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How much information?
Adapted from a presentation by:Jim Gray
Microsoft Researchhttp://research.microsoft.com/~gray
Alex SzalayJohns Hopkins University
http://tarkus.pha.jhu.edu/~szalay/
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.
Infinite Storage?
Yotta
Zetta
Exa
Peta
Tera
Giga
Mega
Kilo
• 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
Digital Information Created, Captured, Replicated Worldwide
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2006 2007 2008 2009 2010 2011
Exabytes
10-fold Growth in 5
Years!
DVDRFID
Digital TVMP3 players
Digital camerasCamera phones, VoIP
Medical imaging, Laptops,Data center applications, Games
Satellite images, GPS, ATMs, ScannersSensors, Digital radio, DLP theaters, Telematics
Peer-to-peer, Email, Instant messaging, Videoconferencing,CAD/CAM, Toys, Industrial machines, Security systems, Appliances
Source: IDC, 2008
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)
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.
How much information is there?• 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 informationhttp://www.sims.berkeley.edu/research/projects/how-much-info/
Yotta
Zetta
Exa
Peta
Tera
Giga
Mega
KiloA BookA Book
.Movie
All books(words)
All Books MultiMedia
Everything!
Recorded
A PhotoA Photo
24 Yecto, 21 zepto, 18 atto, 15 femto, 12 pico, 9 nano, 6 micro, 3 milli
Digital ImmortalityRequirements for storing various media for a single
person’s lifetime at modest fidelity
Bell, Gray, CACM, ‘01
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
PB
EB
TB
Media TB/y Growth Rate, %
optical 50 70
paper 100 2
film 100,000 4
magnetic 1,000,000 55
total 1,100,150 50
• ~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
Information CensusLesk Varian & Lyman
Internet
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)
10 years later1.
6 m
eter
s 30 MB
Terabyte external drive for$200 - 20 cents a gigabyte.
In 5 years, 1 cent/gigabyte, $10 for a terabyte?
Now - Terabytes on your desk
1E+3
1E+4
1E+5
1E+6
1E+7
1988 1991 1994 1997 2000
disk TB growth: 112%/y
Moore's Law: 58.7%/y
ExaByte
Disk TB Shipped per Year1998 Disk Trend (Jim Porter)
http://www.disktrend.com/pdf/portrpkg.pdf.Storage capacity beating Moore’s law
• Improvements:Capacity 60%/yBandwidth 40%/yAccess time 16%/y
• 1000 $/TB today• 100 $/TB in 2007
Moores law 58.70% /year
TB growth 112.30% /year since 1993
Price decline 50.70% /year since 1993
Most (80%) data is personal (not enterprise)This will likely remain true.
Disk Evolution• Capacity:100x in 10 years
1 TB 3.5” drive in 2006 20 GB as 1” micro-drive
• System on a chip • High-speed LAN
• Disk replacing tape• Disk is super computer!
Kilo
Mega
Giga
Tera
Peta
Exa
Zetta
Yotta
Disk Storage Cheaper Than Paper• File Cabinet (4 drawer) 250$
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 pageImage: 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
Why Put Everything in Cyberspace?
Low rentmin $/byte
Shrinks timenow or later
Shrinks spacehere or there
Automate processingknowbots
Point-to-Point OR Broadcast
Imm
edia
te O
R T
ime
Del
ayed
LocateProcessAnalyzeSummarize
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”
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
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
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
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 ++SQL ++DBMSDBMS
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
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
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
Access!
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
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
Space Telescope
P&E Gene SequencerFromhttp://www.genome.uci.edu/
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
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)
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.
Information Science and Data Generation Trends
• What does large amounts of information provide?– New opportunities for search!– New discoveries
• Business opportunities?
• Research opportunities?
• Problems?