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Slide1: 

Tony Hey Corporate Vice President Technical Computing Microsoft Corporation Computer and Information Sciences Life Sciences Multidisciplinary Research Earth Sciences e-Science and Cyberinfrastructure New Materials, Technologies and Processes

Licklider’s Vision: 

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

Physics and the Web: 

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

Beyond the Web?: 

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

What is e-Science?: 

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

The e-Science Vision: 

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

e-Science – Vision and Reality: 

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

Slide8: 

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

The Comb-e-Chem Project: 

The Comb-e-Chem Project National X-Ray Service Data Mining and Analysis Automatic Annotation Combinatorial Chemistry Wet Lab HPC Simulation Video Data Stream Diffractometer Middleware Structures Database

National Crystallographic Service: 

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

Slide20: 

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

Slide21: 

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

Slide22: 

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

Slide23: 

Grid E-Scientists Entire E-Science Cycle Encompassing experimentation, analysis, publication, research, learning 5 Digital Library E-Scientists Graduate Students Undergraduate Students E-Experimentation E-Scientists eBank Project

Support for e-Science: 

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

Six Key Elements for a Global Cyberinfrastructure for e-Science : 

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

The Future: Hybrid Networks: 

The Future: Hybrid Networks 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

Slide27: 

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

Research Prototypes to Production Quality Middleware? : 

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, …

The Web Services ‘Magic Bullet’: 

The Web Services ‘Magic Bullet’

Open Grid Services : 

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

Digital Curation?: 

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

Digital Curation Centre : 

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…..

Slide33: 

Computational Modeling

Technical Computing in Microsoft: 

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

New Science Paradigms: 

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)

Slide36: 

Advanced Computing for Science and Engineering . . .

Key Issues for e-Science : 

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

The LEAD Project: 

The LEAD Project Better predictions for Mesoscale weather

The LEAD Vision: 

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

Composing LEAD Services : 

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

Example LEAD Workflow: 

Example LEAD Workflow

and DiscoveryNet : 

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

An Integrative Analysis Example: 

An Integrative Analysis Example

The Data Deluge: 

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

The Problem for the e-Scientist: 

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

The e-Science Data Chain: 

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

Berlin Declaration 2003: 

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’

NSF ‘Atkins’ Report on Cyberinfrastructure : 

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’

Publishing Data & Analysis Is Changing: 

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

Data Publishing: The Background: 

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

Data Publishing: The issues: 

Data Publishing: The issues Data integration Tying together data from various sources Annotation Adding comments/observations to existing data Becoming a new form of communication Provenance ‘Where did this data come from?’ Exporting/publishing in agreed formats To other programs as well as people Security Specifying/enforcing read/write access to parts of your data

Interoperable Repositories?: 

Interoperable Repositories? Paul Ginsparg’s arXiv at Cornell has demonstrated new model of scientific publishing Electronic version of ‘preprints’ hosted on the Web David Lipman of the NIH National Library of Medicine has developed PubMedCentral as repository for NIH funded research papers Microsoft funded development of ‘portable PMC’ now being deployed in UK and other countries Stevan Harnad’s ‘self-archiving’ EPrints project in Southampton provides a basis for OAI-compliant ‘Institutional Repositories’ Many national initiatives around the world moving towards mandating deposition of ‘full text’ of publicly funded research papers in repositories

Microsoft Strategy for e-Science: 

Microsoft Strategy for e-Science Microsoft intends to work with the scientific and library communities: to define open standard and/or interoperable high-level services, work flows and tools to assist the community in developing open scholarly communication and interoperable repositories

Slide54: 

© 2005 Microsoft Corporation. All rights reserved. This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.

Acknowledgements: 

Acknowledgements With special thanks to Kelvin Droegemeier, Geoffrey Fox, Jeremy Frey, Dennis Gannon, Jim Gray, Yike Guo, Liz Lyon and Beth Plale

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