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

PhD, Computer Science at University of Nicosia (UNic)
Oct. 9, 2012

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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
  6. Drift modeling 6
  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 ( • 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 ( Nicholas Loulloudes ( Marios D. Dikaiakos ( 30