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Surviving The Information Avalanche

Surviving The Information Avalanche. Jim Gray Microsoft Research Talk @ Adobe Developers Conference 26 April 2004 http://research.microsoft.com/~gray/talks. Yotta Zetta Exa Peta Tera Giga Mega Kilo. Outline. Historical trends imply that in 20 years:

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Surviving The Information Avalanche

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  1. SurvivingThe Information Avalanche Jim Gray Microsoft Research Talk @ Adobe Developers Conference 26 April 2004 http://research.microsoft.com/~gray/talks

  2. Yotta Zetta Exa Peta Tera Giga Mega Kilo Outline Historical trends imply that in 20 years: • we can store everything in cyberspace.The personal petabyte. • computers will have natural interfacesspeech recognition/synthesisvision, object recognition beyond OCR Implications • The information avalanche will only get worse. • The user interface will change: less typing, more writing, talking, gesturing, more seeing and hearing • Organizing, summarizing, prioritizinginformation is a key technology. We are here

  3. Things Have Changed 1956 • IBM 305 RAMAC • 10 MB disk • ~1M$ (y2004 $)

  4. 1890-1945 Mechanical Relay 7-year doubling 1945-1985 Tube, transistor,.. 2.3 year doubling 1985-2004 Microprocessor 1.0 year doubling The Next 50 years will see MORE CHANGE ops/s/$ Had Three Growth Curves 1890-1990 Combination of Hans Moravac + Larry Roberts + Gordon Bell WordSize*ops/s/sysprice

  5. Constant Cost or Constant Function? • 100x improvement per decade • Same function 100x cheaper • 100x more function for same price Mainframe SMP Constellation Cluster Constant Price Mini SMP Constellation Workstation Graphics/storage Lower Price – New Category PDA Camera/browser

  6. Growth Comes From NEW Apps • The 10M$ computer of 1980 costs 1k$ today • If we were still doing the same things,IT would be a 0 B$/y industry • NEW things absorb the new capacity

  7. The Surprise-Free Futurein 20 years. • 10,000x more power for same price • Personal supercomputer • Personal petabyte stores • Same function for 10,000x less cost. • Smart dust --the penny PC? • The 10 peta-op computer (for 1,000$).

  8. 10,000x would change things • Human computer interface • Decent computer vision • Decent computer speech recognition • Decent computer speech synthesis • Vast information stores • Ability to search and abstract the stores.

  9. How Good is HCI Today? • Surprisingly good. • Demo of making faces http://research.microsoft.com/research/pubs/view.aspx?pubid=290 • Demo of speech synthesis • Daisy, Hal • Synthetic voice • Speech recognition is improving fast, • Vision getting better • Pen computing finally a reality. • Displays improving fast (compared to last 30 years)

  10. Yotta Zetta Exa Peta Tera Giga Mega Kilo Outline Historical trends imply that in 20 years: • we can store everything in cyberspace.The personal petabyte. • computers will have natural interfacesspeech recognition/synthesisvision, object recognition beyond OCR Implications • The information avalanche will only get worse. • The user interface will change: less typing, more writing, talking, gesturing, more seeing and hearing • Organizing, summarizing, prioritizinginformation is a key technology. We are here

  11. How much information is there? Yotta Zetta Exa Peta Tera Giga Mega Kilo Everything! Recorded • Almost everything is recorded digitally. • Most bytes are never seen by humans. • Data summarization, trend detection anomaly detection are key technologies See Mike Lesk: How much information is there: http://www.lesk.com/mlesk/ksg97/ksg.html See Lyman & Varian: How much information http://www.sims.berkeley.edu/research/projects/how-much-info/ All Books MultiMedia All books (words) .Movie A Photo A Book

  12. And >90% in Cyberspace Because: Low rent min $/byte Shrinks time now or later Shrinks space here or there Automate processing knowbots Point-to-Point OR Broadcast Immediate OR Time Delayed Locate Process Analyze Summarize

  13. MyLifeBits The guinea pig • Gordon Bell is digitizing his life • Has now scanned virtually all: • Books written (and read when possible) • Personal documents (correspondence, memos, email, bills, legal,0…) • Photos • Posters, paintings, photo of things (artifacts, …medals, plaques) • Home movies and videos • CD collection • And, of course, all PC files • Recording: phone, radio, TV, web pages… conversations • Paperless throughout 2002. 12” scanned, 12’ discarded. • Only 30GB Excluding videos • Video is 2+ TB and growing fast

  14. Capture and encoding

  15. I mean everything

  16. 25Kday life ~ Personal Petabyte 1PB Will anyone look at web pages in 2020? Probably new modalities & media will dominate then.

  17. Challenges • Capture: Get the bits in • Organize: Index them • Manage: No worries about loss or space • Curate/ Annotate: atutomate where possible • Privacy: Keep safe from theft. • Summarize: Give thumbnail summaries • Interface: how ask/anticipate questions • Present: show it in understandable ways.

  18. MemexAs We May Think, Vannevar Bush, 1945 “A memex is a device in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility” “yet if the user inserted 5000 pages of material a day it would take him hundreds of years to fill the repository, so that he can be profligate and enter material freely”

  19. Too much storage?Try to fill a terabyte in a year Petabyte volume has to be some form of video.

  20. How Will We Find Anything? • Need Queries, Indexing, Pivoting, Scalability, Backup, Replication,Online update, Set-oriented access • If you don’t use a DBMS, you will implement one! • Simple logical structure: • Blob and link is all that is inherent • Additional properties (facets == extra tables)and methods on those tables (encapsulation) • More than a file system • Unifies data and meta-data SQL ++DBMS

  21. Photos

  22. Searching: the most useful app? • Challenge: What questions for useful results? • Many ways to present answers

  23. Detail view

  24. Resource explorerAncestor (collections), annotations, descendant& preview panes turned on

  25. Synchronized timelines with histogram guide

  26. Value of media depends on annotations • “Its just bits until it is annotated”

  27. System annotations provide base level of value • Date 7/7/2000

  28. Tracking usage – even better • Date 7/7/2000. Opened 30 times, emailed to 10 people (its valued by the user!)

  29. Get the user to say a little something is a big jump • Date 7/7/2000. Opened 30 times, emailed to 10 people. “BARC dim sum intern farewell Lunch”

  30. We took him to lunch at our favorite Dim Sum place to say farewell Dapeng was an intern at BARC for the summer of 2000 At table L-R: Dapeng, Gordon, Tom, Jim, Don, Vicky, Patrick, Jim Getting the user to tell a story is the ultimate in media value • A story is a “layout” in time and space • Most valuable content (by selection, and by being well annotated) • Stories must include links to any media they use (for future navigation/search – “transclusion”). • Cf: MovieMaker; Creative Memories PhotoAlbums

  31. We took him to lunch at our favorite Dim Sum place to say farewell Dapeng was an intern at BARC for the summer of 2000 At table L-R: Dapeng, Gordon, Tom, Jim, Don, Vicky, Patrick, Jim Value of media depends on annotations “Its just bits until it is annotated” • Auto-annotate whenever possible e.g. GPS cameras • Make manual annotation as easy as possible. XP photo capture, voice, photos with voice, etc • Support gang annotation • Make stories easy

  32. 80% of data is personal / individual. But, what about the other 20%? • Business • Wall Mart online: 1PB and growing…. • Paradox: most “transaction” systems < 1 PB. • Have to go to image/data monitoring for big data • Government • Government is the biggest business. • Science • LOTS of data.

  33. CERN Tier 0 Instruments: CERN – LHCPeta Bytes per Year Looking for the Higgs Particle • Sensors: 1000 GB/s (1TB/s ~ 30 EB/y) • Events 75 GB/s • Filtered 5 GB/s • Reduced 0.1 GB/s ~ 2 PB/y • Data pyramid: 100GB : 1TB : 100TB : 1PB : 10PB

  34. Information Avalanche • Both • better observational instruments and • Better simulations are producing a data avalanche • Examples • Turbulence: 100 TB simulation then mine the Information • BaBar: Grows 1TB/day 2/3 simulation Information 1/3 observational Information • CERN: LHC will generate 1GB/s 10 PB/y • VLBA (NRAO) generates 1GB/s today • NCBI: “only ½ TB” but doubling each year, very rich dataset. • Pixar: 100 TB/Movie Image courtesy of C. Meneveau & A. Szalay @ JHU

  35. Q: Where will the Data Come From?A: Sensor Applications • Earth Observation • 15 PB by 2007 • Medical Images & Information + Health Monitoring • Potential 1 GB/patient/y  1 EB/y • Video Monitoring • ~1E8 video cameras @ 1E5 MBps  10TB/s  100 EB/y filtered??? • Airplane Engines • 1 GB sensor data/flight, • 100,000 engine hours/day • 30PB/y • Smart Dust: ?? EB/y http://robotics.eecs.berkeley.edu/~pister/SmartDust/ http://www-bsac.eecs.berkeley.edu/~shollar/macro_motes/macromotes.html

  36. The Big Picture Experiments & Instruments facts • Data ingest • Managing a petabyte • Common schema • How to organize it? • How to reorganize it • How to coexist with others questions facts ? Other Archives facts answers Literature facts Simulations The Big Problems • Query and Vis tools • Support/training • Performance • Execute queries in a minute • Batch query scheduling

  37. FTP - GREP • Download (FTP and GREP) are not adequate • You can GREP 1 MB in a second • You can GREP 1 GB in a minute • You can GREP 1 TB in 2 days • You can GREP 1 PB in 3 years. • Oh!, and 1PB ~3,000 disks • At some point we need indices to limit searchparallel data search and analysis • This is where databases can help • Next generation technique: Data Exploration • Bring the analysis to the data!

  38. The Speed Problem • Many users want to search the whole DBad hoc queries, often combinatorial • Want ~ 1 minute response • Brute force (parallel search): • 1 disk = 50MBps => ~1M disks/PB ~ 300M$/PB • Indices (limit search, do column store) • 1,000x less equipment: 1M$/PB • Pre-compute answer • No one knows how do it for all questions.

  39. 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? • Relax notion of optimal (data is fuzzy, answers are approximate) • Don’t assume infinite computational resources or memory • Combination of statistics & computer science

  40. Analysis and Databases • Much statistical analysis deals with • Creating uniform samples – • data filtering • Assembling relevant subsets • Estimating completeness • censoring bad data • Counting and building histograms • Generating Monte-Carlo subsets • Likelihood calculations • Hypothesis testing • Traditionally these are performed on files • Most of these tasks are much better done inside a database • Move Mohamed to the mountain, not the mountain to Mohamed.

  41. DataGrid Computing • Store exabytes twice (for redundancy) • Access them from anywhere • Implies huge archive/data centers • Supercomputer centers become super data centers • Examples: Google, Yahoo!, Hotmail,BaBar, CERN, Fermilab, SDSC, …

  42. Yotta Zetta Exa Peta Tera Giga Mega Kilo Outline Historical trends imply that in 20 years: • we can store everything in cyberspace.The personal petabyte. • computers will have natural interfacesspeech recognition/synthesisvision, object recognition beyond OCR Implications • The information avalanche will only get worse. • The user interface will change: less typing, more writing, talking, gesturing, more seeing and hearing • Organizing, summarizing, prioritizinginformation is a key technology. We are here

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