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 What can we store. What is stored. Why are we interested.

Infinite Storage? The Terror Bytes are Here Petrified by Peta Bytes? 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

How much information is there? Yotta Zetta Exa Peta Tera Giga Mega Kilo 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/ Everything! Recorded 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

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)

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

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 $/sheet 3 pennies per page Disk: disk (250 GB =) 250$ 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 disk Note: Disk is 100x to 1000x cheaper than RAM

Trying to fill a terabyte in a year Item Items/TB Items/day 300 KB JPEG 3 M 9,800 1 MB Doc 1 M 2,900 1 hour 256 kb/s MP3 audio 9 K 26 1 hour 1.5 Mbp/s MPEG video 290 0.8 Bottom line: we will be able to keep LOTS of video, and vast amounts of smaller data types (audio, photos, documents). Note: probably not worth the time to delete an object

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

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.

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

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, …

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

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 instruments Or data generated by simulator Processed by software Placed in a database / files Scientist analyzes database / files

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

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

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

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.

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

Web Services: The Key? Internet-scale distributed computing 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 Your program Web Server http Web page Your program Web Service soap Data In your address space object in xml

Emerging technologies Look at science High end computation and storage