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g-Social
    Enhancing e-Science Tools with Social
         Networking Functionality

Andriani Stylianou, Nicholas Loulloudes, Marios D. Dikaiakos
Overview

•   Introduction
•   Motivation
•   Problem
•   Current Solutions
•   g-Social – Our Solution
•   Abstractions
•   Implementation
•   Conclusion - Questions


                              2
Fourth Paradigm of Scientific Exploration (J. Gray)
Source: J. Gray, talk to NRC/CSTB, “eScience - A Transformed Scientific
Method.” Mountain View CA, 11 January 2007.
  • Thousand years ago science was empirical
     –    describing natural phenomena
  • Last few hundred years: theoretical branch
     –    using models, generalizations
  • Last few decades: a computational branch
     –    simulating complex phenomena
  • Today: data exploration (eScience)
     – unify theory, experiment, and simulation
     – Data captured by instruments
       Or generated by simulator
     – Processed by software
     – Information/Knowledge stored in computer
     – Scientist analyzes database / files
       using data management and statistics
     – “Computational X” and “X-Informatics”                    2009

                                                                          3
The disappearance of Tenacious (28/1/2007)




Farallon
Islands




           Jim Gray
           Manager of Microsoft Research's eScience Group.
           1998 ACM Turing Award


                                                             4
The search for Tenacious (28/1/07 - 16/2/07)
• Night of 28/1: the USCG launched an airborne and seaborne SAR
  operation for Tenacious
  – The SAR lasted for nearly two weeks - no signs found
• 31/1: the scientific community mobilized to help the SAR mission using
  online tools
  – Computer scientists, oceanographers, engineers, volunteers, and Silicon Valley
    power players [NASA’s JPL, Amazon, Microsoft, Oracle, US Navy, Monterey Bay Aquarium Research
     Institute, SDSC, Cornell Theory Center, Purdue, UWisc, Singular, Canadian Space Agency, Digital Globe.]
• A blog was setup to coordinate efforts and share ideas.Main foci of the
  effort were:
  – Map the trajectory that Tenacious might have followed, in case Jim Gray
    lost control of the boat - to help guide the SAR operation
  – Discover clues about Tenacious presence at sea
  – Map the trajectories of large vessels traveling in the area, that may have
    collided with Tenacious

                     US/CG scoured 132,000 sq. miles of ocean
                                                                                                           5
Drift modeling




                 6
The search for Tenacious: online version
     An exemplary e-Science application scenario
• A multidisciplinary virtual organization of people with a common goal
  – Scientists, engineers, managers, officials, volunteers
• A variety of algorithms and software tools:
  – Ocean-current models and simulators, image processing &
    recognition, cellphone signal tracking and triangulation, data-format
    transformation, data cleansing, satellite collection planning, data
    mining, image geo-referencing
• A deluge of data (hundreds of GBs) retrieved over the net from various
sources, requiring processing and fusion to extract knowledge
  – Satellite orbits, satellite imagery at different resolutions, multispectral
    datasets, Web Databases, radio buoy and airborne sensors, HF radars, data
    about offshore currents, Web cameras
• A federation of computing, networking and service infrastructures
  – Grids, clusters, storage devices, crowd-sourcing services
                                                                             7
Computing Grids
• e-Science motivated the development of Grid technologies and
  Federated Computing Infrastructures during the last decade.
• The Grid vision by Foster, Kesselman, Tuecke [Grid 1.0]:
  – Distributed         computing        infrastructures        that        enable
    flexible, secure, coordinated resource sharing among dynamic collections of
    individuals and institutions
  – Enable communities ( “ Virtual Organizations ” ) to share geographically
    distributed resources as they pursue common goals, in the absence of:
    Homogeneity, Central location, Central control, Existing trust relationships


• The hype following the Grid:
  – One of the sources of the impact of scientific and technological changes on
    the economy and society [Jeremy Rifkin, “The European Dream,” Penguin
    2004]
  – The Grid has been described as the Next Generation Internet, the
    implementation of the Global Computer etc.
                                                                                 8
Grid Infrastructure development
‣ Nowadays, Grid infrastructures comprise an impressive
  collection of computational and software resources
  ‣ drawing an increasing number of users from various disciplines




                                                               9
Data-Intensive Scientific Projects
Motivation

    Grid / Cloud Computing




            Scientists




Resources

  Traditional Collaboration Tools


                                                                10
Problem
• Collaboration is done externally to scientific
  software                         environments
  (email, web, portals, IM, etc.).
• Manual effort for transferring information
  from one tool to another.
• Error prone and time consuming.

 Lack of a unified, user-friendly software and
   collaboration environment for scientists.

                                                 11
Current Solutions
                   Pros
                   • Professional Networking
                   • Minimal Collaboration Functionality
General-Purpose
                   Cons
     OSN           • External to existing scientific software
                      environments – Web Based
                   • Do not support resource* sharing


                   Pros
                   • More immersive collaboration environment
                      than Generic OSN.
                   • Resource sharing and ability to run
                      experiments.
Scientific OSN     Cons
                   • Application Domain Specific.
                   • Proprietary     infrastructures  –    High
                      maintenance.
                   • Introduce additional information sources ->
                      User Information overload                13
Our Solution
g-Eclipse (www.eclipse.org/geclipse)
• Integrated workbench framework
• Build on-top of Eclipse (Extensible and community support)
• Toolset for users, operators & developers of Grid/Cloud infrastructures
  (gLite, GRIA, Amazon AWS) – Middleware agnostic
• Rich functionality:
    • Development & Deployment
    • Benchmarking & Testing
    • Workflow Programming



Online Social Networks
• Easy establishment and management of groups
• Automatic dissemination of notifications
• Professional Networking
• High Availability


                                                                            14
g-Eclipse
Grid Project
   View

                                                                  W
                                                                  o
                                                                  r
                                                                  k
                                                                  b
                                                                  e
                                                                  n
                                                                  c
                                                                  h




 Information View   Authentication View   JSDL Editor View

                                                             15
g-Social
Build on-top of the g-Eclipse Framework
Aims to enable collaboration among scientists that are/will utilize g-Eclipse

Features
• Social Abstractions (Resources, Meta-data, Authentication).
• Definition of structured and standardized social meta-data
• Enrich social meta-data with links to project related resources.
• Access resources easily .
• Share project data and meta-data.
• Retrieve shared information.
• Seamless interaction with OSN.
   • Facebook
   • Twitter
• Extensible for other OSNs

                                        g-Social Work Cycle                     16
g-Social Abstractions

Enable seamless sharing and retrieval (via an OSN) of all particulars of the
research work performed in the context of a real scientific project.

Abstract a Scientific Collaborative Environment which utilize Online Social
Networks.




                                                                           17
Abstractions - Resources

Any file(s) related to the execution of
a Grid task specific to a scientific
project
• Input / Output Dataset
• Executable
• Source Code
• Documentation
• Publications
• …



                                          18
Abstractions – Social Meta-data
Descriptive meta-data that provide to
the OSN and its users information
about purpose and function of each
shared particular
• Name
• Function
• Purpose
• Version
• Tags
• License
• ….


                                        19
Abstractions – Authentication Manager

Enforces security and privacy control
of users while interacting with the
OSN
• Authorization / Authentication
   against an OSN
• Monitor life-cycle of authentication
   tokens




                                         20
Abstractions – Resource Manager

Resource sharing
• Interact with Authentication Manager
• Social meta-data
• Encapsulate the above in a form
  acceptable by and OSN

Resource Retrieval
• Extraction of published meta-data
• g-Eclipse    Authentication   Manager
  invocation
• Resource access via g-Eclipse file
  system
• Resource import in g-Eclipse workspace


                                           21
Abstractions – OSN Interface

• OSN are by design web-based
  systems
• OSN-gEclipse interface serves as an
  intermediate between the web-
  browser and g-Eclipse.
• Invoking g-Eclipse when user clicks
  on an g-Social link inside an OSN.




                                        22
g-Social Implementation
• The g-Eclipse Grid Project.
• A placeholder for the organization of
files/information related to the execution of
Grid/Cloud tasks
    • Executables (local file system)
    • Input / Output dataset (g-Lite, AWS)
    • Documentation
    • Publication (IEEE, ACM, Elsevier)
    • Infrastructure Configurations




                                                23
Implementation (Social Meta-Data Editor)
                                      • Multi-Page GUI Editor
                                      • Easy Insertion of social
                                        meta-data
                                      • Specify Location of
                                        Resources




• XML content meta-data
• Extend Job Submission Definition
  Language (JSDL) schema to include
  social meta-data specification.
                                                             24
g-Social View

  Collaborators            Search for Shared Jobs   OSN Authentication




  List of Shared Jobs                                   Share Job

                        View Job Details
                                                                     25
Implementation (g-Social View)




                          Authorization
                          • Authenticate / Authorize
                            against OSN
                          • Check auth of the underlying
                            storage infrastructure when
                            linking or retrieving a
                            resource
                          • Manage auth tokens life-
                            cycle
                                                    26
Implementation (g-Social View)




                         Share Job to OSN
                         • Share job details as defined
                           in meta-data editor
                         • Ask user to which OSN
                           details should be posted
                         • Parse social meta-data
                         • Encapsulate them in OSN
                           specific post formats.
                                                     27
Implementation (g-Social View)




View Share Job Details
• Social Meta-data
    • Name
    • Description
    • Version

• Resource Handles
   • Download Resource


                                 28
Conclusions & Future Work
Conclusions
g-Social enhances integrated e-Science Tools (g-Eclipse) with
Social Networking functionality. Specifically it:
• Enables the definition of social meta-data for sharing and
    retrieval of information among scientists.
• Enriches meta-data with resource handles which might be
    scattered in heterogeneous storage infrastructures.
• Provides mechanisms for sharing and retrieving scientific
    information with just a few clicks.
Future Work
•   Standardize social meta-data definition
•   Support additional OSNs
•   Recommendation System
•   Release g-Social to Eclipse
                                                           29
Questions – Contact Information

Andriani Stylianou (andriani.stylianou@epfl.ch)
Nicholas Loulloudes (loulloudes.n@cs.ucy.ac.cy)
Marios D. Dikaiakos (mdd@cs.ucy.ac.cy)




                      http://grid.ucy.ac.cy


                                                  30

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g-Social - Enhancing e-Science Tools with Social Networking Functionality

  • 1. g-Social Enhancing e-Science Tools with Social Networking Functionality Andriani Stylianou, Nicholas Loulloudes, Marios D. Dikaiakos
  • 2. Overview • Introduction • Motivation • Problem • Current Solutions • g-Social – Our Solution • Abstractions • Implementation • Conclusion - Questions 2
  • 3. Fourth Paradigm of Scientific Exploration (J. Gray) Source: J. Gray, talk to NRC/CSTB, “eScience - A Transformed Scientific Method.” Mountain View CA, 11 January 2007. • Thousand years ago science was empirical – describing natural phenomena • Last few hundred years: theoretical branch – using models, generalizations • Last few decades: a computational branch – simulating complex phenomena • Today: data exploration (eScience) – unify theory, experiment, and simulation – Data captured by instruments Or generated by simulator – Processed by software – Information/Knowledge stored in computer – Scientist analyzes database / files using data management and statistics – “Computational X” and “X-Informatics” 2009 3
  • 4. The disappearance of Tenacious (28/1/2007) Farallon Islands Jim Gray Manager of Microsoft Research's eScience Group. 1998 ACM Turing Award 4
  • 5. The search for Tenacious (28/1/07 - 16/2/07) • Night of 28/1: the USCG launched an airborne and seaborne SAR operation for Tenacious – The SAR lasted for nearly two weeks - no signs found • 31/1: the scientific community mobilized to help the SAR mission using online tools – Computer scientists, oceanographers, engineers, volunteers, and Silicon Valley power players [NASA’s JPL, Amazon, Microsoft, Oracle, US Navy, Monterey Bay Aquarium Research Institute, SDSC, Cornell Theory Center, Purdue, UWisc, Singular, Canadian Space Agency, Digital Globe.] • A blog was setup to coordinate efforts and share ideas.Main foci of the effort were: – Map the trajectory that Tenacious might have followed, in case Jim Gray lost control of the boat - to help guide the SAR operation – Discover clues about Tenacious presence at sea – Map the trajectories of large vessels traveling in the area, that may have collided with Tenacious US/CG scoured 132,000 sq. miles of ocean 5
  • 7. The search for Tenacious: online version An exemplary e-Science application scenario • A multidisciplinary virtual organization of people with a common goal – Scientists, engineers, managers, officials, volunteers • A variety of algorithms and software tools: – Ocean-current models and simulators, image processing & recognition, cellphone signal tracking and triangulation, data-format transformation, data cleansing, satellite collection planning, data mining, image geo-referencing • A deluge of data (hundreds of GBs) retrieved over the net from various sources, requiring processing and fusion to extract knowledge – Satellite orbits, satellite imagery at different resolutions, multispectral datasets, Web Databases, radio buoy and airborne sensors, HF radars, data about offshore currents, Web cameras • A federation of computing, networking and service infrastructures – Grids, clusters, storage devices, crowd-sourcing services 7
  • 8. Computing Grids • e-Science motivated the development of Grid technologies and Federated Computing Infrastructures during the last decade. • The Grid vision by Foster, Kesselman, Tuecke [Grid 1.0]: – Distributed computing infrastructures that enable flexible, secure, coordinated resource sharing among dynamic collections of individuals and institutions – Enable communities ( “ Virtual Organizations ” ) to share geographically distributed resources as they pursue common goals, in the absence of: Homogeneity, Central location, Central control, Existing trust relationships • The hype following the Grid: – One of the sources of the impact of scientific and technological changes on the economy and society [Jeremy Rifkin, “The European Dream,” Penguin 2004] – The Grid has been described as the Next Generation Internet, the implementation of the Global Computer etc. 8
  • 9. Grid Infrastructure development ‣ Nowadays, Grid infrastructures comprise an impressive collection of computational and software resources ‣ drawing an increasing number of users from various disciplines 9
  • 10. Data-Intensive Scientific Projects Motivation Grid / Cloud Computing Scientists Resources Traditional Collaboration Tools 10
  • 11. Problem • Collaboration is done externally to scientific software environments (email, web, portals, IM, etc.). • Manual effort for transferring information from one tool to another. • Error prone and time consuming. Lack of a unified, user-friendly software and collaboration environment for scientists. 11
  • 12. Current Solutions Pros • Professional Networking • Minimal Collaboration Functionality General-Purpose Cons OSN • External to existing scientific software environments – Web Based • Do not support resource* sharing Pros • More immersive collaboration environment than Generic OSN. • Resource sharing and ability to run experiments. Scientific OSN Cons • Application Domain Specific. • Proprietary infrastructures – High maintenance. • Introduce additional information sources -> User Information overload 13
  • 13. Our Solution g-Eclipse (www.eclipse.org/geclipse) • Integrated workbench framework • Build on-top of Eclipse (Extensible and community support) • Toolset for users, operators & developers of Grid/Cloud infrastructures (gLite, GRIA, Amazon AWS) – Middleware agnostic • Rich functionality: • Development & Deployment • Benchmarking & Testing • Workflow Programming Online Social Networks • Easy establishment and management of groups • Automatic dissemination of notifications • Professional Networking • High Availability 14
  • 14. g-Eclipse Grid Project View W o r k b e n c h Information View Authentication View JSDL Editor View 15
  • 15. g-Social Build on-top of the g-Eclipse Framework Aims to enable collaboration among scientists that are/will utilize g-Eclipse Features • Social Abstractions (Resources, Meta-data, Authentication). • Definition of structured and standardized social meta-data • Enrich social meta-data with links to project related resources. • Access resources easily . • Share project data and meta-data. • Retrieve shared information. • Seamless interaction with OSN. • Facebook • Twitter • Extensible for other OSNs g-Social Work Cycle 16
  • 16. g-Social Abstractions Enable seamless sharing and retrieval (via an OSN) of all particulars of the research work performed in the context of a real scientific project. Abstract a Scientific Collaborative Environment which utilize Online Social Networks. 17
  • 17. Abstractions - Resources Any file(s) related to the execution of a Grid task specific to a scientific project • Input / Output Dataset • Executable • Source Code • Documentation • Publications • … 18
  • 18. Abstractions – Social Meta-data Descriptive meta-data that provide to the OSN and its users information about purpose and function of each shared particular • Name • Function • Purpose • Version • Tags • License • …. 19
  • 19. Abstractions – Authentication Manager Enforces security and privacy control of users while interacting with the OSN • Authorization / Authentication against an OSN • Monitor life-cycle of authentication tokens 20
  • 20. Abstractions – Resource Manager Resource sharing • Interact with Authentication Manager • Social meta-data • Encapsulate the above in a form acceptable by and OSN Resource Retrieval • Extraction of published meta-data • g-Eclipse Authentication Manager invocation • Resource access via g-Eclipse file system • Resource import in g-Eclipse workspace 21
  • 21. Abstractions – OSN Interface • OSN are by design web-based systems • OSN-gEclipse interface serves as an intermediate between the web- browser and g-Eclipse. • Invoking g-Eclipse when user clicks on an g-Social link inside an OSN. 22
  • 22. g-Social Implementation • The g-Eclipse Grid Project. • A placeholder for the organization of files/information related to the execution of Grid/Cloud tasks • Executables (local file system) • Input / Output dataset (g-Lite, AWS) • Documentation • Publication (IEEE, ACM, Elsevier) • Infrastructure Configurations 23
  • 23. Implementation (Social Meta-Data Editor) • Multi-Page GUI Editor • Easy Insertion of social meta-data • Specify Location of Resources • XML content meta-data • Extend Job Submission Definition Language (JSDL) schema to include social meta-data specification. 24
  • 24. g-Social View Collaborators Search for Shared Jobs OSN Authentication List of Shared Jobs Share Job View Job Details 25
  • 25. Implementation (g-Social View) Authorization • Authenticate / Authorize against OSN • Check auth of the underlying storage infrastructure when linking or retrieving a resource • Manage auth tokens life- cycle 26
  • 26. Implementation (g-Social View) Share Job to OSN • Share job details as defined in meta-data editor • Ask user to which OSN details should be posted • Parse social meta-data • Encapsulate them in OSN specific post formats. 27
  • 27. Implementation (g-Social View) View Share Job Details • Social Meta-data • Name • Description • Version • Resource Handles • Download Resource 28
  • 28. Conclusions & Future Work Conclusions g-Social enhances integrated e-Science Tools (g-Eclipse) with Social Networking functionality. Specifically it: • Enables the definition of social meta-data for sharing and retrieval of information among scientists. • Enriches meta-data with resource handles which might be scattered in heterogeneous storage infrastructures. • Provides mechanisms for sharing and retrieving scientific information with just a few clicks. Future Work • Standardize social meta-data definition • Support additional OSNs • Recommendation System • Release g-Social to Eclipse 29
  • 29. Questions – Contact Information Andriani Stylianou (andriani.stylianou@epfl.ch) Nicholas Loulloudes (loulloudes.n@cs.ucy.ac.cy) Marios D. Dikaiakos (mdd@cs.ucy.ac.cy) http://grid.ucy.ac.cy 30