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How much information?

Explore the overwhelming growth of information worldwide, its storage costs, and strategies for managing data effectively. Learn about the rise of terabyte storage, data summarization, and future technology trends. Discover how data is transforming industries, science, and the way we approach 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 • What can we store. • What is stored. • Why are we interested.

  3. Infinite Storage? • The Terror Bytes are Here • 1 TB costs 1k$ to buy • 1 TB costs 300k$/y to own • Management & curation are expensive • Searching 1TB takes minutes or hours • Petrified by Peta Bytes? • But… people can “afford” them so, – Even though they can never actually be seen in your lifetime • Automate the process Yotta Zetta Exa Peta Tera Giga Mega Kilo We are here

  4. How much information is there? Yotta Zetta Exa Peta Tera Giga Mega Kilo Everything! Recorded • Soon everything can 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

  5. 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)

  6. 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.

  7. 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

  8. Trying to fill a terabyte in a year

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

  10. 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.

  11. 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

  12. Premise: 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, …

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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.

  19. 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

  20. Web Services: The Key? Your program Web Server 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 Web Service soap Data In your address space objectin xml

  21. Emerging technologies • Look at science • High end computation and storage

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