ICEGOV2009 - Tutorial 1 - Technology in Electronic Government

Technology in Electronic Government

Jim Davies, Jeremy Gibbons, Steve Harris
Oxford University Computing Laboratory
ICEGOV tutorial, 10th Nov 2009

Technology in eGov: ICEGOV 2009 tutorial (JWD, JG, SJH) 2

1. Interoperability

“the ability of two or more systems or components to exchange
information and to use the information which has been
exchanged” (IEEE Standard Computer Dictionary)

the ability of “independent [. . . ] components [to] smoothly and
effectively work together in a predeﬁned and agreed upon
fashion” (Scholl & Klischewski, IJPA, 2007)


1.1. Aspects of interoperability

governance

quality

semantics

representation

authorisation

authentication

transport

(After Paul Davidson, UK LeGSB.)


1.2. Semantic interoperability

“Data semantics is the relationship between data and what the
data stand for. In order to obtain mutual understanding of
interchanged data, the actors have to share a model of what the
data represent. Semantic interoperability is about how to
achieve such mutual understanding.”
(Solvberg et al, ERCIM News, 2002)


“For [semantic interoperability] to be achieved, context
information must be an active component of information
systems. We deﬁne the context of a piece of data to be the
metadata relating to its meaning, properties (such as its source,
quality, and precision), and organization.”
(Sciore et al, ACM TODS, 1994)

“The resulting metadata must be managed, and will require
similar agreements about the semantics of the terminology
used in the documentation. The need for semantic agreements
simply moves up one link in the data/metadata chain.”
(Heiler, ACMCS, 1995)


1.3. Postmodernism

We can’t agree on a single
model; we shouldn’t try to.
There is no one privileged
view.
We need to allow for multiple
models, and ﬁgure out how
to make them interoperable.


1.4. Meta-analysis

Medical research today depends on meta-analysis: the integration of data
sets from different but related studies.
This is very difﬁcult to do after the fact; it only works well if we can:

• create and maintain a model for each clinical study

• ensure that the data collected is properly associated with the model

• determine study compatibility—and even integrate data—
automatically

The CancerGrid project in Oxford has been working on supporting
semantic interoperability in experimental medicine since 2005.
Much of this work is equally applicable to e-Government.


2. Semantic frameworks
To understand what a model means, we need to:

• understand the modelling language, as described by a language
grammar or metamodel, and

• understand the meaning of the atoms in the model, the references
made to items deﬁned elsewhere.

But what are these referenced items?


2.1. Administered items

• terminologies
collections of deﬁned terms, and relationships

• value domains
enumerations, ranges, values, codes, datatypes, units

• data elements
observation templates, attributes in model, questions on form

• models
study designs, components, forms, processes, standards


2.2. Value domains

A value domain will have a notion of representation, a deﬁnition as a data
type, and some semantics:

• free-form text, and links to external documents

• references
◮ to other value domains
◮ to data elements
◮ to models


Example


2.3. Terminologies

“The US Census Bureau
deﬁnition includes people
who originate in the original
peoples of Southeast Asia
and the Indian subcontinent.”
(Wikipedia)

(In popular US usage, ‘Asian’
often means Far East or SE
Asian. In Britain, ‘Asian’
particularly refers India,
Pakistan, Bangladesh,
Sri Lanka.)


Another terminology

(Wikipedia)


2.4. Data elements

• unique identiﬁer

• value domain

• [name]

• [description]

• [classiﬁcation]

• [status]


Example

name: ‘special meal’

identiﬁer: 1777777-14-72-131-424

description: special meal request for individual on particular ﬂight

value domain: BA Special Meals (by reference)


2.5. Models

• data elements and value domains in context of
◮ deﬁnition, classiﬁcation, administration
◮ usage and application

• can be written in any language that we can understand


Example

<<1777777-14-72-131-001>>
BA Special Meal <<enum>>

<<1777777-14-72-131-424>>
SpecialMealRequest

mealRequest

PassengerNumberRecord


Example

<<1777777-14-72-131-424>>
SpecialMealRequest

mealPreference

mealRequest

PassengerNumberRecord FrequentFlyerPreferences


2.6. Meta-analysis examples

• is a vegetarian passenger more likely to be ﬂying business class or
economy class?

• does Singapore Airlines carry a higher percentage of strict
vegetarians than British Airways?


A related value domain


A related model

<<197200078R-7-3-32-03>>
SG Special Meal <<enum>>

<<197200078R-7-3-32-34>>
MealOrder

mealOrder

Reservation


Elaboration

But which translation do we use?
For the purpose of our question, what would we consider vegetarian?
From BA: asian vegetarian (until 1 Jun 2009, when it became hindu
vegetarian), lacto ovo vegetarian, vegan vegetarian, and jain meal?
From SQ: raw vegetarian, vegetarian oriental, vegetarian indian,
vegetarian jain, vegan, fruit platter meal
Do we include Jain meals?


Translation

We can use a model to describe the proposed translation.
The meta-analysis is an experiment in itself, and represents a new
context, and new layer of semantics.
This model could be reusable—a metamodel for special meals
meta-analysis—or a ‘one-off’, relating speciﬁc datasets.


3. Metadata registries

“Repository products provide DBMS-like features for managing
specific types of metadata (such as designs, schemas, interfaces,
and mappings), as well as [. . . ] versioning, configuration
management, and change notification. Equally important, they
provide agreements on the semantics of the metadata.”

“At present, commercial repositories do not adhere to a
common interface or metadata model across vendors: so, in
addressing interoperability problems between clients and
servers, they introduce interoperability problems of their own.”

(Heiler, ACMCS, 1995)


3.1. ISO/IEC 11179

“ISO/IEC 11179 – Metadata registries (MDR), addresses the
semantics of data, the representation of data, and the
registration of the descriptions of that data. It is through these
descriptions that an accurate understanding of the semantics
and a useful depiction of the data are found.”
(ISO/IEC 11179-1:2004(E))


3.2. 11179 models

#'#+,*,

%++# #,#('

('),-%(#'

(',0, (* #'#+,* ,

,%',

,%',('),

$,%++

*()*,1

)*+',,#('%++

%-(#'


3.3. 11179 concepts

,% ',(' ), (' ),-%($'
0)* ++ 1 #.$' +) $!1$' * )* + ', 1

0)* ++$' * )* + ', 1 * )* + ',$' * )* + ',$'

,% ', %- ($'


3.4. Our implementation

Administered
Item

Value Data
Domain Element Model

We use models for the registration of concepts, conceptualisation,
classiﬁcation, context, . . .


3.5. Relationships

* * Administered
Model
Item

Value 1 * Data
Domain Element

All relationships are potentially subjective, relative, and can be captured
in models, including default models for the various aspects of the 11179
standard. Links can be administered items in their own right.


3.6. Models in practice

Model

Classiﬁcation
Scheme

Recommendation
Scheme

Usage
Model

Transformation
Model

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