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How much information?

How much information?. Adapted from a presentation by: Jim Gray Microsoft Research http://research.microsoft.com/~gray Alex Szalay Johns Hopkins University http://tarkus.pha.jhu.edu/~szalay/. How much information is there in the world. Infometrics - the measurement of information

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How much information?

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  1. How much information? Adapted from a presentation by: Jim GrayMicrosoft Research http://research.microsoft.com/~gray Alex Szalay Johns Hopkins University http://tarkus.pha.jhu.edu/~szalay/

  2. How much information is there in the world Infometrics - the measurement of information • What can we store • What do we intend to store. • What is stored. • Why are we interested.

  3. Infinite Storage? • The Terror Bytes are Here • 1 TB costs <100$ to buy • 1 TB costs 300k$/y to own • Management & curation are expensive • Searching without indexing 1TB takes minutes or hours • Petrified by Peta Bytes? • But… people can “afford” them so, – They will be used. • Solution: Automate processes Yotta Zetta Exa Peta Tera Giga Mega Kilo

  4. Digital Information Created, Captured, Replicated Worldwide Exabytes 10-fold Growth in 5 Years! DVD RFID Digital TV MP3 players Digital cameras Camera phones, VoIP Medical imaging, Laptops, Data center applications, Games Satellite images, GPS, ATMs, Scanners Sensors, Digital radio, DLP theaters, Telematics Peer-to-peer, Email, Instant messaging, Videoconferencing, CAD/CAM, Toys, Industrial machines, Security systems, Appliances Source: IDC, 2008

  5. Scale of things to come • Information: • In 2002, recorded media and electronic information flows generated about 22 exabytes (1018) of information • In 2006, the amount of digital information created, captured, and replicated was 161 EB • In 2010, the amount of information added annually to the digital universe will be about 988 EB (almost 1 ZB)

  6. Digital Universe Environmental Footprint In our physical universe, 98.5% of the known mass is invisible, composed of interstellar dust or what scientists call “dark matter.” In the digital universe, we have our own form of dark matter — the tiny signals from sensors and RFID tags and the voice packets that make up less than 6% of the digital universe by gigabyte, but account for more than 99% of the “units,” information “containers,” or “files” in it. Tenfold growth of the digital universe in five years will have a measurable impact on the environment, in terms of both power consumed and electronic waste.

  7. How much information is there? Yotta Zetta Exa Peta Tera Giga Mega Kilo Everything! Recorded • Soon most everything will be recorded and indexed • Most bytes will never be 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 24 Yecto, 21 zepto, 18 atto, 15 femto, 12 pico, 9 nano, 6 micro, 3 milli

  8. Digital Immortality Bell, Gray, CACM, ‘01 Requirements for storing various media for a single person’s lifetime at modest fidelity

  9. What is Digital Immortality? • Preservation and interaction of digitized experiences for individuals and/or groups • Preservation and access • Active interaction with archives through queries and/or an avatar (agents) • Avatar interactions for group experiences • Issues: • Archiving • Indexing • Veracity • Access

  10. EB PB TB Information CensusLesk Varian & Lyman • ~10 Exabytes • ~90% digital • > 55% personal • Print: .003% of bytes5TB/y, but text has lowest entropy • Email is (10 Bmpd) 4PB/y and is 20% text (estimate by Gray) • WWW is ~50TBdeep web ~50 PB • Growth: 50%/y

  11. Internet

  12. First Disk 1956 • IBM 305 RAMAC • 4 MB • 50x24” disks • 1200 rpm • 100 ms access • 35k$/y rent • Included computer & accounting software(tubes not transistors)

  13. 10 years later 30 MB 1.6 meters

  14. Now - Terabytes on your desk Terabyte external drive for $200 - 20 cents a gigabyte. In 5 years, 1 cent/gigabyte, $10 for a terabyte?

  15. Storage capacity beating Moore’s law • Improvements:Capacity 60%/yBandwidth 40%/yAccess time 16%/y • 1000 $/TB today • 100 $/TB in 2007 Moores law 58.70% /year TB growth 112.30% /yearsince 1993 Price decline 50.70% /yearsince 1993 Most (80%) data is personal (not enterprise)This will likely remain true.

  16. Kilo Mega Giga Tera Peta Exa Zetta Yotta Disk Evolution • Capacity:100x in 10 years 1 TB 3.5” drive in 2006 20 GB as 1” micro-drive • System on a chip • High-speed LAN • Disk replacing tape • Disk is super computer!

  17. Disk Storage Cheaper Than Paper • File Cabinet (4 drawer) 250$Cabinet: Paper (24,000 sheets) 250$ Space (2x3 @ 10€/ft2) 180$ Total 700$ 0.03 $/sheet3 pennies per page • Disk: disk (250 GB =) 250$ ASCII: 100 m pages 2e-6 $/sheet(10,000x cheaper)micro-dollar per page Image: 1 m photos 3e-4 $/photo (100x cheaper)milli-dollar per photo • Store everything on diskNote: Disk is 100x to 1000x cheaper than RAM

  18. Why Put Everything in Cyberspace? 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

  19. 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”

  20. Trying to fill a terabyte in a year

  21. Projected Portable Computer for 2006 • 100 Gips processor • 1 GB RAM • 1 TB disk • 1 Gbps network • “Some” of your software finding things is a data mining challenge

  22. The Personal Terabyte(s) (All Your Stuff Online)So you’ve got it – now what do you do with it? • TREASURED (what’s the one thing you would save in a fire?) • Can you find anything? • Can you organize that many objects? • Once you find it will you know what it is? • Once you’ve found it, could you find it again? • Information Science Goal:Have GOOD answers for all these Questions

  23. How Will We Find Anything? • Need Queries, Indexing, Pivoting, Scalability, Backup, Replication,Online update, Set-oriented accessIf 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

  24. 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.

  25. 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

  26. 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

  27. Thesis • Most new information is digital(and old information is being digitized) • An Information Science Grand Challenge: • Capture • Organize • Summarize • Visualize this information • Optimize Human Attention as a resource • Improve information quality

  28. Access!

  29. The Evolution of Science • Observational Science • Scientist gathers data by direct observation • Scientist analyzes data • Analytical Science • Scientist builds analytical model • Makes predictions. • Computational Science • Simulate analytical model • Validate model and makes predictions • Data Exploration Science Data captured by instrumentsOr data generated by simulator • Processed by software • Placed in a database / files • Scientist analyzes database / files

  30. Computational Science Evolves • Historically, Computational Science = simulation. • New emphasis on informatics: • Capturing, • Organizing, • Summarizing, • Analyzing, • Visualizing • Largely driven by observational science, but also needed by simulations. • Too soon to say if comp-X and X-info will unify or compete. BaBar, Stanford P&E Gene Sequencer From http://www.genome.uci.edu/ Space Telescope

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

  32. Smart Data (active databases) • If there is too much data to move around, take the analysis to the data! • Do all data manipulations at database • Build custom procedures and functions in the database • Automatic parallelism guaranteed • Easy to build-in custom functionality • Databases & Procedures being unified • Example temporal and spatial indexing • Pixel processing • Easy to reorganize the data • Multiple views, each optimal for certain types of analyses • Building hierarchical summaries are trivial • Scalable to Petabyte datasets

  33. Data Mining in the Image Domain: Can We Discover New Types of Phenomena Using Automated Pattern Recognition? (Every object detection algorithm has its biases and limitations) – Effective parametrization of source morphologies and environments – Multiscale analysis (Also: in the time/lightcurve domain)

  34. Challenge: Make Data Publication & Access Easy • Augment FTP with data query: Return intelligent data subsets • Make it easy to • Publish: Record structured data • Find: • Find data anywhere in the network • Get the subset you need • Explore datasets interactively • Realistic goal: • Make it as easy as publishing/reading web sites today.

  35. Information Science and Data Generation Trends • What does large amounts of information provide? • New opportunities for search! • New discoveries • Business opportunities? • Research opportunities? • Problems?

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