CIVIL AIRCRAFT TECHNOLOGY

ENABLED SERVICES

Data Interoperability Standards

Charlie Dibsdale – Chief of Research and IP management in O-SyS

OPINIONS EXPRESSED IN

THIS PACK ARE ENTIRELY

THE AUTHOR‟S

A personal perspective:

Having done research for this presentation, I become more aware

that the more I learn, the more I realise I do not know!

Data interoperability – who / what?

Complex, multifarious, confusing, difficult to understand

The Technology stacks – where do

standards really work?

• In order to exchange data

all of these stacks are

required

• OSI model is an influential

„reference‟

• TCP-IP – is practical

• Standardisation seems to

work better bottom-up (a

personal observation)

• Trying to standardise „data

schemas‟ is very difficult…

Common Data Model Approach

S-Series_CDM_001-01

Contains all the classes that are in at least one of the S-Series specifications

Engineeri

ng Data_

model_00

2-01

Common UoFs and classes

Specific UoFs and classes for Engineering

Manaufac

turing

Data_

model_00

2-03

Specific UoFs and classes for Manufacturing

Case Study – Mimosa / Prognostics The true requirement – data

attributes in this model Allowable data representations of prognostics in Mimosa (V3)

• Allows three data

fields:

• Coarse, choose one

from the set of {0, 25,

50, 75, 100) %

• Fine setting, integers

in the range {0 ..

100}%

• Certainty -

• % of what??

S Series

Alternatives – Master Data Management

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ERP EAM PLM

Financial perspective

External Enterprise

Data exchange

Perspective

Design – Manufacturing

perspective

MDM For major ‘Entities’ in an organisation (such as a product)

Many enterprise systems model the entities differently

(They have different perspectives on the data)

MDM maps all entities from their enterprise systems

and has a master data schema

The master data schema

References reconciles

differences between the

individual enterprise systems

MDM becomes the

Enterprise single source

of truth for that entity

MDM processes include

Data quality, data management governance, repeatable processes etc.

Configuration

perspective

ISO 8000 100 series

Neutral Schemas (EAV-CR)

• This allows any „entity‟ data

to be defined and easily

modified

• Widely used pattern in

medical and pharma.

• Allows flexibility to add

rich metadata

• There are performance

concerns with this structure

• There is never a free lunch!

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De-couples us

from structure

ReST (web service interfaces) • Is an architectural style – not a

standard. But ReST uses standards.

• Based on HTTP (or HTTPS) • Commands: Get-Post-head-delete-put-

options-trace

• Elegant by its simplicity

• Full use of URI and web addressing of resources

• Usable with XML/HTML/TXT/GIF or JSON (not exhaustive list) the resource “representation” at the URI. • Resource type is part of the information in

the HTTP header

• Data exchange using E-A-V (where “E” and “A” are URI resources) is widely used

• See http://www.ics.uci.edu/~taylor/documents/2002-REST-TOIT.pdf

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What I did was suggest that we collectively could respond to the ECSAB for a paper to

cover:

1). The potential implications of linking EHM with the safety case.

2). The benefits of EHM on maintenance cost.

Designed for performance, scalability & simplicity

Semantic Technologies (RDF - OWL) W3C Provenance Ontology

(Provenance – ISO 8000 part 120) • These could relate data

using predicate logic

• RDF is made up of data

“Triplets” – an object – a

predicate – a value (EAV

pattern)

• Simple example: “Gas

Turbine” – “has a module” –

“Compressor”

• Elements in the triplets are

able to be stored as

resources at URI‟s…

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The Internet of things • Current IPv4 URI web addresses are 32 bit.

IPv6 URI‟s are 128 bit. (unlimited addresses)

• Every individual equipment component could

have a unique URI and can be located

• Any base URI can have an associated web

server

• Web servers can expose (ReST) web service

interfaces • Data can be “published”

• We can „subscribe‟ to the services (If

authorised to do so… )

• Machine to machine interaction

• Agent based architectures – machine

autonomy

• We can “layer” this architecture with semantic

web technology • We can „ask questions‟ of the data

• This architecture is very flexible de-coupling us

from standardising data structures

Rolls-Royce proprietary information

Source: SRI Consulting Business Intelligence

Reflection, observations and discussion points

• Standards are easier the lower

they are in the OSI stack?

• Are the static structures way of

addressing data interoperability

the best way?

• MDM seems to be a more

pragmatic approach

• EAV-CR is being exploited in

science, pharma and medical –

can we learn?

• Mimosa object model also has

aspects of EAV

• Can we use ReST and really

exploit the internet architecture?

• How can we develop and realise the

game changing promise of the

“semantic web” – RDF and OWL

• How can we position ourselves and

move toward exploiting the „internet of

things‟

• How can „cloud‟ be used to reduce

cost.

• Do we have the right competencies /

knowledge to map a practical way

forward? (mix of technical-business)

• Should we be standardising the

“questions we want to ask of data” as

„interface standards‟ instead of

standardising the structures of data?

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WHAT are our next steps?

Back-up

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Back up – mimosa object model

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