SlideShare ist ein Scribd-Unternehmen logo
1 von 20
Integrating practical
     Author/Presenter: David Fergusson
            Technology Masters –
Alain Roy, Ben Clifford, Rebbeca Breu, Carlos
 Aranda, Emidio Giorgio, Tony Calanduci,
        Steve Crouch, Tilaye Alemu

           SFK Master – Ted Wen
SfK = simulation of typical
    e-Science Research

    Collaboration between scientists (your group)
    Exploring large amounts of Data to find particular patterns of interest
         Astronomy
         Particle physics
         Biomedicine
         Geophysics

           ….
    Using results of other researchers’ work
The Pillars of Wisdom

                                         background
                                         Pillar –
                                         Overrides background
                                         Rectangular
                                         Constant height
                                         Aligned with x-y axis
                                            Plaque –
                                            Overrides background
                                            Rectangular
                                            Constant height
                                            > Or < pillar height
                                            Aligned with x-y axis

                                              Word or phrase –
                                              Overrides background
                                              Rectangular
                            Wise Words        Constant height
Total of 20 Pillars                           > Or < plaque height
                                              Aligned with x-y axis
Hints




                                                            Hints are found
                                                            using OGSA-DAI

                                       Hints look like this (in relational form):

                                 x       y          form    technology
                                 12.554886          2.295809        CALCULATE
                                 764.082765         91.932643       DATA OMII

  Boundary of the Surface
 Hints, (Xi, Yi), obtained by “previous research teams” - may not be
  completely trustworthy!
 Hints tell you which technology to use and the form of the data
  (calculated on the fly or stored as files and accessed via metadata)
Real search space
characteristics
 In real life
      Noise is much larger and pervasive (ie. Top of pillars)
         Ratio of signal to noise is usually larger
      Search spaces generally larger
      Don’t normally know the complete parameters of your search
       space
         E.G. Boundaries & alignment

 So, here, we do not need statistics to analyse the
  patterns
 Do not need to run complex models
 You are being given the searching tool (may often be the
  case in real life)
tex
 t




      6
tex
 t




      7
tex
 t




      8
Bounding box

                     X2, y2


               tex
                t        Step size
  x1, y1




                                     9
tex
 t




      10
tex
 t




      11
Text can have white space – make sure you find all of it



tex
 t




                                                            12
Search space
(conceptual view, not actual)

                                          10000



                                          Computed data area


 -10000                                                        10000

          GridSAM   GT4         UNICORE    Condor      gLite

                                           Stored data area




                                -10000
Framework



Expected to
 Write program(s) / script(s)
   To   run explorations across Surface,
      E.G. interfacing tools for displaying result with
       technologies that deliver those results
   to   find
      Pillars, then plaques
 Do visualisation on Plaques to read Wisdom
  Words
 Recognise the pattern
 Making full use of capabilities
Scanner tool
 Use the Scanner tool to search for a pillar in a given area
 From the command line,
 java -jar sfkscan-XXX.jar <x1> <y1> <x2>
  <y2> <step_size>
      (XXX = technology name: glite globus condor
       unicore gridsam)
      X1y1 = bottom left, x2y2 = top right
 If a pillar is found in the given area, the word on the
  plaque will be printed on the screen.
 It can be saved in a text file to view better if the word
  wraps on the screen.
 This can be done through redirecting the output to a file.
                                                              15
Parameters
 Area = -10,000 to 10,000
 Step size range = 0.0001 -> 0.1
 Each technology has sample pillar that
  you will be given.

 30 pillars in total



                                           16
Semantic Grid Integrating Practical
 Objectives
      Use the lessons learned in the Semantic Grid Practical
      Query the metadata stored in a Globus container
 Procedure
      After you find all the words hidden in the pillars…
      Connect to issgc-client-01.polytech.unice.fr
      Use the query-all-notes command in the Globus installation
      Instructions: http://www.dia.fi.upm.es/~ocorcho/ISSGC2009/
       web/index_integrating.html
 Results
      The results are the name of the elements you were querying
       to the Metadata Query Service: 8 words
      Combining these words with the previous ones from the
       pillars you get the final solution
      http://www.dia.fi.upm.es/~ocorcho/ISSGC2009/Inte
       gratingWeb/integrating.html
Reporting colums

 Browse to
 http://dc06.nesc.ed.ac.uk:8080/sfk/
 To enter discovered pillars:
   Group   - this is your group number, (do not
    add for other groups!)
   Text - the whole text you found on the pillar
    (need to find all of it)
   x1, y1, x2, y2 - specify a bounding box that
    includes all of that text

                                                    18
Reporting your results
 Each team will get
    5 minutes during session 56
    2 minutes to change over and get started!
 Maximum number of slides
    Title
    4 others
 What you learnt and insights gained
    Results of the search
    Technologies used
    Evaluation of technologies
    Evaluation of your strategies
       Team organisation, roles and how they worked
Instructions
Technology-specific instructions for the integrating practical:

Submission: (When a pillar is found, submit it to this website)
http://dc06.nesc.ed.ac.uk:8080/sfk/
   Condor:
http://pages.cs.wisc.edu/~roy/grid_school_2009/integrating.html
2. GridSAM:
http://www.ecs.soton.ac.uk/~stc/ISSGC09/GridSAMIntegratingPractical.htm
3. gLite:
http://issgc-server-01.polytech.unice.fr/glite/issgc09/glite-integrating-practical.html
4. Globus:
http://www.ci.uchicago.edu/~benc/issgc09/integrating.html
5. UNICORE:
http://www.fz-juelich.de/jsc/unicore/ISSGC09/
6. OGSA-DAI:
http://homepages.nesc.ac.uk/~elias/issgc09/html/hints.html
7. Semantic Grid
http://www.dia.fi.upm.es/~ocorcho/ISSGC2009/IntegratingWeb/integrating.html
                                                                                          20

Weitere ähnliche Inhalte

Andere mochten auch

Session 58 - Cloud computing, virtualisation and the future
Session 58 - Cloud computing, virtualisation and the future Session 58 - Cloud computing, virtualisation and the future
Session 58 - Cloud computing, virtualisation and the future
ISSGC Summer School
 
Session 58 :: Cloud computing, virtualisation and the future Speaker: Ake Edlund
Session 58 :: Cloud computing, virtualisation and the future Speaker: Ake EdlundSession 58 :: Cloud computing, virtualisation and the future Speaker: Ake Edlund
Session 58 :: Cloud computing, virtualisation and the future Speaker: Ake Edlund
ISSGC Summer School
 
Session 48 - Principles of Semantic metadata management
Session 48 - Principles of Semantic metadata management Session 48 - Principles of Semantic metadata management
Session 48 - Principles of Semantic metadata management
ISSGC Summer School
 
Session 50 - High Performance Computing Ecosystem in Europe
Session 50 - High Performance Computing Ecosystem in EuropeSession 50 - High Performance Computing Ecosystem in Europe
Session 50 - High Performance Computing Ecosystem in Europe
ISSGC Summer School
 
Session 49 Practical Semantic Sticky Note
Session 49 Practical Semantic Sticky NoteSession 49 Practical Semantic Sticky Note
Session 49 Practical Semantic Sticky Note
ISSGC Summer School
 

Andere mochten auch (6)

Departure
DepartureDeparture
Departure
 
Session 58 - Cloud computing, virtualisation and the future
Session 58 - Cloud computing, virtualisation and the future Session 58 - Cloud computing, virtualisation and the future
Session 58 - Cloud computing, virtualisation and the future
 
Session 58 :: Cloud computing, virtualisation and the future Speaker: Ake Edlund
Session 58 :: Cloud computing, virtualisation and the future Speaker: Ake EdlundSession 58 :: Cloud computing, virtualisation and the future Speaker: Ake Edlund
Session 58 :: Cloud computing, virtualisation and the future Speaker: Ake Edlund
 
Session 48 - Principles of Semantic metadata management
Session 48 - Principles of Semantic metadata management Session 48 - Principles of Semantic metadata management
Session 48 - Principles of Semantic metadata management
 
Session 50 - High Performance Computing Ecosystem in Europe
Session 50 - High Performance Computing Ecosystem in EuropeSession 50 - High Performance Computing Ecosystem in Europe
Session 50 - High Performance Computing Ecosystem in Europe
 
Session 49 Practical Semantic Sticky Note
Session 49 Practical Semantic Sticky NoteSession 49 Practical Semantic Sticky Note
Session 49 Practical Semantic Sticky Note
 

Ähnlich wie Integrating Practical2009

Web Data Extraction Como2010
Web Data Extraction Como2010Web Data Extraction Como2010
Web Data Extraction Como2010
Giorgio Orsi
 

Ähnlich wie Integrating Practical2009 (20)

Web Data Extraction Como2010
Web Data Extraction Como2010Web Data Extraction Como2010
Web Data Extraction Como2010
 
Software Testing:
 A Research Travelogue 
(2000–2014)
Software Testing:
 A Research Travelogue 
(2000–2014)Software Testing:
 A Research Travelogue 
(2000–2014)
Software Testing:
 A Research Travelogue 
(2000–2014)
 
Easy edd phd talks 28 oct 2008
Easy edd phd talks 28 oct 2008Easy edd phd talks 28 oct 2008
Easy edd phd talks 28 oct 2008
 
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
 
Standardizing on a single N-dimensional array API for Python
Standardizing on a single N-dimensional array API for PythonStandardizing on a single N-dimensional array API for Python
Standardizing on a single N-dimensional array API for Python
 
High-Performance Graph Analysis and Modeling
High-Performance Graph Analysis and ModelingHigh-Performance Graph Analysis and Modeling
High-Performance Graph Analysis and Modeling
 
Using Deep Learning to do Real-Time Scoring in Practical Applications
Using Deep Learning to do Real-Time Scoring in Practical ApplicationsUsing Deep Learning to do Real-Time Scoring in Practical Applications
Using Deep Learning to do Real-Time Scoring in Practical Applications
 
AI and Deep Learning
AI and Deep Learning AI and Deep Learning
AI and Deep Learning
 
Principal Component Analysis For Novelty Detection
Principal Component Analysis For Novelty DetectionPrincipal Component Analysis For Novelty Detection
Principal Component Analysis For Novelty Detection
 
Claire98
Claire98Claire98
Claire98
 
Distributed Deep Learning + others for Spark Meetup
Distributed Deep Learning + others for Spark MeetupDistributed Deep Learning + others for Spark Meetup
Distributed Deep Learning + others for Spark Meetup
 
lecture1.ppt
lecture1.pptlecture1.ppt
lecture1.ppt
 
Deep Learning And Business Models (VNITC 2015-09-13)
Deep Learning And Business Models (VNITC 2015-09-13)Deep Learning And Business Models (VNITC 2015-09-13)
Deep Learning And Business Models (VNITC 2015-09-13)
 
Greg Hogan – To Petascale and Beyond- Apache Flink in the Clouds
Greg Hogan – To Petascale and Beyond- Apache Flink in the CloudsGreg Hogan – To Petascale and Beyond- Apache Flink in the Clouds
Greg Hogan – To Petascale and Beyond- Apache Flink in the Clouds
 
Software Defined Visualization (SDVis): Get the Most Out of ParaView* with OS...
Software Defined Visualization (SDVis): Get the Most Out of ParaView* with OS...Software Defined Visualization (SDVis): Get the Most Out of ParaView* with OS...
Software Defined Visualization (SDVis): Get the Most Out of ParaView* with OS...
 
Computational decision making
Computational decision makingComputational decision making
Computational decision making
 
Machine Learning and Go. Go!
Machine Learning and Go. Go!Machine Learning and Go. Go!
Machine Learning and Go. Go!
 
Neural Networks: Support Vector machines
Neural Networks: Support Vector machinesNeural Networks: Support Vector machines
Neural Networks: Support Vector machines
 
Fosdem 2013 petra selmer flexible querying of graph data
Fosdem 2013 petra selmer   flexible querying of graph dataFosdem 2013 petra selmer   flexible querying of graph data
Fosdem 2013 petra selmer flexible querying of graph data
 
24-TensorFlow-Clipper.pptxnjjjjnjjjjjjmm
24-TensorFlow-Clipper.pptxnjjjjnjjjjjjmm24-TensorFlow-Clipper.pptxnjjjjnjjjjjjmm
24-TensorFlow-Clipper.pptxnjjjjnjjjjjjmm
 

Mehr von ISSGC Summer School

Session 49 - Semantic metadata management practical
Session 49 - Semantic metadata management practical Session 49 - Semantic metadata management practical
Session 49 - Semantic metadata management practical
ISSGC Summer School
 
Session 46 - Principles of workflow management and execution
Session 46 - Principles of workflow management and execution Session 46 - Principles of workflow management and execution
Session 46 - Principles of workflow management and execution
ISSGC Summer School
 
Session 37 - Intro to Workflows, API's and semantics
Session 37 - Intro to Workflows, API's and semantics Session 37 - Intro to Workflows, API's and semantics
Session 37 - Intro to Workflows, API's and semantics
ISSGC Summer School
 
Session 24 - Distribute Data and Metadata Management with gLite
Session 24 - Distribute Data and Metadata Management with gLiteSession 24 - Distribute Data and Metadata Management with gLite
Session 24 - Distribute Data and Metadata Management with gLite
ISSGC Summer School
 
General Introduction to technologies that will be seen in the school
General Introduction to technologies that will be seen in the school General Introduction to technologies that will be seen in the school
General Introduction to technologies that will be seen in the school
ISSGC Summer School
 

Mehr von ISSGC Summer School (20)

Session 49 - Semantic metadata management practical
Session 49 - Semantic metadata management practical Session 49 - Semantic metadata management practical
Session 49 - Semantic metadata management practical
 
Session 46 - Principles of workflow management and execution
Session 46 - Principles of workflow management and execution Session 46 - Principles of workflow management and execution
Session 46 - Principles of workflow management and execution
 
Session 42 - GridSAM
Session 42 - GridSAMSession 42 - GridSAM
Session 42 - GridSAM
 
Session 37 - Intro to Workflows, API's and semantics
Session 37 - Intro to Workflows, API's and semantics Session 37 - Intro to Workflows, API's and semantics
Session 37 - Intro to Workflows, API's and semantics
 
Session 43 :: Accessing data using a common interface: OGSA-DAI as an example
Session 43 :: Accessing data using a common interface: OGSA-DAI as an exampleSession 43 :: Accessing data using a common interface: OGSA-DAI as an example
Session 43 :: Accessing data using a common interface: OGSA-DAI as an example
 
Session 40 : SAGA Overview and Introduction
Session 40 : SAGA Overview and Introduction Session 40 : SAGA Overview and Introduction
Session 40 : SAGA Overview and Introduction
 
Session 36 - Engage Results
Session 36 - Engage ResultsSession 36 - Engage Results
Session 36 - Engage Results
 
Session 23 - Intro to EGEE-III
Session 23 - Intro to EGEE-IIISession 23 - Intro to EGEE-III
Session 23 - Intro to EGEE-III
 
Session 33 - Production Grids
Session 33 - Production GridsSession 33 - Production Grids
Session 33 - Production Grids
 
Social Program
Social ProgramSocial Program
Social Program
 
Session29 Arc
Session29 ArcSession29 Arc
Session29 Arc
 
Session 24 - Distribute Data and Metadata Management with gLite
Session 24 - Distribute Data and Metadata Management with gLiteSession 24 - Distribute Data and Metadata Management with gLite
Session 24 - Distribute Data and Metadata Management with gLite
 
Session 23 - gLite Overview
Session 23 - gLite OverviewSession 23 - gLite Overview
Session 23 - gLite Overview
 
General Introduction to technologies that will be seen in the school
General Introduction to technologies that will be seen in the school General Introduction to technologies that will be seen in the school
General Introduction to technologies that will be seen in the school
 
Session 3-Distributed System Principals
Session 3-Distributed System PrincipalsSession 3-Distributed System Principals
Session 3-Distributed System Principals
 
Session10part2 Servers Detailed
Session10part2  Servers DetailedSession10part2  Servers Detailed
Session10part2 Servers Detailed
 
Session18 Madduri
Session18  MadduriSession18  Madduri
Session18 Madduri
 
Session6 Security Emidio
Session6 Security  EmidioSession6 Security  Emidio
Session6 Security Emidio
 
Session9part1
Session9part1Session9part1
Session9part1
 
Session19 Globus
Session19 GlobusSession19 Globus
Session19 Globus
 

Kürzlich hochgeladen

Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global Impact
PECB
 
Gardella_Mateo_IntellectualProperty.pdf.
Gardella_Mateo_IntellectualProperty.pdf.Gardella_Mateo_IntellectualProperty.pdf.
Gardella_Mateo_IntellectualProperty.pdf.
MateoGardella
 
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdfMaking and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdf
Chris Hunter
 

Kürzlich hochgeladen (20)

fourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingfourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writing
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global Impact
 
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAPM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across Sectors
 
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxSOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
 
PROCESS RECORDING FORMAT.docx
PROCESS      RECORDING        FORMAT.docxPROCESS      RECORDING        FORMAT.docx
PROCESS RECORDING FORMAT.docx
 
Gardella_Mateo_IntellectualProperty.pdf.
Gardella_Mateo_IntellectualProperty.pdf.Gardella_Mateo_IntellectualProperty.pdf.
Gardella_Mateo_IntellectualProperty.pdf.
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 
Class 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdfClass 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdf
 
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
 
Unit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptxUnit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptx
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
 
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
 
Advance Mobile Application Development class 07
Advance Mobile Application Development class 07Advance Mobile Application Development class 07
Advance Mobile Application Development class 07
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
 
Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..
 
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdfMaking and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdf
 
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
 

Integrating Practical2009

  • 1. Integrating practical Author/Presenter: David Fergusson Technology Masters – Alain Roy, Ben Clifford, Rebbeca Breu, Carlos Aranda, Emidio Giorgio, Tony Calanduci, Steve Crouch, Tilaye Alemu SFK Master – Ted Wen
  • 2. SfK = simulation of typical e-Science Research  Collaboration between scientists (your group)  Exploring large amounts of Data to find particular patterns of interest  Astronomy  Particle physics  Biomedicine  Geophysics ….  Using results of other researchers’ work
  • 3. The Pillars of Wisdom background Pillar – Overrides background Rectangular Constant height Aligned with x-y axis Plaque – Overrides background Rectangular Constant height > Or < pillar height Aligned with x-y axis Word or phrase – Overrides background Rectangular Wise Words Constant height Total of 20 Pillars > Or < plaque height Aligned with x-y axis
  • 4. Hints Hints are found using OGSA-DAI Hints look like this (in relational form): x y form technology 12.554886 2.295809 CALCULATE 764.082765 91.932643 DATA OMII Boundary of the Surface  Hints, (Xi, Yi), obtained by “previous research teams” - may not be completely trustworthy!  Hints tell you which technology to use and the form of the data (calculated on the fly or stored as files and accessed via metadata)
  • 5. Real search space characteristics  In real life  Noise is much larger and pervasive (ie. Top of pillars)  Ratio of signal to noise is usually larger  Search spaces generally larger  Don’t normally know the complete parameters of your search space  E.G. Boundaries & alignment  So, here, we do not need statistics to analyse the patterns  Do not need to run complex models  You are being given the searching tool (may often be the case in real life)
  • 6. tex t 6
  • 7. tex t 7
  • 8. tex t 8
  • 9. Bounding box X2, y2 tex t Step size x1, y1 9
  • 10. tex t 10
  • 11. tex t 11
  • 12. Text can have white space – make sure you find all of it tex t 12
  • 13. Search space (conceptual view, not actual) 10000 Computed data area -10000 10000 GridSAM GT4 UNICORE Condor gLite Stored data area -10000
  • 14. Framework Expected to  Write program(s) / script(s)  To run explorations across Surface,  E.G. interfacing tools for displaying result with technologies that deliver those results  to find  Pillars, then plaques  Do visualisation on Plaques to read Wisdom Words  Recognise the pattern  Making full use of capabilities
  • 15. Scanner tool  Use the Scanner tool to search for a pillar in a given area  From the command line,  java -jar sfkscan-XXX.jar <x1> <y1> <x2> <y2> <step_size>  (XXX = technology name: glite globus condor unicore gridsam)  X1y1 = bottom left, x2y2 = top right  If a pillar is found in the given area, the word on the plaque will be printed on the screen.  It can be saved in a text file to view better if the word wraps on the screen.  This can be done through redirecting the output to a file. 15
  • 16. Parameters  Area = -10,000 to 10,000  Step size range = 0.0001 -> 0.1  Each technology has sample pillar that you will be given.  30 pillars in total 16
  • 17. Semantic Grid Integrating Practical  Objectives  Use the lessons learned in the Semantic Grid Practical  Query the metadata stored in a Globus container  Procedure  After you find all the words hidden in the pillars…  Connect to issgc-client-01.polytech.unice.fr  Use the query-all-notes command in the Globus installation  Instructions: http://www.dia.fi.upm.es/~ocorcho/ISSGC2009/ web/index_integrating.html  Results  The results are the name of the elements you were querying to the Metadata Query Service: 8 words  Combining these words with the previous ones from the pillars you get the final solution  http://www.dia.fi.upm.es/~ocorcho/ISSGC2009/Inte gratingWeb/integrating.html
  • 18. Reporting colums  Browse to http://dc06.nesc.ed.ac.uk:8080/sfk/  To enter discovered pillars:  Group - this is your group number, (do not add for other groups!)  Text - the whole text you found on the pillar (need to find all of it)  x1, y1, x2, y2 - specify a bounding box that includes all of that text 18
  • 19. Reporting your results  Each team will get  5 minutes during session 56  2 minutes to change over and get started!  Maximum number of slides  Title  4 others  What you learnt and insights gained  Results of the search  Technologies used  Evaluation of technologies  Evaluation of your strategies  Team organisation, roles and how they worked
  • 20. Instructions Technology-specific instructions for the integrating practical: Submission: (When a pillar is found, submit it to this website) http://dc06.nesc.ed.ac.uk:8080/sfk/  Condor: http://pages.cs.wisc.edu/~roy/grid_school_2009/integrating.html 2. GridSAM: http://www.ecs.soton.ac.uk/~stc/ISSGC09/GridSAMIntegratingPractical.htm 3. gLite: http://issgc-server-01.polytech.unice.fr/glite/issgc09/glite-integrating-practical.html 4. Globus: http://www.ci.uchicago.edu/~benc/issgc09/integrating.html 5. UNICORE: http://www.fz-juelich.de/jsc/unicore/ISSGC09/ 6. OGSA-DAI: http://homepages.nesc.ac.uk/~elias/issgc09/html/hints.html 7. Semantic Grid http://www.dia.fi.upm.es/~ocorcho/ISSGC2009/IntegratingWeb/integrating.html 20