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

mealRequest

BA Special Meal <<enum>>

SpecialMealRequest

PassengerNumberRecord

<<1777777-14-72-131-001>>

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


Example

mealRequest

SpecialMealRequest

PassengerNumberRecord

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

mealPreference

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

SG Special Meal <<enum>>

MealOrder

Reservation

<<197200078R-7-3-32-03>>

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

mealOrder


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

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3.3. 11179 concepts

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3.4. Our implementation

Value

DomainModel

Administered

Item

Data

Element

We use models for the registration of concepts, conceptualisation,

classification, context, . . .


3.5. Relationships

ModelAdministered

Item

* *

1 *Value

Domain

Data

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

ClassificationScheme

RecommendationScheme

TransformationModel

Usage Model


3.7. Model-driven development

“MDA is about using modelling languages as programming

languages rather than merely as design languages.”

(Frankel, Model-Driven Architecture, 2003)

We need the models to tell us what the data means, in terms of

contextualised data elements and value domains.

But the models are more than documentation: they can embody the

essence of the implementation too.


4. Meanings in the architecture

What is the semantics of ‘semantics’?

Ultimately, consensus is a social process.

Technological tools can support the process, but can’t supplant it.

The best we can manage technically is to make associations explicit,

and to leave it to people to interpret the associations.

icegov2009 - tutorial 1 - technology in electronic government

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