Tony Hey Corporate Vice President Technical Computing Microsoft Corporation

1. Social Sciences Life Sciences e-Science and Cyberinfrastructure Earth Sciences Computer andInformation Sciences Tony Hey Corporate Vice President Technical Computing Microsoft Corporation New Materials,Technologiesand Processes MultidisciplinaryResearch

2. Licklider’s Vision “Lick had this concept – all of the stuff linked together throughout the world, that you can use a remote computer, get data from a remote computer, or use lots of computers in your job” Larry Roberts – Principal Architect of the ARPANET

3. Physics and the Web • Tim Berners-Lee developed the Web at CERN as a tool for exchanging information between the partners in physics collaborations • The first Web Site in the USA was a link to the SLAC library catalogue • It was the international particle physics community who first embraced the Web • ‘Killer’ application for the Internet • Transformed modern world – academia, business and leisure

4. Beyond the Web? • Scientists developing collaboration technologies that go far beyond the capabilities of the Web • To use remote computing resources • To integrate, federate and analyse information from many disparate, distributed, data resources • To access and control remote experimental equipment • Capability to access, move, manipulate and mine data is the central requirement of these new collaborative science applications • Data held in file or database repositories • Data generated by accelerator or telescopes • Data gathered from mobile sensor networks

5. What is e-Science? ‘e-Science is about global collaboration in key areas of science, and the next generation of infrastructure that will enable it’ John Taylor Director General of Research Councils UK, Office of Science and Technology

6. The e-Science Vision • e-Science is about multidisciplinary science and the technologies to support such distributed, collaborative scientific research • Many areas of science are in danger of being overwhelmed by a ‘data deluge’ from new high-throughput devices, sensor networks, satellite surveys … • Areas such as bioinformatics, genomics, drug design, engineering, healthcare … require collaboration between different domain experts • ‘e-Science’ is a shorthand for a set of technologies to support collaborative networked science

7. e-Science – Vision and Reality Vision • Oceanographic sensors - Project Neptune • Joint US-Canadian proposal Reality • Chemistry – The Comb-e-Chem Project • Annotation, Remote Facilities and e-Publishing

8. http://www.neptune.washington.edu/

9. Undersea Sensor Network Connected & Controllable Over the Internet

10. Data Provenance

11. Persistent Distributed Storage Visual Programming

12. Distributed Computation Interoperability & Legacy Support via Web Services

13. Searching & Visualization Live Documents Reputation & Influence

14. Reproducible Research

15. Collaboration

16. Handwriting

17. Interactive Data Dynamic Documents

18. The Comb-e-Chem Project Automatic Annotation Video Data Stream HPC Simulation Data Mining and Analysis StructuresDatabase Diffractometer Combinatorial Chemistry Wet Lab National X-RayService Middleware

19. National Crystallographic Service Send sample material to NCS service Collaborate in e-Lab experiment and obtain structure Search materials database and predict properties using Grid computations Download full data on materials of interest

20. A digital lab book replacement that chemists were able to use, and liked

21. Monitoring laboratory experiments using a broker delivered over GPRS on a PDA

22. Crystallographic e-Prints Direct Access to Raw Data from scientific papers Raw data sets can be very large - stored at UK National Datastore using SRB software

23. eBank Project Virtual Learning Environment Reprints Peer-Reviewed Journal & Conference Papers Technical Reports LocalWeb Preprints & Metadata Institutional Archive Publisher Holdings Certified Experimental Results & Analyses Data, Metadata & Ontologies Undergraduate Students Digital Library Graduate Students E-Scientists E-Scientists E-Scientists Grid 5 E-Experimentation Entire E-Science CycleEncompassing experimentation, analysis, publication, research, learning

24. Support for e-Science • Cyberinfrastructure and e-Infrastructure • In the US, Europe and Asia there is a common vision for the ‘cyberinfrastructure’ required to support the e-Science revolution • Set of Middleware Services supported on top of high bandwidth academic research networks • Similar to vision of the Grid as a set of services that allows scientists – and industry – to routinely set up ‘Virtual Organizations’ for their research – or business • Many companies emphasize computing cycle aspect of Grids • The ‘Microsoft Grid’ vision is more about data management than about compute clusters

25. Six Key Elements for a Global Cyberinfrastructure for e-Science • High bandwidth Research Networks • Internationally agreed AAA Infrastructure • Development Centers for Open Standard Grid Middleware • Technologies and standards for Data Provenance, Curation and Preservation • Open access to Data and Publications via Interoperable Repositories • Discovery Services and Collaborative Tools

26. Standard packet routed production network for email, Web access, … User-controlled ‘lambda’ connections for e-Science applications requiring high performance end-to-end Quality of Service Similar strategies in North America, Europe and Asia The GLIF consortium The Future: Hybrid Networks

27. Towards an International Grid Infrastructure UK NGS Leeds Manchester Starlight (Chicago) Netherlight (Amsterdam) Oxford RAL SDSC NCSA US TeraGrid PSC UCL UKLight SC05 Local laptops in Seattle and UK All sites connected by production network (not all shown) Computation Steering clients Service Registry Network PoP

28. Research Prototypes to Production Quality Middleware? • Research projects not funded to do testing, configuration and QA required to produce production quality middleware • Common rule of thumb is that 10 times more effort is needed to re-engineer ‘proof of concept’ research software to production quality (Brooks – ‘Mythical Man Month’) • Key issue for realizing a global e-Science Cyberinfrastructure is existence of robust, open standard, interoperable middleware • OMII-UK, OMII-China, NMI, NAREGI, …

29. Company A (J2EE) Web Services Company B (ONE) Company C (.Net) The Web Services ‘Magic Bullet’

30. Open Grid Services • Development of Web Services • Require small set of useful ‘Grid Services’ supported by IT industry • Execution Management, Data Access, Resource Management, Security, Information Management • Research community experimenting with higher level services: Workflow, Transactions, Data Mining, Knowledge Discovery … • Exploit Synergy: Use Commercial Development Environment and Tooling to build robust Grid Services

31. Digital Curation? • In 20 years can guarantee that the operating system and spreadsheet program and the hardware used to store data will not exist • Need research ‘curation’ technologies such as workflow, provenance and preservation • Need to liaise closely with individual research communities, data archives and libraries • The UK has set up the ‘Digital Curation Centre’ in Edinburgh with Glasgow, UKOLN and CCLRC • Attempt to bring together skills of scientists, computer scientists and librarians

32. Digital Curation Centre • Actions needed to maintain and utilise digital data and research results over entire life-cycle • For current and future generations of users • Digital Preservation • Long-run technological/legal accessibility and usability • Data curation in science • Maintenance of body of trusted data to represent current state of knowledge • Research in tools and technologies • Integration, annotation, provenance, metadata, security…..

33. Real-world Data Persistent Distributed Data Workflow, Data Mining& Algorithms Interpretation & Insight ComputationalModeling

34. Technical Computing in Microsoft • Radical Computing • Research in potential breakthrough technologies • Advanced Computing for Science and Engineering • Application of new algorithms, tools and technologies to scientific and engineering problems • High Performance Computing • Application of high performance clusters and database technologies to industrial applications

35. New Science Paradigms • Thousand years ago: Experimental Science - description of natural phenomena • Last few hundred years: Theoretical Science - Newton’s Laws, Maxwell’s Equations … • Last few decades: Computational Science - simulation of complex phenomena • Today: e-Science or Data-centric Science - unify theory, experiment, and simulation - using data exploration and data mining • Data captured by instruments • Data generated by simulations • Processed by software • Scientist analyzes databases/files (With thanks to Jim Gray)

36. TOOLS Workflow, Collaboration, Visualization, Data Mining DATA Acquisition, Storage, Annotation, Provenance, Curation, Preservation CONTENT Scholarly Communication, Institutional Repositories Advanced Computing for Science and Engineering Bioinformatics Energy Science Earth Science Engineering . . .

37. Key Issues for e-Science • Workflows and Tools • The LEAD Project • The DiscoveryNet Project • The Data Chain • From Acquisition to Preservation • Scholarly Communication • Open Access to Data and Publications

38. The LEAD Project Better predictions for Mesoscale weather

39. The LEAD Vision DYNAMIC OBSERVATIONS • Product Generation, • Display, • Dissemination Prediction/Detection PCs to Teraflop Systems • Analysis/Assimilation • Quality Control • Retrieval of Unobserved • Quantities • Creation of Gridded Fields Models and Algorithms Driving Sensors The CS challenge: Build a virtual “eScience” laboratory to support experimentation and education leading to this vision. • End Users • NWS • Private Companies • Students

40. Composing LEAD Services Need to construct workflows that are: • Data Driven • The weather input stream defines the nature of the computation • Persistent and Agile • An agent mines a data stream and notices an “interesting” feature. This event may trigger a workflow scenario that has been waiting for months • Adaptive • The weather changes • Workflow may have to change on-the-fly • Resources

41. Example LEAD Workflow

42. and DiscoveryNet • Constructing a ubiquitous workflow : by scientists • Integrate information resources and applications cross-domain • Capture the best practice of your scientific research • Warehousing workflows: for scientists • Manage discovery processes within an organisation • Construct an enterprise process knowledge bank • Deployment workflow: to scientists • Turn workflows into reusable applications/services • Turn every scientist into a solution builder

43. Relational data mining Relational data mining Decision tree model of metabonomic profile Text mining Text mining Visualizing serial/spectrum data Visualizing cluster statistics Visualizingmultidimensional data Visualizingsequence data Visualizingrelational data clusters Chemical structure visualization Visualizingpathway data Chemical data model Chemical sequence data model Text mining visualization Spectrum data mining Spectrum data mining An Integrative Analysis Example

44. The Data Deluge • In the next 5 years e-Science projects will produce more scientific data than has been collected in the whole of human history • Some normalizations: • The Bible = 5 Megabytes • Annual refereed papers = 1 Terabyte • Library of Congress = 20 Terabytes • Internet Archive (1996 – 2002) = 100 Terabytes • In many fields new high throughput devices, sensors and surveys will be producing Petabytes of scientific data

45. Experiments & Instruments facts questions facts ? Other Archives facts answers Literature facts Simulations The Problem for the e-Scientist • Data ingest • Managing a petabyte • Common schema • How to organize it? • How to reorganize it? • How to coexist & cooperate with others? • Data Query and Visualization tools • Support/training • Performance • Execute queries in a minute • Batch (big) query scheduling

46. The e-Science Data Chain • Data Acquisition • Data Ingest • Metadata • Annotation • Provenance • Data Storage • Curation • Preservation

47. Berlin Declaration 2003 • ‘To promote the Internet as a functional instrument for a global scientific knowledge base and for human reflection’ • Defines open access contributions as including: • ‘original scientific research results, raw data and metadata, source materials, digital representations of pictorial and graphical materials and scholarly multimedia material’

48. NSF ‘Atkins’ Report on Cyberinfrastructure • ‘the primary access to the latest findings in a growing number of fields is through the Web, then through classic preprints and conferences, and lastly through refereed archival papers’ • ‘archives containing hundreds or thousands of terabytes of data will be affordable and necessary for archiving scientific and engineering information’

49. Publishing Data & Analysis Is Changing Roles Authors Publishers Curators Archives Consumers Traditional Scientists Journals Libraries Archives Scientists Emerging Collaborations Project web site Data+Doc Archives Digital Archives Scientists

50. Data Publishing: The Background In some areas – notably biology – databases are replacing (paper) publications as a medium of communication • These databases are built and maintained with a great deal of human effort • They often do not contain source experimental data - sometimes just annotation/metadata • They borrow extensively from, and refer to, other databases • You are now judged by your databases as well as your (paper) publications • Upwards of 1000 (public databases) in genetics