Managing Innovation: How Microsoft Research Works

2. Managing Innovation:How Microsoft Research Works Jim Gray Distinguished Engineer Microsoft Corporation

3. Actionable Ideas • Co-lo if possible • Adopt a “university model” • Recruit from the top • Recruit for passion and a desire to have impact • Install a Research Program Management organization to orchestrate tech-transfer • Institute an annual TechFest

4. InnovationBuild versus Buy versus Invest • Build: Have in-house research • Bell Labs, IBM, GM, Pfizer, Merc, Microsoft… • Buy: Acquire startups or whole companies • IBM, Cisco, Intel, Microsoft, Pfizer, Merc… • Invest: All boats rise • Government research funding • IBM, Cisco, Intel, Microsoft, Pfizer, Merc… • All 3 approaches valid • Complement one another

5. other 2% Intel Microsoft IBM Oracle Product Product Product 18% 19% Gross 26% Gross Product Gross 38% 36% 31% Gross 40% 50% S G&A S G&A 16% 27% S G&A S G&A R&D R&D R&D R&D 26% 23% 12% 6% 15% 15% Cisco DELL Accenture HP other EDS 7% Gross Gross other Gross 8% 18% Gross Gross S G&A Product 14% R&D 26% 1% 27% 32% 9% Product 33% Product 47% S G&A 44% 8% R&D Product 73% 6% R&D Product S G&A 16% S G&A 69% R&D R&D S G&A 25% 21% 0% 0% 16% Companies Are Different • Selected IT company FY02 R&D budgets: • Notice that R&D is correlated with margin • IBM and HP have large service revenues So, their “real” R&D investment rate is higher • Dell, Accenture, EDS have modest R&D – innovate in other ways

6. Most R&D Is DHow to Do Basic Research in Industry?Critical questions (from Rick Rashid) • How can Icreate and maintain a world class research organization in an industrial setting? • How do I keep the lines of communication open between product teams and researchers? • How do Iget new technology into products quickly?

7. ApproachAdapt the Academic Model • Organizational goal: Advance state of the art • University organizational model • Flat structure, critical mass groups • Open research environment • Aggressive publication in peer-reviewed literature • Frequent visitors, daily seminars • Strong ties to University Research • Nearly 15% of basic research budget directly invested in Universities • Lab grants, research grants, fellowships, etc. • Hundreds of interns and visitors

8. Microsoft Research Today • Founded in 1991 • Staff of over 700 in over 55 areas • Internationally recognized research teams • Research lab locations : • Redmond, Washington, 75% • San Francisco, California 1% • Cambridge, United Kingdom 10% • Beijing, People’s Republic of China 10% • Mountain View, California 5%

9. Microsoft ResearchExpanding the State of the Art • Thousands of peer-reviewed publications • 10%…30% of papers at our focus conferencesgraphics, programming, systems, data management… • Community leadership • Professional societies • Journals • Conferences • Mentoring Interns • Hosting academic summers and sabbaticals • Special workshops

10. How To Build A Group • Identify a promising area • Hire the leader (internal or external) • Support her/him • Build team around senior researcher • Look for people who • Want to have impact • Have passion for their ideas • Same template works for whole labs • Cambridge, Beijing, Silicon Valley

11. Keeping Open The Lines Of Communication To Product Teams • Co-location helps: 75% “on campus” • “How can I help?” attitude demonstrates willingness to “get dirty” to help product succeed • Product group spin-offs build strong tiesOver time a number of product groups evolved from research (e.g., Windows Media) • Researchers involved in all corporate product reviews

12. MSR Relationship To MS Products • Virtually every research group actively engaged with product groups • E.G., Windows, Office, streaming media, SQL, Exchange, IIS, commerce server, visual studio, office, consumer products, MSN, etc. • Tech transfer: • Ideas • Code • People • Contacts • Recruiting

13. Focused Technology Transfer Quickly getting technology into products • Program management team with sole focus on tech transfer • Researchers on product “advisory” boards • “Mind-swaps” – joint product/research off-sites • Joint product/research teams, e.g., • ClearType (Windows XP) • Datamining (SQL 2000) • Natural Language & Speech (Office) • TabletPC • Smart Personal Objects (SPOT) • Encourage and recognize contributions

14. MSR Techfest • Internal open house for Microsoft Research • Annual event since 2001 • ~ 7000 attendees • 170 demos, 26 lectures • “Research in progress” • Breadboard demos • This is research idea/prototype • Great networking event: • Breaks down barriers • Serendipitous connections.

15. Examples Of Technology Transfer • Critical support technologies • Memory Optimization Technology enabled sim-ship of Win95/Office95 • Automated bug detection in Windows 2000 • Key technologies that drive products • E.G., MS audio 4.0, ClearType, intelligent search, collaborative filtering, Intellimirror, etc. • Incubated major products • Windows streaming media • Windows CE, TabletPC, eBook • Ecommerce, Datamining • Natural language and speech technologies, etc.

16. MSR Mission Statement • Expand the state of the art in each of the areas in which we do research • Rapidly transfer innovative technologies into Microsoft products • Ensure that Microsoft products have a future

17. Personal Examples of R&D • Scaleable Servers • TerraServer • SkyServer • Databases • Data Cube, Snapshot Isolation • SQL Stress testing • Reliable Multicast • Personal Media Management

18. http://terraservice.net A .NET web service OpenGIS Place Search TerraServer Map Server Landmarks & annotations layered on imagery Used by thousands of real apps today Shows Web Services Performance http://terraserver-usa.com USGS Photo and Topo maps 16TB of data Online since 1997 7 billon pages served120 TB served Shows Scalability Availability Manageability SQL + Windows TerraServer & TerraService TerraServer TerraService

19. TerraServer Today

20. KVM / IP TerraServer Tomorrow • Mirrored System versus SAN • 3 mirrored DB servers + spare versus 4 DB servers • Commodity versus Enterprise • White box Dual Xeon versus 8-way branded • DAS 250GB SATA versus FC-SAN 73GB SCSI • No Tape versus LTO Tape Robot • $0.1M versus $1.8M • Geoplex: 2 sites • You can afford 2!

21. World Wide Telescopehttp://www.voforum.org/ • Premise: Most Astro data is online • So, the Internet isthe world’s best telescope: • Has data on every part of the sky • In every measured spectral band • As deep as the best instruments • It is up when you are up;the “seeing” is always great(no working at night, no clouds no moons no…) • It’s a smart telescope: • links objects and data to literature on them

22. Next-Generation Data Analysis • Looking for • Needles in haystacks – the Higgs particle • Haystacks: Dark matter, Dark energy • Needles are easier than haystacks • Global statistics have poor scaling • Correlation functions are N2,likelihood techniques N3 • As data and computers grow at same rate, we can only keep up with N logN • A way out? • data is fuzzy, answers are approximate • Requires combination of statistics and computer science

23. Data Federations Of Web Services • Massive datasets live near their owners: • Near the instrument’s software pipeline • Near the applications • Near data knowledge and curation • Super Computer centers become Super Data Centers • Each Archive publishes a web service • Schema: documents the data • Methods on objects (queries) • Scientists get “personalized” extracts • Uniform access to multiple Archives • A common global schema • Challenge: • What is the object model for your science? Federation

24. Web Service Web Service Web Services – The Key? Your program http • Web SERVER: • Given a url + parameters • Returns a web page (often dynamic) • Web SERVICE: • Given a XML document (soap msg) • Returns an XML document • Tools make this look like an RPC. • F(x,y,z) returns (u, v, w) • Distributed objects for the web. • + naming, discovery, security,.. • Internet-scale distributed computing Web page Your program soap Data In your address space objectin xml

25. Federating Astronomy Archives IRAS 25m • Great Test for data mining algorithms • It is real and well documented data • High-dimensional data (with confidence intervals) • Spatial data • Temporal data • Many different instruments from many different places and many different times • Federation is a goal • There is a lot of it (petabytes) • Can share cross company • University researchers 2MASS 2m DSS Optical IRAS 100m WENSS 92cm NVSS 20cm ROSAT ~keV GB 6cm

26. SkyServer – One such archiveSkyServer.SDSS.org • Sloan Digital Sky Survey Pixels + Data Mining • 400 attributes per “object” • Spectrograms for 1% • Demo: pixel space record space set space teaching

27. SkyQuery: Federating Archiveshttp://skyquery.net/ • Distributed Query tool using a set of web services • Federates ten astronomy archives from Pasadena, Chicago, Baltimore, Cambridge (England) • Implemented in C# and .NET • Allows queries like: SELECT o.objId, o.r, o.type, t.objId FROM SDSS:PhotoPrimary o, TWOMASS:PhotoPrimary t WHERE XMATCH(o,t)<3.5 AND AREA(181.3,-0.76,6.5) AND o.type=3 and (o.I - t.m_j)>2

28. Each SkyNode publishes Schema Web Service Database Web Service Portal Plans Query (2 phase) Integrates answers Is itself a web service ImageCutout SDSS INT FIRST SkyQuery Structure SkyQuery Portal 2MASS

29. DatabasesTheory to practice • Data Cube • Wrote paper • SQL Server product and ISO Standard adopted idea • Snapshot Isolation • Paper in 1996 • Product in 2004 old Reader version new

30. DatabasesStress Test SqlServer • Generate millions of random SQL queries • Send them to 4 different products • Compare the answers: • If all agree, good! • If not, a bug somewhere • Found many bugs in DB products • Much appreciated by MS DB group • Tool cloned by other DB vendors DB2 = Oracle Informix

31. SQL Automated Test Example Four SQL systems on 2,000 statements Case W X Y Z 1672 1672 1672 1672 232 234 241 31 1 1 1 1 31 15 12 28 1 12 5 116 0 29 32 4 18 18 19 25 45 19 18 113 All fouragree 84% W,X, and Y agree 95% Problem with intermediate table. Error

32. PGMPretty Good Multicast • Reliable multicast protocol • Scales using hierarchy, suppression, and FEC “on-demand” (FEC on-demand is our contribution) • Joint work with Cisco and others • IETF standard • Implemented prototype (Multicast PowerPoint) • Shipped in Windows XP

33. MyLifeBits • “A lifetime store of everything” • The experiment: • digitizing Gordon Bell’s life • The software: • Based on SQL server • Tools to capture web pages, IM chats, TV, radio & telephone • Reports, links, full text search, pivot by time or any other attribute

34. MyLifeBits Software Radio capture tool Telephone capture tool PocketPC transfer tool PocketRadio player TV capture tool MyLifeBits store Internet Radio EPG tool TV EPG download tool MAPI interface Legacy email client Browser tool database files Legacy applications MyLifeBits Shell Voice annotation tool Text annotation tool

35. Research Failures • Not everything is a success • We had technology transfer failures • We had projects with little impact • Success and Failure depend on environment • Even if you have a GREAT! idea • There are many exogenous factors in technology transfer • And, sometimes the idea or focus is wrong • Allow people to fail once or twice.

36. SummaryActionable Ideas • Co-lo if possible • Adopt a “university model” • Recruit from the top • Recruit for passion and a desire to have impact • Install a Research Program Management organization to orchestrate tech-transfer • Institute an annual TechFest

37. © 2003 Microsoft Corporation. All rights reserved. This presentation is for informational purposes only. Microsoft makes no warranties, express or implied, in this summary.