SlideShare uma empresa Scribd logo
1 de 65
Proof of Concept: SOA Application Composition using the Genetic Algorithm Jim Fuller http://www.ruminate.co.uk http://www.slgchorus.com
Introduction ,[object Object],[object Object],[object Object],[object Object]
Lecture Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How we use WS in today's applications ,[object Object],[object Object],[object Object],[object Object]
MVC type architectures are popular  Client Tier Presentation Tier Business Tier Integration Tier Resource Tier Data Repository, XML Binding, Persistence  Model View Controller External web  services Internal web  services
WS MVC with the Browser Controller EventHandler SOAPEventHandler Model The Model receives events from the Controller and updates itself sending Data which gets transformed by  our view components.  View -IE web service client side processing -XSLT templates -CSS -Global.xml -Global.xsl HTTP GET HTTP POST REQUEST Internal web services External web services HTTP  RESPONSE Internet Explorer  Client
SOA Anchor ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Bazaar not opened yet ,[object Object],[object Object],[object Object],[object Object],[object Object]
The  world of ‘millions of web services’ ,[object Object],[object Object],[object Object]
Automatic application composition methods ,[object Object],Random search of the problem  domain AI  / intelligent  Software agent  methods
Genetic Algorithm Refresher ,[object Object],[object Object],[object Object],[object Object]
Abridged Genetic Algorithm  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
GA operations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Genetic Programming Process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Symbolic expressions and XSLT ,[object Object],[object Object],[object Object],[object Object]
[object Object],Simplest Lisp Example 3 4 + * 2 Hierarchical computer programs are more expressive then manipulating linear strings
XSLT are also general hierarchical computer programs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],<d/> <c/> <xsl:template/> <xsl:stylesheet/> There are some differences, e.g.  there are a variety of node types within XML
Problem definition ,[object Object],[object Object],[object Object],[object Object]
Source XML ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Target XML ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Early Genetic Experiment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
M=500, G=51 Parameters Same as raw fitness, approaching 0 is better fitness Standardized fitness One fitness case Fitness Cases Node count on  xmldiff  patch file difference between result xml and target xml Raw fitness Subset of xslt instructions Function Set <a/> <b/> <c/> <d/> Terminal Set Generate an xslt program that transforms source xml into result xml which is equivalent to target xml Objective
Step 0. Generate Initial Population ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Avoid  ‘early taxonomisation’   ,[object Object],[object Object],[object Object],[object Object],[object Object]
Ant: generate_initial_population  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Step 1: Evaluate Fitness XSLT generation xslt Source.xml result.xml Target.xml evaluate fitness transformation xml diff Each individual is ranked, by testing xslt program against a source xml
Step 1. evaluate fitness (cont) ,[object Object],[object Object],[object Object],[object Object]
RESULT XML from XSLT individual transformation with SOURCE XML TREEDIFFMERGE DIFFERENCE PATCH <?xml version=&quot;1.0&quot; encoding=&quot;utf-8&quot;?><root><a> <b> <c> <d/> </c> </b> </a></root> <?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?> <diff /> <?xml version=&quot;1.0&quot; encoding=&quot;utf-8&quot;?><root> <a/><a><a><c/><c><a><d/></a><c/></c></a><b><b/><a/><c/><b> <c> <d/> </c> </b></b><a/></a><d><a><c/><a/><a/></a><c/></d><c/> </root> <?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?> <diff xmlns:diff='http://diff.org'> <diff:copy src=&quot;2&quot; dst=&quot;1&quot;>   <diff:copy src=&quot;16&quot; dst=&quot;2&quot; />   </diff:copy> </diff> <?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?> <root/> <?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?> <diff xmlns:diff='http://diff.org'> <diff:insert dst=&quot;1&quot;>   <a> <b> <c>   <d />   </c>   </b>   </a>   </diff:insert> </diff>
XML Diff issues ,[object Object],[object Object],[object Object]
Ant: transform_src ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ant: fitness_src ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Step 2. Select individuals ,[object Object],Selected XSLT population Select individuals for genetic operations, based on their fitness
A word on fitness ,[object Object],[object Object],[object Object],[object Object],[object Object]
Step 3. Primary Genetic Operations Selected XSLT population New generation Reproduction Individual reproduced into new generation
Step 3. Primary Genetic Operations Selected XSLT population New generation Creates  2 offspring ‘ Mom’ ‘ Dad’ Crossover ( Recombination ) Select parents then crossover creates 2 offspring
Step 3. Primary Genetic Operations Crossover ( Recombination ) ‘ Dad XSLT’ ‘ Mom XSLT’ ‘ offspring xslt’ ‘ offspring xslt’ New generation Swap nodes between selected parent xslt
Step 3. Secondary Genetic Operations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Step 3. Secondary Genetic Operations mutation ‘ selected XSLT’ Pick a node and randomly mutate Completely new set of instructions ‘ offspring xslt’
Step 3. Secondary Genetic Operations permutation ‘ selected XSLT’ ‘ offspring xslt’ Permutated node order
Step 3. Secondary Genetic Operations editing ‘ selected XSLT’ ‘ offspring xslt’ Replace node with evaluated expression
Step 3. Secondary Genetic Operations encapsulation ‘ selected XSLT’ ‘ define new function’ Identify useful subtrees and encapsulate by defining new function ‘ XSLT’
Step 3. Secondary Genetic Operations decimation Identify very poor fitness individuals and remove from population <xsl:stylesheet xmlns:xsl=&quot;http://www.w3.org/1999/XSL/Transform&quot; version=&quot;1.0&quot;> </xsl:stylesheet> <xsl:stylesheet/>
Ant: select, perform, and generate new population ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Step 4. Generate X populations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ant properties ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Simplified Ant Build Target ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results ,[object Object],[object Object],[object Object]
Ruminations  ,[object Object],[object Object],[object Object],[object Object]
GA Strategies to Consider ,[object Object],[object Object]
Enhance existing Prototype ,[object Object],[object Object],[object Object],[object Object]
Simple application composition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Web Services Search Engine ,[object Object],[object Object],[object Object],[object Object],[object Object]
Manual Harvesting of Web Services ,[object Object],[object Object],[object Object],[object Object],[object Object]
Simple WSIL example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
WSIL with 2 services ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
inspection.wsil at XMETHODS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
XSLT Generic SOAP client ,[object Object],[object Object],[object Object],[object Object]
Example of using a web service in XSLT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
M=1000, G=51 Parameters three fitness cases Fitness Cases Node count on  xmldiff  patch file difference between result xml and target xml Raw fitness Subset of xslt instructions + ws:invoke Function Set <a/>, <b/> ( 2 numbers ) Terminal Set Generate an xslt program that multiplies 2 numbers, converts to Celsius and returns number in Chinese Objective
Results ,[object Object],[object Object],[object Object],[object Object],[object Object]
Enhancement ,[object Object],[object Object],[object Object],[object Object]
The Internet as a maturing Software Framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Mais conteúdo relacionado

Mais procurados

Effective Java, Third Edition - Keepin' it Effective
Effective Java, Third Edition - Keepin' it EffectiveEffective Java, Third Edition - Keepin' it Effective
Effective Java, Third Edition - Keepin' it EffectiveC4Media
 
Introduction to JPA and Hibernate including examples
Introduction to JPA and Hibernate including examplesIntroduction to JPA and Hibernate including examples
Introduction to JPA and Hibernate including examplesecosio GmbH
 
Overview of entity framework by software outsourcing company india
Overview of entity framework by software outsourcing company indiaOverview of entity framework by software outsourcing company india
Overview of entity framework by software outsourcing company indiaJignesh Aakoliya
 
ORM Concepts and JPA 2.0 Specifications
ORM Concepts and JPA 2.0 SpecificationsORM Concepts and JPA 2.0 Specifications
ORM Concepts and JPA 2.0 SpecificationsAhmed Ramzy
 

Mais procurados (9)

Apache Persistence Layers
Apache Persistence LayersApache Persistence Layers
Apache Persistence Layers
 
JPA and Hibernate
JPA and HibernateJPA and Hibernate
JPA and Hibernate
 
Effective Java, Third Edition - Keepin' it Effective
Effective Java, Third Edition - Keepin' it EffectiveEffective Java, Third Edition - Keepin' it Effective
Effective Java, Third Edition - Keepin' it Effective
 
Java
Java Java
Java
 
C#/.NET Little Pitfalls
C#/.NET Little PitfallsC#/.NET Little Pitfalls
C#/.NET Little Pitfalls
 
Introduction to JPA and Hibernate including examples
Introduction to JPA and Hibernate including examplesIntroduction to JPA and Hibernate including examples
Introduction to JPA and Hibernate including examples
 
Overview of entity framework by software outsourcing company india
Overview of entity framework by software outsourcing company indiaOverview of entity framework by software outsourcing company india
Overview of entity framework by software outsourcing company india
 
Basic Hibernate Final
Basic Hibernate FinalBasic Hibernate Final
Basic Hibernate Final
 
ORM Concepts and JPA 2.0 Specifications
ORM Concepts and JPA 2.0 SpecificationsORM Concepts and JPA 2.0 Specifications
ORM Concepts and JPA 2.0 Specifications
 

Destaque

STUDY ON PROJECT MANAGEMENT THROUGH GENETIC ALGORITHM
STUDY ON PROJECT MANAGEMENT THROUGH GENETIC ALGORITHMSTUDY ON PROJECT MANAGEMENT THROUGH GENETIC ALGORITHM
STUDY ON PROJECT MANAGEMENT THROUGH GENETIC ALGORITHMAvay Minni
 
Using Genetic algorithm for Network Intrusion Detection
Using Genetic algorithm for Network Intrusion DetectionUsing Genetic algorithm for Network Intrusion Detection
Using Genetic algorithm for Network Intrusion DetectionSagar Uday Kumar
 
Class GA. Genetic Algorithm,Genetic Algorithm
Class GA. Genetic Algorithm,Genetic AlgorithmClass GA. Genetic Algorithm,Genetic Algorithm
Class GA. Genetic Algorithm,Genetic Algorithmraed albadri
 
Introduction to the Genetic Algorithm
Introduction to the Genetic AlgorithmIntroduction to the Genetic Algorithm
Introduction to the Genetic AlgorithmQiang Hao
 
Genetic Algorithm for task scheduling in Cloud Computing Environment
Genetic Algorithm for task scheduling in Cloud Computing EnvironmentGenetic Algorithm for task scheduling in Cloud Computing Environment
Genetic Algorithm for task scheduling in Cloud Computing EnvironmentSwapnil Shahade
 
A hybrid genetic algorithm and chaotic function model for image encryption
A hybrid genetic algorithm and chaotic function model for image encryptionA hybrid genetic algorithm and chaotic function model for image encryption
A hybrid genetic algorithm and chaotic function model for image encryptionsadique_ghitm
 
FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...
FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...
FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...u772020
 
genetic algorithm based music recommender system
genetic algorithm based music recommender systemgenetic algorithm based music recommender system
genetic algorithm based music recommender systemneha pevekar
 

Destaque (12)

STUDY ON PROJECT MANAGEMENT THROUGH GENETIC ALGORITHM
STUDY ON PROJECT MANAGEMENT THROUGH GENETIC ALGORITHMSTUDY ON PROJECT MANAGEMENT THROUGH GENETIC ALGORITHM
STUDY ON PROJECT MANAGEMENT THROUGH GENETIC ALGORITHM
 
Genetic Algorithm
Genetic AlgorithmGenetic Algorithm
Genetic Algorithm
 
Using Genetic algorithm for Network Intrusion Detection
Using Genetic algorithm for Network Intrusion DetectionUsing Genetic algorithm for Network Intrusion Detection
Using Genetic algorithm for Network Intrusion Detection
 
Modified Genetic Algorithm for Solving n-Queens Problem
Modified Genetic Algorithm for Solving n-Queens ProblemModified Genetic Algorithm for Solving n-Queens Problem
Modified Genetic Algorithm for Solving n-Queens Problem
 
Class GA. Genetic Algorithm,Genetic Algorithm
Class GA. Genetic Algorithm,Genetic AlgorithmClass GA. Genetic Algorithm,Genetic Algorithm
Class GA. Genetic Algorithm,Genetic Algorithm
 
Introduction to the Genetic Algorithm
Introduction to the Genetic AlgorithmIntroduction to the Genetic Algorithm
Introduction to the Genetic Algorithm
 
Genetic algorithm
Genetic algorithmGenetic algorithm
Genetic algorithm
 
Genetic Algorithm for task scheduling in Cloud Computing Environment
Genetic Algorithm for task scheduling in Cloud Computing EnvironmentGenetic Algorithm for task scheduling in Cloud Computing Environment
Genetic Algorithm for task scheduling in Cloud Computing Environment
 
A hybrid genetic algorithm and chaotic function model for image encryption
A hybrid genetic algorithm and chaotic function model for image encryptionA hybrid genetic algorithm and chaotic function model for image encryption
A hybrid genetic algorithm and chaotic function model for image encryption
 
Genetic Algorithm
Genetic AlgorithmGenetic Algorithm
Genetic Algorithm
 
FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...
FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...
FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...
 
genetic algorithm based music recommender system
genetic algorithm based music recommender systemgenetic algorithm based music recommender system
genetic algorithm based music recommender system
 

Semelhante a Genetic algorithm SOA application composition using XSLT

Declarative Multilingual Information Extraction with SystemT
Declarative Multilingual Information Extraction with SystemTDeclarative Multilingual Information Extraction with SystemT
Declarative Multilingual Information Extraction with SystemTLaura Chiticariu
 
Silverlight Developer Introduction
Silverlight   Developer IntroductionSilverlight   Developer Introduction
Silverlight Developer IntroductionTomy Ismail
 
PowerPoint
PowerPointPowerPoint
PowerPointVideoguy
 
JavaScript Miller Columns
JavaScript Miller ColumnsJavaScript Miller Columns
JavaScript Miller ColumnsJonathan Fine
 
The Theory Of The Dom
The Theory Of The DomThe Theory Of The Dom
The Theory Of The Domkaven yan
 
Practical catalyst
Practical catalystPractical catalyst
Practical catalystdwm042
 
Introductionto Xm Lmessaging
Introductionto Xm LmessagingIntroductionto Xm Lmessaging
Introductionto Xm LmessagingLiquidHub
 
Inroduction to XSLT with PHP4
Inroduction to XSLT with PHP4Inroduction to XSLT with PHP4
Inroduction to XSLT with PHP4Stephan Schmidt
 
Domain oriented development
Domain oriented developmentDomain oriented development
Domain oriented developmentrajmundr
 
Choose'10: Ralf Laemmel - Dealing Confortably with the Confusion of Tongues
Choose'10: Ralf Laemmel - Dealing Confortably with the Confusion of TonguesChoose'10: Ralf Laemmel - Dealing Confortably with the Confusion of Tongues
Choose'10: Ralf Laemmel - Dealing Confortably with the Confusion of TonguesCHOOSE
 
Unit 1 - TypeScript & Introduction to Angular CLI.pptx
Unit 1 - TypeScript & Introduction to Angular CLI.pptxUnit 1 - TypeScript & Introduction to Angular CLI.pptx
Unit 1 - TypeScript & Introduction to Angular CLI.pptxMalla Reddy University
 
Entity Framework V1 and V2
Entity Framework V1 and V2Entity Framework V1 and V2
Entity Framework V1 and V2ukdpe
 
SURE Research Report
SURE Research ReportSURE Research Report
SURE Research ReportAlex Sumner
 
Struts 2 Overview
Struts 2 OverviewStruts 2 Overview
Struts 2 Overviewskill-guru
 
Googleappengineintro 110410190620-phpapp01
Googleappengineintro 110410190620-phpapp01Googleappengineintro 110410190620-phpapp01
Googleappengineintro 110410190620-phpapp01Tony Frame
 
GWT is Smarter Than You
GWT is Smarter Than YouGWT is Smarter Than You
GWT is Smarter Than YouRobert Cooper
 
Building nTier Applications with Entity Framework Services (Part 1)
Building nTier Applications with Entity Framework Services (Part 1)Building nTier Applications with Entity Framework Services (Part 1)
Building nTier Applications with Entity Framework Services (Part 1)David McCarter
 
Advanced Web Development
Advanced Web DevelopmentAdvanced Web Development
Advanced Web DevelopmentRobert J. Stein
 

Semelhante a Genetic algorithm SOA application composition using XSLT (20)

Declarative Multilingual Information Extraction with SystemT
Declarative Multilingual Information Extraction with SystemTDeclarative Multilingual Information Extraction with SystemT
Declarative Multilingual Information Extraction with SystemT
 
Silverlight Developer Introduction
Silverlight   Developer IntroductionSilverlight   Developer Introduction
Silverlight Developer Introduction
 
PowerPoint
PowerPointPowerPoint
PowerPoint
 
JavaScript Miller Columns
JavaScript Miller ColumnsJavaScript Miller Columns
JavaScript Miller Columns
 
The Theory Of The Dom
The Theory Of The DomThe Theory Of The Dom
The Theory Of The Dom
 
Sweo talk
Sweo talkSweo talk
Sweo talk
 
Practical catalyst
Practical catalystPractical catalyst
Practical catalyst
 
Introductionto Xm Lmessaging
Introductionto Xm LmessagingIntroductionto Xm Lmessaging
Introductionto Xm Lmessaging
 
Java Basics
Java BasicsJava Basics
Java Basics
 
Inroduction to XSLT with PHP4
Inroduction to XSLT with PHP4Inroduction to XSLT with PHP4
Inroduction to XSLT with PHP4
 
Domain oriented development
Domain oriented developmentDomain oriented development
Domain oriented development
 
Choose'10: Ralf Laemmel - Dealing Confortably with the Confusion of Tongues
Choose'10: Ralf Laemmel - Dealing Confortably with the Confusion of TonguesChoose'10: Ralf Laemmel - Dealing Confortably with the Confusion of Tongues
Choose'10: Ralf Laemmel - Dealing Confortably with the Confusion of Tongues
 
Unit 1 - TypeScript & Introduction to Angular CLI.pptx
Unit 1 - TypeScript & Introduction to Angular CLI.pptxUnit 1 - TypeScript & Introduction to Angular CLI.pptx
Unit 1 - TypeScript & Introduction to Angular CLI.pptx
 
Entity Framework V1 and V2
Entity Framework V1 and V2Entity Framework V1 and V2
Entity Framework V1 and V2
 
SURE Research Report
SURE Research ReportSURE Research Report
SURE Research Report
 
Struts 2 Overview
Struts 2 OverviewStruts 2 Overview
Struts 2 Overview
 
Googleappengineintro 110410190620-phpapp01
Googleappengineintro 110410190620-phpapp01Googleappengineintro 110410190620-phpapp01
Googleappengineintro 110410190620-phpapp01
 
GWT is Smarter Than You
GWT is Smarter Than YouGWT is Smarter Than You
GWT is Smarter Than You
 
Building nTier Applications with Entity Framework Services (Part 1)
Building nTier Applications with Entity Framework Services (Part 1)Building nTier Applications with Entity Framework Services (Part 1)
Building nTier Applications with Entity Framework Services (Part 1)
 
Advanced Web Development
Advanced Web DevelopmentAdvanced Web Development
Advanced Web Development
 

Último

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUK Journal
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024The Digital Insurer
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CVKhem
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 

Último (20)

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 

Genetic algorithm SOA application composition using XSLT

  • 1. Proof of Concept: SOA Application Composition using the Genetic Algorithm Jim Fuller http://www.ruminate.co.uk http://www.slgchorus.com
  • 2.
  • 3.
  • 4.
  • 5. MVC type architectures are popular Client Tier Presentation Tier Business Tier Integration Tier Resource Tier Data Repository, XML Binding, Persistence Model View Controller External web services Internal web services
  • 6. WS MVC with the Browser Controller EventHandler SOAPEventHandler Model The Model receives events from the Controller and updates itself sending Data which gets transformed by our view components. View -IE web service client side processing -XSLT templates -CSS -Global.xml -Global.xsl HTTP GET HTTP POST REQUEST Internal web services External web services HTTP RESPONSE Internet Explorer Client
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22. M=500, G=51 Parameters Same as raw fitness, approaching 0 is better fitness Standardized fitness One fitness case Fitness Cases Node count on xmldiff patch file difference between result xml and target xml Raw fitness Subset of xslt instructions Function Set <a/> <b/> <c/> <d/> Terminal Set Generate an xslt program that transforms source xml into result xml which is equivalent to target xml Objective
  • 23.
  • 24.
  • 25.
  • 26. Step 1: Evaluate Fitness XSLT generation xslt Source.xml result.xml Target.xml evaluate fitness transformation xml diff Each individual is ranked, by testing xslt program against a source xml
  • 27.
  • 28. RESULT XML from XSLT individual transformation with SOURCE XML TREEDIFFMERGE DIFFERENCE PATCH <?xml version=&quot;1.0&quot; encoding=&quot;utf-8&quot;?><root><a> <b> <c> <d/> </c> </b> </a></root> <?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?> <diff /> <?xml version=&quot;1.0&quot; encoding=&quot;utf-8&quot;?><root> <a/><a><a><c/><c><a><d/></a><c/></c></a><b><b/><a/><c/><b> <c> <d/> </c> </b></b><a/></a><d><a><c/><a/><a/></a><c/></d><c/> </root> <?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?> <diff xmlns:diff='http://diff.org'> <diff:copy src=&quot;2&quot; dst=&quot;1&quot;> <diff:copy src=&quot;16&quot; dst=&quot;2&quot; /> </diff:copy> </diff> <?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?> <root/> <?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot;?> <diff xmlns:diff='http://diff.org'> <diff:insert dst=&quot;1&quot;> <a> <b> <c> <d /> </c> </b> </a> </diff:insert> </diff>
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34. Step 3. Primary Genetic Operations Selected XSLT population New generation Reproduction Individual reproduced into new generation
  • 35. Step 3. Primary Genetic Operations Selected XSLT population New generation Creates 2 offspring ‘ Mom’ ‘ Dad’ Crossover ( Recombination ) Select parents then crossover creates 2 offspring
  • 36. Step 3. Primary Genetic Operations Crossover ( Recombination ) ‘ Dad XSLT’ ‘ Mom XSLT’ ‘ offspring xslt’ ‘ offspring xslt’ New generation Swap nodes between selected parent xslt
  • 37.
  • 38. Step 3. Secondary Genetic Operations mutation ‘ selected XSLT’ Pick a node and randomly mutate Completely new set of instructions ‘ offspring xslt’
  • 39. Step 3. Secondary Genetic Operations permutation ‘ selected XSLT’ ‘ offspring xslt’ Permutated node order
  • 40. Step 3. Secondary Genetic Operations editing ‘ selected XSLT’ ‘ offspring xslt’ Replace node with evaluated expression
  • 41. Step 3. Secondary Genetic Operations encapsulation ‘ selected XSLT’ ‘ define new function’ Identify useful subtrees and encapsulate by defining new function ‘ XSLT’
  • 42. Step 3. Secondary Genetic Operations decimation Identify very poor fitness individuals and remove from population <xsl:stylesheet xmlns:xsl=&quot;http://www.w3.org/1999/XSL/Transform&quot; version=&quot;1.0&quot;> </xsl:stylesheet> <xsl:stylesheet/>
  • 43.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
  • 51.
  • 52.
  • 53.
  • 54.
  • 55.
  • 56.
  • 57.
  • 58.
  • 59.
  • 60. M=1000, G=51 Parameters three fitness cases Fitness Cases Node count on xmldiff patch file difference between result xml and target xml Raw fitness Subset of xslt instructions + ws:invoke Function Set <a/>, <b/> ( 2 numbers ) Terminal Set Generate an xslt program that multiplies 2 numbers, converts to Celsius and returns number in Chinese Objective
  • 61.
  • 62.
  • 63.
  • 64.
  • 65.

Notas do Editor

  1. My day job is that of internet services manager of a medium sized UK company which builds proprietary solutions involving web service and xml technologies; My primary interests initially revolved around XSLT and xml technologies; started up EXSLT along with Dave Pawson and Jeni Tennison which now enjoys widespread adoption/implementation, have done quite a bit of technical reviewing and co-authoring for the now defunct WROX, and as with many authors I have now moved over to their benefactors, e.g. now writing a tome for APRESS provisionally titled Ant Gems which is due to go to print march next year I am a typical application developer, having refreshed my skill set as the times have changed.
  2. This talk will outline a journey which actually started with a desire to answer XSLT list member questions to a personal rediscovery of the genetic algorithm. Over the years of answering peoples questions on the XSLT list, I noticed that most of the questions were simple mapping transformations…e.g. I have this source xml and what to transform it into target xml. If the user knew the source and target xml, what automated methods could be brought to bear ? I wont be talking about REST, not because I don’t think it important or that it can’t be considered as the valid I see both REST and SOAP based web services existing quite happily together. xml technologies and some
  3. Specifications are being finalized to handle complex messaging: orchestration, coordination, and routing
  4. Due to the lack of critically adopted WS orchestration, composition, coordination standards, many are finding the MVC architecture approach a good match Instead of exposing a large variety of web services, expose one controller web service, which places the importance into the message body, simple interactions, complex messages. List MVC examples
  5. SOAP response could be styled by XSLT This technique lies between typical Web Services and REST
  6. I personally use Systinet WASP server, it takes care of everything a developer would want to not deal with….especially security. The folks who made WASP have a deep heritage with CORBA Instantly solve some problems…stateful web services Anchor is usually used in negative connotations, e.g. boat anchor antipatterns. With many specifications still being worked out, having an anchor in a storm is useful
  7. By integrating Amazon Web Services, Amazon.com Research Services for Microsoft Office System will provide Microsoft Office System users with convenient and seamless access to Amazon.com from within Microsoft productivity applications via the Research Task Pane. Users will be able to access Amazon.com information and make purchases without launching a browser or leaving their document, e-mail message or presentation. For example, a customer reading a bibliography in a Word document could easily click on a book title and purchase it from within the Research Task Pane without having to leave the Word document. Alternatively, a user will be able to add a footnote, bibliography entry and even cover art for books without needing to manually enter the information into a document. The Research Task Pane, a feature in the Microsoft Office 2003 Edition desktop applications (Word, Excel, the Outlook messaging and collaboration client, the PowerPoint presentation graphics program and Access) and in Microsoft Office System products OneNote (TM) note-taking program, Publisher and Visio drawing and diagramming software, uses industry-standard XML to enable users to retrieve and navigate relevant internal or external Web-based information, all from within Office programs. &amp;quot;Amazon.com is breaking new ground in its use of XML-enabled Web Services that connect data from disparate systems, allow greater access to content, and create a more valuable experience for Web users,&amp;quot; said Gytis Barzdukas, director of Office Product Management at Microsoft. &amp;quot;By using the advancements of the Microsoft Office System, Microsoft and Amazon.com are transforming the desktop into a dynamic interface for Office customers everywhere.&amp;quot; &amp;quot;We are excited to help make this new service available to our customers,&amp;quot; said Jeff Barr, Web services technical program manager at Amazon.com. &amp;quot;This Microsoft Office System solution adds significant convenience for our Microsoft users and Amazon.com customers in finding and discovering products. We look forward to receiving feedback from users and to adding more features in the future.&amp;quot;
  8. Google (file:wsdl, file:wsil ) Look for inspection.wsil Refer to xmethods or well known UDDI registries The importance of a human understandable description of a web service should not be underestimated, What if the human description is in a different language ? Is the interface enough for automatic composition methods ?
  9. With unlimited processing power and network bandwidth random search is fine. Intelligent software agents must have knowledge of the problem domain, either gained via learning ( neural network ) or through experts embedding knowledge As you will find out, GA does not need any specialist knowledge to solve a problem, and is quicker then random/linear search of large problem domains
  10. This approach is not specific to any problem domain…can be applied to anything different partial effective gene combinations or “schemata” are searched in parallel manner Analogies are good in computing, but can be dangerous and can cloud over some of the more subtle aspects. It may so happen, what happens actually in nature is completely irrevelent, it just so happens that for some groups of problems this technique is potentially useful. Just because an analogy ‘feels right’ does not mean it explains ‘how’ something works…analogies are good for illustration purposes, not for explanation purposes.
  11. where M(H, t) number of strings in population &apos;t&apos; with the schema &apos;H&apos;. f(H) average fitness of the strings with the schema &apos;H&apos;. F average fitness of the entire population. p1 probability of the schema being destroyed by crossover. p2 probability of the schema being destroyed by mutation. There are many variations
  12. There are primary and secondary operations in the genetic operation
  13. fitness is usually encompasses domain specific factors Primary operation: reproduction / recombination Secondary operation: mutation / editing / encapsulation
  14. I was reviewing a book by WROX, called Beginning Databases….and since I was xml through and through I was forced to re-examine the differences between hierarchical data models with relational, etc… . Somehow this investigation led me to S BOX structures in LISP…..which re-introduced me to the genetic algorithm…and the idea of partial schemata being used to solve problems. The xslt guru David Carlisle probably didn’t know it, but him and his lot at XSLT UK caused me to investigate the fp approach using XSLT
  15. LISP Symbolic expressions contain lists or atoms Use polish notation LISP is good at Programs and data have the same form A lisp program is its own parse tree EVAL function for lisp easy way to chain execution LISP facilitates the programming of hierarchical structures LISP is not a special GA language, in my opinion working with hierarchical computer programs is more expressive
  16. Most programming languages internally convert to a parse tree, xml and especially xslt is akin to LISP in that we have direct access to the ‘tree’. Since XSLT is xml, we can easily manipulate computer programs as if it were data, this is important in the genetic operations. Since XSLT is the language for transforming XML, we could use it to transform XSLT programs. In practice there is a performance hit to this approach. In any event, this talk focuses on the strategy, and not the precise implementation method.
  17. There will be reasons why I use ANT revealed later on, for one this was a natural choice as this talk is the final chapter in the previously mentioned book. Ant is a natural for dealing with lots of files, as we will be generating lots of xslt populations, applying transformations and various processes on them….was a no-brainer I have been using SAXON from the beginning, it is the only XSLT processor that implements XSLT 2.0.
  18. This type of equivalency problem was chosen to make the prototype’s output easy to validate In addition, looking for logical equivalency, not worried about whitespace at the moment
  19. * Comes from processing specific xslt individual with source.xml
  20. 500 xslt documents Going to generate 51 generations
  21. Can supply with parameters to define nodedepth, repeats, supply a random seed, weight odds for certain elements or attributes to be generated. Uses a DTD to define allowable elements. As you can see the example template really does nothing useful, it is typical that starting populations consistently have a low fitness for its ultimate purpose
  22. I wanted to reduce complexity in my early experiments so I avoided what I call early taxonomisation .
  23. We indirectly measure the fitness of an XSLT program by checking its output with a desired target xml. Transformation to each xslt individual in the population Best Fitness for our purposes is defined as an exact match between result and target xml. Fitness does not have to be the result of a single metric, we could have multiple tests for a fitness of an individual Source and target xml were supplied as part of the problem formulation
  24. Note that we have added a &lt;root/&gt; element, this is to ensure that XSLT that returned nothing, at least returned a valid xml document with one root node. There were situations where logically the fitness metric was not sufficient for certain special cases, in actuality having a number of source and target xml solved this issue.
  25. IBM’s is based on some novel thinking, though I have not used it ( commercial ) Microsoft’s is fine and fast
  26. Can choose the same individual for multiple operations, any number of times better fitness individuals have larger slice of the pie, so they will be selected more There can be some additional fitness penalties, for example in generation 0 many xslt files maybe invalid and not process at all.
  27. Raw fitness is a metric in terms of the problem, for example if you are trying to optimise some business process that sells products. The number of products sold could be the fitness ranking ( more the better ). Fitness could be calculated over a series of values and event outcomes, e.g. we could have multiple source and target xmls and the overall ranking of an individual would be its ranking
  28. From the selected population an individual is selected to be perfectly reproduced into the new generation
  29. Normally creates 2 offspring, though in nature this is not the case.
  30. Secondary operations tend to speed up convergence towards a solution, though if used too much will restrict convergence to ever occur.
  31. Pick a point and randomly mutate Asexual In xslt this must run XML generator again to obtain nodeset to augment. a form of crossover
  32. A random node is selected and its arguments are reorganized. Since ordering in xml is rarely important this operation has been omitted from our process Asexual
  33. If any function has no side effects, and is not context dependent, has only constant atoms as arguments the editing operation will evaluate that function and replace it with a value. &lt;xsl:if test=“true()”&gt; &lt;a&gt;&lt;/a&gt; &lt;/xsl:if&gt; &lt;xsl:value select=“count(//a)”/&gt; should always return the same amount if the source xml remains the same, so editing would resolve this and replace the xsl instruction with a 1.
  34. Identify useful subtrees by searching high fitness individuals for common subtrees. The effect of encapsulation is that the selected subtree is no longer subject to the potentially disruptive effects of crossover.
  35. Variety in a population drops quickly after generation 0, because GA focuses on marginally better fitness. To improve genetic diversity apply decimation, a set of rules which removes very poor fitness individuals. The example shows a 1 node XSLT, which is indeed very poor for solving our problem. An empty stylesheet is no use to us.
  36. There are situations where convergence around a single version never occurs
  37. Compiling xslt templates
  38. Its hard to apply genetic operations to languages that do not have any discreteness, like xml has with angle brackets demarcating each instruction. This is why s-boxes and the functional approach was the AI choice, because it was easy to
  39. * Comes from processing specific xslt individual with source.xml
  40. Harvesting program is found at www.semantic-web.co.uk wsil solved UDDI/WSDL umbrella
  41. SOAP 1.1 would have these HTTP headers: Content-Type: text/xml SOAPAction: &amp;quot;http://example.com/ticker“ SOAP 1.2 message would have the following: Content-Type: application/soap+_xml; action=http://example.com/ticker Moving all of the metadata into the one place where it should be is also a good thing.
  42. &lt;inspection/&gt; top level element defines namespaces used &lt;service/&gt; contains a service referencedNamespace, location of wsdl, UDDI specific stuff &lt;description/&gt; and &lt;link/&gt; may contain other elements, known as extensibility elements
  43. Shows how we can use with both UDDI and WSDL Link element imports more wsil service definitions 2 conventions of usage; place inspection.wsil in root web directory of web server or under current dir of the webservice itself with the root level wsil containing links to these encapsulated wsil docs. avoid the 2nd convention of using a meta tag and use a RDDL doc to describe &lt;HEAD&gt; &lt;META name=&amp;quot;serviceInspection&amp;quot; content=&amp;quot;localservices.wsil&amp;quot;&gt; &lt;META name=&amp;quot;serviceInspection&amp;quot; content=&amp;quot;http://www.example.com/calculators.wsil&amp;quot;&gt; &lt;META name=&amp;quot;serviceInspection&amp;quot; content=&amp;quot;ftp://www.anotherexample.com/translators.wsil&amp;quot;&gt; &lt;/HEAD&gt; xml schema exists
  44. Notice extension mechanism Very easy to extend, any description or link element can have extension element
  45. 500 xslt documents Going to generate 51 generations
  46. higher order orchestration standards are striving to become established supporting standards for SOA should stabilize by Q2 2004, with heavy commercial uptake for Q4 2004 XML, XSLT, and XPATH are successful XML schema, RELAX NG and DTD primary forms of schema languages UDDI is struggling to make an impact with developers There are some key differences though between SOA and CORBA/DCOM/RMI that developers and architects are getting confused with. We are possibly occupying that no mans land between white box reuse and true black box components
  47. Does a car build itself based on a set of criteria ? Do we expect it ? Nano technology ……. Allowing problem domain experts to formulate problems assists in direct requirements capture Will a functional approach be the true path to black box reuse ? In a world of unlimited processing, who cares if a computer program is elegantly constructed ? In a world of unlimited bandwidth who cares if we use XML as the preferred over the wire format ? Successful programmatic methods are useful because they assist in modeling the problem. If that model is then used to generate a million line program…..focus on model-led development