generic Natural Language is a natural language not tied to any local natural language, for data interchange purposes for instance” Towards a Theory of Natural Language Interfaces to Databases (2003) Popescu, Etzioni, Kautz

The French 'monter‘ is translated as: 'go...up' in English, and ‘up…go’ in Mandarin ‘上去’.


How Kodaxil brings semantics to data

Elements frequently found in data interchange are identity, ratio, and quantity

A study of many frameworks (including uncefact, wordnet, togaf, ebXml, UDEF, UDR, opencyc) shows that most of them consider time, money, and other quantities as different entities. But from a formal standpoint, there is strictly no difference between a Temperature expressed in degrees (Celsius, Fahrenheit, Kelvin, etc.), a currency expressed in dollar or in Yen, or time, or a distance expressed in yards: all can be expressed as: ‘UNIT’, ‘UNIT RATIO’, ‘SIGN’ and ‘VALUE’, or a set of Kodaxil words specifying all of the above, and so that the value is always an integer.

-The ‘Value’ element, expressed as a base64-encoded string of digits; its location is explicit, and a parser knows how to compute the offset to the next meaningful piece of information (e.g. next Kodaxil word or construct). For redundancy, the string is enclosed between the reserved word “quantity” and “end-quantity” – but the value can be stored ‘as is’ in a database.

-A temperature such as 98.6F will be expressed as:

Kodaxil reserved words for ‘quantity’, ‘degree Fahrenheit’ (unit), ‘1/10 ‘ (Unit ratio), ‘+’ sign. Followed with a value of 986, since the unit ratio is 1/10, and the ‘end-quantity’ reserved word.

Kodaxil: Why? Oliver Goldman, Jon Ferriaolo, Larry Masinter, Adobe Systems, Inc. all three members of the w3c, on the Binary Interchange of XML Infosets:

http://www.w3.org/2003/08/binary-interchange-workshop/28-adobe.pdf

“Encodings of XML infosets, or encodings which are not isomorphic to the XML infoset, will lead to poor interoperability and confusion. Thus, we believe that the W3C should endorse at most one alternative encoding of XML infosets”

“The web has a crying lack of machine-processable information” Tim Bray, co-inventor of XML


Interoperability is based on similar semantics, and similar semantics are based on similar words, and word arrangements

XML "does not know“ that: <EmployeeNumber>111-222-333</EmployeeNumber>

is the same object as: <Employeenumber>111-222-333</Employeenumber>

And much less that: <temperature unit="Fahrenheit" value="98.6">

is identical to: <temperatura unità="Celsius" valore="37">

The goal of Kodaxil is to implement: To represent documents, Global Text Interoperability, and objects, data, processes • Global Data Interoperability knowledge, database and database entities in the same way worldwide

So. any of these, composed in some language,

in Russian for instance, can be understood immediately

in any language such as English, Chinese, Spanish or Arabic. ‘write once, use everywhere’










Communication

• Concept: abstract idea or mental symbol. denotes all of the objects in a given category or class of entities,

• Humans communicate mainly using words, the building blocks of ideas,

objects of exchange for sharing information and communication

“An ontology is a specification of a conceptualization”

A single concept can be expressed by any number of languages. Concepts are independent of language

Translation is possible because words in various languages have identical meaning, they express one and the same concept

A conceptualization is an abstract, simplified view of the world that we wish to represent for some purpose. T. R. Gruber: A translation approach to portable ontologies. Knowledge Acquisition, 5(2):199-220, 1993

http://ksl-web.stanford.edu/KSL_Abstracts/KSL-92-71.html

Languages let express thoughts, feelings, knowledge, action, concepts, define objects The syntax and grammar of a language manage its sequence of words and the rules of association, composition, and transformation: “It’s raining" – Present continuous “Yesterday, it rained”-Preterit – rain + ‘ed’ / morpheme "Did you have any meal yesterday?" Interrogative form









Words, Signs, Symbols

Humans also use signs and symbols (∀x:Cat) (∀y:Fish) like(x,y) Humans understand the words they use are parts of speech:

– verbs, nouns, adjectives, adverbs, articles, etc.

• A word alone means nothing without a "context" • Contexts are elements of written matter or speech tied to a particular word

or passage. They help precise the meaning of a word, or a group of words. • Contexts may sometimes reference past events (temporal contexts), or the

current spatial-temporal universe in which a text or discourse is read or heard (Anaphora)

• A context may itself be part of a "hyper-context“: – Medicine, Aerospace, everyday life, etc. jargon

• Software developers combine ordinary words to create variable names:

– Page_Load, Load_Page, CustomerName, BillingAddress


SEMANTIC INVARIANTS • In spite of a syntactic variance, it is still possible to form semantic entities, because

all languages present similarities in semantics: semantic invariants can be found both in the classes of words (according to their role/function in a phrase), and semantic categories within semantic classes

The above classes of words are found in every language, as parts of speech. They are SEMANTIC INVARIANTS

Semantic invariants are part of the encoding of Kodaxil words when it is obvious that semantics are really invariant, as classes of words are, for instance, and wherever space constraints allow. Each word is created in the "default" context, with the word acting as a key, and additional semantic information is placed there (Actually, the denomination "semantic entity" or "lexeme" is more appropriate than "word"). Limits of a knowledge representation – another way to express ideas


How Kodaxil brings semantics to data

Elements frequently found in data interchange are identity, ratio, and quantity

A study of many frameworks (including uncefact, wordnet, togaf, ebXml, UDEF, UDR, opencyc) shows that most of them consider time, money, and other quantities as different entities. But from a formal standpoint, there is strictly no difference between a Temperature expressed in degrees (Celsius, Fahrenheit, Kelvin, etc.), a currency expressed in dollar or in Yen, or time, or a distance expressed in yards: all can be expressed as:

‘UNIT’, ‘UNIT RATIO’, ‘SIGN’ and ‘VALUE’, or a set of Kodaxil words specifying all of the above, and so that the value is always an integer.

-The ‘Value’ element, expressed as a base64-encoded string of digits; its location is explicit, and a parser knows how to compute the offset to the next meaningful piece of information (e.g. next Kodaxil word or construct). For redundancy, the string is enclosed between the reserved word “quantity” and “end-quantity” – but the value can be stored ‘as is’ in a database.

-A temperature such as 98.6F will be expressed as:

Kodaxil reserved words for ‘quantity’, ‘degree Fahrenheit’ (unit), ‘1/10 ‘ (Unit ratio), ‘+’ sign. Followed with a value of 986, since the unit ratio is 1/10, and the ‘end-quantity’ reserved word.

Semantic Objects in IT (all use words) The following examples represent most of the semantic objects found in IT: • The President goes to China • Tomorrow, wind will be east 15 to 20

mph with gusts to 25 mph. Temperature will remain in the low 50s for New Hampshire

Requirements are critical to the software development process:

The ‘Save’ button must be enabled for Managers, it must be disabled for other staff The view must be sorted by employee name, in ascending order These semantic objects: information, knowledge, data, facts, rules, requirements, specifications, and configurations share a common feature: they can be expressed using words, and understood owing to grammar and syntax. Even numeric data can be embedded within phrasal context, providing information such as the type of data processed, as shown below:


A script, or an algorithm, uses the imperative mode. For instance, ‘b2 – 4ac’ can be represented by a sequence of statements: 1/ MULTIPLY ‘b’ BY ‘b’ 2/ MULTIPLY ‘a’ BY ‘c’ 3/ Subtract the result of 2/ from the result of 1/


Sentiment Analysis (ML), Threats Consumer profiles Retargeting, pre-targeting & look-alike targeting Conversion machines

Global social medium, with on-the-fly translation. Project discovery worldwide Profile discovery worldwide

polarity of text impressions/image political party

Global Medical Expert Systems

• Representing documents in the same way worldwide.

• Harmonizing databases

Conclusion: understanding, deducting…

• Using Kodaxil and other formalisms such as <BRA|KETS> to convert the various sources of medical information in the USA in their various encodings to a common language neutral vector results in a global base of medical data, and invites other countries to implement a similar effort.

• This effort produces a database, larger than the initial one by orders of magnitude, where ourselves and other teams of researchers can extract medical knowledge and improve both the accuracy of prognosis, and the quality of medicine worldwide as these expert systems may act as a teaching tool.


Big Data Challenges Big Data = High Performance Computing

Many computing infrastructures are suffering from dependencies which have grown so complex normal design patterns do not apply anymore. In stead of top-down management a much more simple way involves a system of search-agents reporting to a central system to maintain a real-time dependency map. Along with virtualization and the ongoing maturation of AI, a gradual shift is happening away from the old way of monolithic application building to network-centric service-oriented application networks. Apply evolutionary principles and application building shifts to self-regulating swarms of functional agents with goal-driven emergent hierarchies.. mirroring and end user’s role and persona in a shift from structured programming to adaptive unstructedness.

One size fits all

Why clouds ? • Machines are becoming logical, hardware becomes software

• Reduced price of abstraction leading to virtualization

• Virtualization leads to more flexibility with converging trends in – Hardware virtualization (run on a logical machine)

– Operating System virtualization (run multiple logical machines)

– Storage virtualization (e.g. Storage Area Network, Archiving)

– Network virtualization ( https://www. some-place.geo )

– Desktop virtualization (log in anywhere)

– Data virtualization (Master-Data-Management–aaS)

– Application virtualization (Software-aaS, Composite Apps)

• Flexibility leads to agility, scalability, reliability, reduced costs, increased security and operations.

• Device - and location independence.. *-as-a-Service, Thick-clients

Network of Networks

Abstracted models Concrete reality

Why adaptive ? • Adaptive = real-time automatic autonomous flexibility

– Continuous improvement without disruption (perpetual beta)

– Self-optimizing relaying for collaborative platforms

– Demand-driven pay-per-use augmented reality

• Dynamic serviceability achieves more with less in 24x7x365 follow-the-sun economics

• Dynamic location-based gathering, pre-fetching, caching – Location-aware ambient services (e.g. office assistant)

– Pre-loading medical records to place of accident

• History matters.. physical limits of existing and future environments can not keep up with speed of progress – Command and Control can not deal anymore with current levels of ICT and business complexity

– Keeping the lights on - when costs equals value ICT often defines a competitive dead end

• Technological convergence of mainstream computing, with mobile, with ambient / pervasive computing, with telematics

• Functional convergence of business automation with technical automation – Mix telephony, computer, electricity, television networks with processing, memory, sensors..

decentralized processing power.

• Proximity matters

Modeless emergent hierarchies for a Universal Infrastructure

Irreducible complex building blocks for unstructured data models

Location-aware Infrastructures • Decentralized federation-based virtualization using artificial

organic behaviour

• Relevance-lifecycle-driven data exposure • Goal-driven functional composite application hierarchies • Collaboration-driven technical composite application hierarchies • Activity-driven instantiation, scalability and proximity of

functional Lego-bricks for optimized relaying and resource usage

• Adaptive virtualization using a semi-static physical infrastructure. Hardware turns logical and non-local.

• Functionally semi-static, yet technically semi-dynamic application infrastructure using adaptive virtualization

• Generalized composite applications using adaptive application infrastructure (catalogue of functions)

• Personalized enterprise mash-ups using composite applications • Smart clients enabling any kind of access to mash-up desktops

Make things as simple as possible, but not simpler – Albert Einstein

Traditional Clouds

• Clouds, public or private, dynamic infrastructures, space based computing, grids, service fabrics and application tapestries are solutions of reduced static behaviour, at best a subdivision of adaptive clouds. They do not conflict, but are a functional lock-in.

• “Virtualized applications can reduce the cost of testing, packaging and supporting an application by 60%, and they reduced overall TCO by 5% to 7% in our model.” – Gartner

• “While green issues are a primary driver in 10% of current data center outsourcing and hosting initiatives, cost reductions initiatives are a driver 47% of the time and are now aligned well with green goals. Combining the two means that at least 57% of data center outsourcing and hosting initiatives are driven by green.” – Gartner

• Case studies on smart uptime planning show a reduction from 24 to 15 hours.

Adaptive Clouds

Use virtualization to mix mobility and non-locality leading to the following: • Reduced network load by computation bundling • Overcome network latency by proximity optimization • Overcome networking and interoperability bottlenecks

by dynamic encapsulation (automatic request ordering and prioritization for a local flock)

• True dynamic adaptation depending on environment • Asynchronicity allows for parallel processing (distribute

requests over a dynamically scalable application, batch up requests for delayed processing)

• Autonomy means fewer dependencies, increased robustness and fault-tolerant

• Reduced maintenance costs due to compartmentalization, autonomy and generic runtime

• Exploit complexity instead of avoiding it. • Future proof. The missing link for wide-scale adoption

for mixes with Augmented Reality, Internet Of Things, Mobile and the Real-Time Web

Inter-Cloud Infrastructure • One centralized registry to deal with universal non-uniform legalities, privacy,

security, interoperability, normalization, translation, transformation. • Global-scale service provisioning should follow these ubiquitous principles:

– Universal Accessibility: every entity should be made accessible anywhere-anytime – Universal Identification and Naming: every entity should be uniquely identifiable – Universal Explorability: every entity should be self-describing – Universal Data: all data exchanged between applications should be self-describing – Trustworthiness customization: every entity should be able to adjust or to expose

adjustment rules regarding security, privacy, integrity, compliancy and certification aspects based on the interactions in which it operates

– Non-Functional Aspects: elementary non-functional aspects such as quality, governance, accountability, resilience, availability, and integrity should be properly supported in various degrees

• To realize above shoulds and get from the A to B and onwards to B2B2B2 .. • A Virtual Enterprise is a temporary alliance of enterprises that come together to

share skills or core competencies and resources in order to better respond to business opportunities, and whose cooperation is supported by computer networks.

Federation: virtual reunion

A centralized smart registry of • Contracts one or more statements of intent

• Circles of Trust among one or more consortiums

• Certificates adhering to one or more frameworks

• Qualities concerning one or more subjective standards

• To register any form of agreements • To form alliance networks of varied trust policies and security ‘strengths’ • To associate on any set of electronic specifications • To detail any combination of utilitarian qualities • To enable ad-hoc federation, syndication, normalization and interoperability • To enable cooperative, collaborative, coopetitive and competitive structures

among people, companies, applications and machines • To trade in an augmented world.

assemblies across multiple sources

Contracts

Circles of

Trust

Qualia

Certificates

who

why how

what

• Jurisdictional Extensibility – The jurisdiction(s) in which the Circle of Trust operates and the class of business (b2c, b2b, gov2b, gov2c, etc, and

whether involved with sensitive personal data) which dictate what applicable laws and customs may apply, and whether and to what extent the parties can contractually agree to operate under an agreed upon choice of law.

– The agreed upon economics to the Founders and Members, and associated tax consequences, may also drive the selection of the contractual framework.

– Regional variations in applicable law may dictate that one approach should be favored over another for use in the jurisdiction.

• Privacy and Security Requirements – Establishment of privacy floor (minimum regarding data integrity, verification, options regarding use) – Rules for sharing and restrictions/directives on use of personal information – Rules for trans-border exchanges – Technical, operational and administrative security and authentication standards – Incident notification and response procedures

• New Member Participation Eligibility Requirements – Recommendations – Profiling

• Business Rules – Rights and obligations of each role/type of participant – Scope of authority and responsibility – Transparency, Audit/Verification standards – Risk allocation matters and enforcement procedures – Liability for non-conformance – Operational performance standards / service levels – Day to day governance

Hardening Security

Documents

Transforming Healthcare Through Use of Clouds, …Transforming Healthcare Through Use of a Universal Exchange Language, Universal Semantics, and Universal Infrastructure for Big Data