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Technical, social and organisational InteroperabilityFranz-Josef Behr
Keynote Presentation given at AGSE 2012:
Technical Interoperabilty
Personal Interoperabilty
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Institutional Interoperabilty
Political Interoperabilty
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Design and Evolution of Cloud-Based Systems
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Huge Gap in Software Architecture Design
Knowledge that can Result in Technical Debts
• Dozens of Architectural Related Challenges in
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• Systematically Building and Leveraging
Architectural Design Knowledge is Important for
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Flexible optimization algorithms to balance real-time constraints with cost and other goals.
Improved fault-tolerance fail-over to support real-time requirements.
Data provisioning and load balancing algorithms that rely on physical properties of computations.
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Some Observations on Common Patterns in Information Technology
1. PatternsinInformationTechnology
1
Some Observations on Common Patterns in
Information Technology
Prof. Dr. Franz-Josef Behr, Stuttgart University of Applied Sciences
ICTES 2014, University of Kelaniya, Sri Lanka
http://en.wikipedia.org/wiki/University_of_Kelaniya#mediaviewer/File:Unikelruki.jpg
2. PatternsinInformationTechnology
2
Outline
• Introduction
• Features and collections of them, and their Identity
• Encoding
– Markup Languages, Structuring vocabularies by namespaces
– Key value pairs
– Separating information chunks
• Standards
• Openess
• Networking and Service Orientation
3. PatternsinInformationTechnology
3
• Field data collection
• Surveying
• Photogrammetry
• Remote sensing
• Mobile Apps
• Geodatabeses
• Spatial Data Infrastructures
• Data Harmonization
• Service oriented Architectures
• Web Mapping
• OGC WebServices
• Architectures
• Reports & Presentations
• 3D, 4D, …
8. PatternsinInformationTechnology
8
Features
• real world: consisting of entities /
phenomena (latin: ens: a being)
• Feature = an abstraction of real
world phenomena (ISO 19101)‖
• geographic feature: associated
with a location relative to the
Earth.
• The state of a feature is defined
by a set of properties, where each
property may be thought of as a
{name, type, value} triple.
Real worldt
Model
Entity
feature
Identity
properties
behaviour
attributes
geometry
styling properties
meta properties
Technical viewpoint,
abstraction,
generalization
relationships
10. PatternsinInformationTechnology
10
Single features and collections: in GML
• FeatureCollection: a collection of GML Features, having own properties and an
optional list of Spatial Reference System Definitions. [1]
• GML feature collections are themselves valid GML features. [2]
<complexType name="AbstractFeatureCollectionType" abstract="true">
<complexContent>
<extension base="gml:AbstractFeatureType">
<sequence>
<element ref="gml:featureMember" minOccurs="0" maxOccurs="unbounded"/>
<element ref="gml:featureMembers" minOccurs="0"/>
</sequence>
</extension>
</complexContent>
</complexType>>
<element name="AbstractFeatureCollection"
type="gml:AbstractFeatureCollectionType" abstract="true"
substitutionGroup="gml:AbstractFeature" />
[1] http://www.w3.org/Mobile/posdep/GMLIntroduction.html
[2] OpenGIS Geography Markup Language (GML) Encoding Standard, v 3.2.1, http://portal.opengeospatial.org/files/?
artifact_id=20509
15. PatternsinInformationTechnology
15
Encoding Data
Principle of MarkUp
• Markup Language
• Definition of structural
elements for data
• Strict syntax
End-TagElement contentStart tag with attribute
Element name Attribute name Attribute value
<person city="Karlsruhe">Franz-Josef Behr</person>
eXtensible
23. PatternsinInformationTechnology
23
Open agenda is now implemented by
the UK Government and delivering
huge cost savings for government
£409 million in the first half
of this year
Read on: https://www.gov.uk/government/news/government-bodies-must-comply-with-open-standards-
principles
25. PatternsinInformationTechnology
25
Source
Data
Standards
Open
Openess
• Data must be complete.
• Data must be primary.
• Data must be timely.
• Data must be accessible.
• Data must be machine-processable.
• Access must be non-discriminatory.
• Data formats must be non-proprietary.
• Data must be under an open license or license free.
• Data must have permanence and be findable over time.
• Usage costs must be affordable (de minimis).
29. PatternsinInformationTechnology
29
Networking and Service Orientation
Source: http://commons.wikimedia.org/wiki/File:Social_Network_Analysis_Visualization.png
GIS = High Complexity
= Empowering a Few
Web Services:
Simplicity =
Empowering a Billion
31. PatternsinInformationTechnology
31
Web Gis Architecture
Internet
HTTP
Webserver
HTTP
CGI protocol
Document root
Static
resources
Server side programs
Dynamic Generation of resources
Client
"User Agent"
PNG, JPG, …
XML / SVG
DBMS
Server
Data preparation, analysis
SQL
Presentation Tier
Data Tier
Communication Tier,
Web Tier
Application Tier,
Business Logic Tier
Application
Server
IP address / Server Name / Host nameIP address
DNS Server
80
HTTP-Client
HTTP-Client
HTTP-Client
Local GIS
80
Communication TierCommunication Tier
Application TierApplication Tier
Data TierData Tier
Presentation TierPresentation Tier
32. PatternsinInformationTechnology
32
Internet
HTTP
Webserver
HTTP
CGI protocol
Document root
Static
resources
Server side programs
Dynamic Generation of resources
Client
"User Agent"
PNG, JPG, …
XML / SVG
DBMS
Server
Data preparation, analysis
SQL
Presentation Tier
Data Tier
Communication Tier,
Web Tier
Application Tier,
Business Logic Tier
Application
Server
IP address / Server Name / Host nameIP address
DNS Server
80
HTTP-Client
HTTP-Client
HTTP-Client
Local GIS
80
Architecture: The protocols
HTTP
RFC 2616
HTTP
RFC 2616
CGI
RFC 3875
CGI
RFC 3875
SQL
ISO/IEC 9075
SQL
ISO/IEC 9075
XML
w3.org/XML/
XML
w3.org/XML/
Content-Type
RFC 2045
Content-Type
RFC 2045
URI
RFC 1630
URI
RFC 1630
34. PatternsinInformationTechnology
34
Summary / Outlook
• Several observable patterns in ICT were shown.
• Try to figure out ALWAYS the design pattern in information technology
applicable for YOUR filed of work.
• Take care of standards.
• Be open regarding data, services, collaboration – be interoperable.