Welcome!

Artificial Intelligence Authors: Elizabeth White, Zakia Bouachraoui, Liz McMillan, Pat Romanski, Carmen Gonzalez

RSS Feed Item

RE: What makes a data component good for standardizing?

Awesome message Bruce! Thank you.

There is so much information packed into your short message. I'd like to focus today on one part. I'd like to verify that I have a correct understanding of the "unanticipated user."

What is the "unanticipated user?"

EXAMPLE

Suppose you deploy a web service to provide currency exchange rates - a user sends two currencies (e.g., USD and CAN) and your web service responds with their exchange rates (1 USD = 1.23 CAN, 1 CAN = 0.90 USD).

The "unanticipated user" means the users of your web service may be unknown to you. It does not mean that requests made by users are unknown or arbitrary. For example, your web service can't be expected to handle requests from a user wanting metric-to-English conversion rates. Rather, your service only handles requests for currency exchange rates, and the users issuing the requests may be unknown.

"Design your system to support the unanticipated user."

I think this is misleading. It should be:

"Design your system to support the anticipated but as yet unknown user."

Do I have a correct understanding of the meaning of the "unanticipated user?"

If so, I now understand why my example of the nuclear physicist unexpectedly sending me a document on subatomic physics and expecting me to process it, or pieces of it, is not a reasonable expectation. Right?

/Roger
________________________________________
From: Cox, Bruce [[email protected]]
Sent: Tuesday, January 13, 2009 8:40 PM
To: Len; Costello, Roger L.; xml-dev@l...
Subject: RE:  What makes a data component good for standardizing?

Roger, your example is weak.  If the name is not useful outside its
context, then neither is the address.  Is it a shipping address? Home
address? Birth place? Current residence? Future residence? For sale? For
rent? Target? Party house?  Yes, you can put it in your contact list,
but you can do that with the name as well, and with just about as much
value.

If I understand him, I agree with Len, what is or isn't worth
standardizing is negotiated with at least a surrogate for the
anticipated but as yet unknown user.  Who buys after-market motorcycle
parts?  You might not yet know the person, but you know the role they
play in the transaction you build into your system based on the
established market dynamics that you've studied carefully and documented
fully for your SBA loan approval.  That's the context you use to decide
what to standardize.  Without that, you don't even have a market, and
therefore no competitors or related services, so why bother
standardizing anything since there aren't any other business services to
interoperate with.

Some data clumps have a context larger than a specific market segment,
such as name and address and credit card info.  They represent the
social infrastructure that makes interoperability in a specific market
segment possible.  A particular user (role player) might be
unanticipated, but the role they play is not.

The value in standardization is in reducing friction in a transaction,
which in a market is measured by changes in profit.  In an academic
context, standardization might mean reducing effort in getting
published, or the cost of publication, all tending to improve one's
standing in the community.  In every case I can think of, the decision
to standardize or not would be a cost vs. benefit for effort involved.
If the data already has a standard representation, someone has answered
that question in the affirmative.  They've done so on the basis of some
abstract model of the transaction, probably derived from historical
records, regulations, custom, or other past experience.  Surely, some
such decisions are just wrong, due to poor analysis or based on poor,
tacit, or seat-of-the-pants models.

What to standardize for the sake of interoperability is based on the
model of the operation, which has to include all the roles for potential
users, even in the hypothetical nirvana of the semantic web.  That stuff
just pushes it to a higher level of abstraction, making it all the more
difficult to achieve since there will be less chance of agreement on the
model and (I'm going to really stick my neck out here) absolutely no
chance of basing the model on any actual human behavior.  How can we
expect people to behave in accord with an abstract model that is
completely foreign to them?  Does this mean that the semantic web
actually makes interoperability more difficult rather than less?  Hmmm.
But that's completely off topic.  Sorry.

Enough rambling.

Bruce B Cox
Manager, Standards Development Division
OCIO/SDMG
571-272-9004


-----Original Message-----
From: Len [mailto:[email protected]]
Sent: Saturday, January 10, 2009 11:49 AM
To: 'Costello, Roger L.'; [email protected]
Subject: RE:  What makes a data component good for
standardizing?

The user anticipates you.

It is a negotiation.

len

-----Original Message-----
From: Costello, Roger L. [mailto:[email protected]]
Sent: Saturday, January 10, 2009 8:34 AM
To: '[email protected]'
Subject:  What makes a data component good for standardizing?


Hi Folks,

Suppose you set out to create some standard data components. Your goal
is to
improve interoperability by creating standardized data components.
Particularly, you want these standardized data components to improve
interoperability between systems that weren't a priori coded to
understand
each other's data exchange format (i.e. you want to improve
interoperability
with the "unanticipated user").

What makes one data component good, and another bad?

(By "good" I mean the data component would in fact help improve
interoperability with the unanticipated user. By "bad" I mean the data
component would do little, if anything, to improving interoperability
with
the unanticipated user.)

I'll share my initial thoughts. I'd like your feedback on my initial
thoughts, and I'd also like to hear your thoughts.

Note: by "data component" I mean a chunk of markup that can be reused in
multiple XML vocabularies.


MY INITIAL THOUGHTS

I think that some data components would be good to standardize, while
others
would not be useful.

I'll start with two examples of data components would be good to
standardize.

Think about a postal address. It would be a good data component to
standardize. It's a useful data component even if I don't understand the
context in which it's being used.

      For example, suppose some nuclear physicist unexpectedly sends
      me a document containing data that I have no clue
      what it means, but embedded in it is a postal address.
      I may not be able to process all that data about
      subatomic particles (quarks, neutrinos, etc), but I can
      pluck out the postal address and store it in my address book.

That's interoperability between unanticipated users, albeit limited.

Another example of a useful data component is a business card (vcard).
Again, that's a useful data component that I can immediately utilize,
even
if I have no clue what the rest of the document is talking about.

These data components are useful independent of their context. I can use
the
data components even if I can't use all the stuff that they reside in.

Now I'll give an example of a data component which I think would not be
useful to standardize.

Both postal address and vcard gives a person's name (along with other
data).
Suppose I decide that I want data components with finer granularity than
postal address or vcard. Would "person name" make a good component for
standardizing?

I think not. A person's name would not be useful independent of context.


      For example, the same nuclear physicist above
      sends me the same document but containing the
      standardized PersonName data component, about
      a person named "Jim Brown.
      I am PersonName-aware so I am able to pluck out that
      Jim Brown information, even though I have
      no clue what the rest of the document says.
      Have I gained anything? No. It could be Jim Brown
      the ex-football player or some other person by that name.
      To make sense of the data component I need to
      understand its context.

I propose these two metrics for evaluating the usefulness of data
component:

    1. The data component must be standardized
       and broadly adopted (see below).
    2. If I can meaningfully use the data component
       without understanding any of the context in
       which it resides then it is a good data
       component. If I must understand its context
       then it is a bad data component.

Standardizing is good. It enables two parties to understand each other,
i.e., interoperate.

But standardization is not enough. I want more than interoperability
between
two parties that have a priori agreed to a data interchange format. I
want
interoperability between two parties that haven't a priori agreed to a
data
interchange format. I want interoperability between unanticipated
parties.

So the key is to not only standardize, but standardize the right things.


SUMMARY

We would go a long way toward advancing interoperability of
unanticipated
systems if we focused on creating standardized components that are
useful
independent of context.

What do you think?

/Roger
_______________________________________________________________________

XML-DEV is a publicly archived, unmoderated list hosted by OASIS
to support XML implementation and development. To minimize
spam in the archives, you must subscribe before posting.

[Un]Subscribe/change address: http://www.oasis-open.org/mlmanage/
Or unsubscribe: [email protected]
subscribe: [email protected]
List archive: http://lists.xml.org/archives/xml-dev/
List Guidelines: http://www.oasis-open.org/maillists/guidelines.php

Read the original blog entry...

IoT & Smart Cities Stories
The platform combines the strengths of Singtel's extensive, intelligent network capabilities with Microsoft's cloud expertise to create a unique solution that sets new standards for IoT applications," said Mr Diomedes Kastanis, Head of IoT at Singtel. "Our solution provides speed, transparency and flexibility, paving the way for a more pervasive use of IoT to accelerate enterprises' digitalisation efforts. AI-powered intelligent connectivity over Microsoft Azure will be the fastest connected pat...
There are many examples of disruption in consumer space – Uber disrupting the cab industry, Airbnb disrupting the hospitality industry and so on; but have you wondered who is disrupting support and operations? AISERA helps make businesses and customers successful by offering consumer-like user experience for support and operations. We have built the world’s first AI-driven IT / HR / Cloud / Customer Support and Operations solution.
Codete accelerates their clients growth through technological expertise and experience. Codite team works with organizations to meet the challenges that digitalization presents. Their clients include digital start-ups as well as established enterprises in the IT industry. To stay competitive in a highly innovative IT industry, strong R&D departments and bold spin-off initiatives is a must. Codete Data Science and Software Architects teams help corporate clients to stay up to date with the mod...
At CloudEXPO Silicon Valley, June 24-26, 2019, Digital Transformation (DX) is a major focus with expanded DevOpsSUMMIT and FinTechEXPO programs within the DXWorldEXPO agenda. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throug...
Druva is the global leader in Cloud Data Protection and Management, delivering the industry's first data management-as-a-service solution that aggregates data from endpoints, servers and cloud applications and leverages the public cloud to offer a single pane of glass to enable data protection, governance and intelligence-dramatically increasing the availability and visibility of business critical information, while reducing the risk, cost and complexity of managing and protecting it. Druva's...
BMC has unmatched experience in IT management, supporting 92 of the Forbes Global 100, and earning recognition as an ITSM Gartner Magic Quadrant Leader for five years running. Our solutions offer speed, agility, and efficiency to tackle business challenges in the areas of service management, automation, operations, and the mainframe.
The Jevons Paradox suggests that when technological advances increase efficiency of a resource, it results in an overall increase in consumption. Writing on the increased use of coal as a result of technological improvements, 19th-century economist William Stanley Jevons found that these improvements led to the development of new ways to utilize coal. In his session at 19th Cloud Expo, Mark Thiele, Chief Strategy Officer for Apcera, compared the Jevons Paradox to modern-day enterprise IT, examin...
With 10 simultaneous tracks, keynotes, general sessions and targeted breakout classes, @CloudEXPO and DXWorldEXPO are two of the most important technology events of the year. Since its launch over eight years ago, @CloudEXPO and DXWorldEXPO have presented a rock star faculty as well as showcased hundreds of sponsors and exhibitors! In this blog post, we provide 7 tips on how, as part of our world-class faculty, you can deliver one of the most popular sessions at our events. But before reading...
DSR is a supplier of project management, consultancy services and IT solutions that increase effectiveness of a company's operations in the production sector. The company combines in-depth knowledge of international companies with expert knowledge utilising IT tools that support manufacturing and distribution processes. DSR ensures optimization and integration of internal processes which is necessary for companies to grow rapidly. The rapid growth is possible thanks, to specialized services an...
At CloudEXPO Silicon Valley, June 24-26, 2019, Digital Transformation (DX) is a major focus with expanded DevOpsSUMMIT and FinTechEXPO programs within the DXWorldEXPO agenda. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throug...