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

Surviving
The Information Avalanche
Jim Gray
Microsoft Research
Talk @ Adobe Developers Conference
26 April 2004
http://research.microsoft.com/~gray/talks
Outline
Historical trends imply that in 20 years:
1. we can store everything in cyberspace.
The personal petabyte.
2. computers will have natural interfaces
speech recognition/synthesis
vision, object recognition beyond OCR
Implications
1. The information avalanche will only get
worse.
2. The user interface will change:
less typing,
more writing, talking, gesturing,
more seeing and hearing
3. Organizing, summarizing, prioritizing
information is a key technology.
Yotta
Zetta
Exa
Peta
We are here
Tera
Giga
Mega
Kilo
Things Have Changed
1956
• IBM 305 RAMAC
• 10 MB disk
• ~1M$ (y2004 $)
The Next 50 years will see MORE CHANGE
ops/s/$ Had Three Growth Curves 1890-1990
1890-1945
Mechanical
Relay
7-year doubling
1945-1985
Tube, transistor,..
2.3 year doubling
1985-2004
Microprocessor
1.0 year doubling
Combination of Hans Moravac + Larry Roberts + Gordon Bell
WordSize*ops/s/sysprice
1.E+09
ops per second/$
doubles every
1.0 years
1.E+06
1.E+03
1.E+00
1.E-03
doubles every
7.5 years
doubles every
2.3 years
1.E-06
1880
1900
1920
1940
1960
1980
2000
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
SMP
Constellation
Graphics/storage
Camera/browser
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$/y industry
• NEW things absorb the new capacity
The Surprise-Free Future
in 20 years.
• 10,000x more power for same price
– Personal supercomputer
– Personal petabyte stores
• Same function for 10,000x less cost.
– Smart dust --the penny PC?
– The 10 peta-op computer (for 1,000$).
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.
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)
Outline
Historical trends imply that in 20 years:
1. we can store everything in cyberspace.
The personal petabyte.
2. computers will have natural interfaces
speech recognition/synthesis
vision, object recognition beyond OCR
Implications
1. The information avalanche will only get
worse.
2. The user interface will change:
less typing,
more writing, talking, gesturing,
more seeing and hearing
3. Organizing, summarizing, prioritizing
information is a key technology.
Yotta
Zetta
Exa
Peta
We are here
Tera
Giga
Mega
Kilo
How much information is there?
Yotta
• 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:
Everything
!
Recorded
All Books
MultiMedia
Zetta
Exa
Peta
All books
(words)
.Movi
e
Tera
Giga
http://www.lesk.com/mlesk/ksg97/ksg.html
See Lyman & Varian:
How much information
http://www.sims.berkeley.edu/research/projects/how-much-info/
A Photo
A Book
Mega
Kilo
Low rent
min $/byte
Shrinks time
now or later
Shrinks space
here or there
Automate processing
knowbots
Immediate OR Time Delayed
And >90% in Cyberspace Because:
Point-to-Point
OR
Broadcast
Locate
Process
Analyze
Summarize
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
Capture and encoding
I mean everything
25Kday life ~ Personal Petabyte
Lifetime Storage
1PB
1000.
100.
10.
TB
1.
0.1
0.01
0.001
Msgs
web
pages
Tifs
Books
jpegs
1KBps
sound
music
Videos
Will anyone look at web pages in 2020?
Probably new modalities & media will dominate then.
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.
Memex
As 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”
Too much storage?
Try to fill a terabyte in a year
Item
Items/TB
Items/day
300 KB JPEG
3M
9,800
1 MB Doc
1M
2,900
1 hour 256 kb/s
MP3 audio
1 hour 1.5 Mbp/s
MPEG video
9K
26
290
0.8
Petabyte volume has to be some form of video.
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
Photos
Searching: the most useful app?
• Challenge: What questions for useful results?
• Many ways to present answers
•
Detail view
Resource explorer
Ancestor (collections), annotations, descendant
& preview panes turned on
Synchronized timelines with
histogram guide
Value of media depends on
annotations
• “Its just bits until it is annotated”
System annotations provide base
level of value
• Date 7/7/2000
Tracking usage – even better
• Date 7/7/2000. Opened 30 times, emailed to 10
people (its valued by the user!)
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”
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
Dapeng was an
intern at BARC
for the summer
of 2000
We took him to
lunch at our
favorite Dim Sum
place to say
farewell
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
Dapeng was
an intern at
BARC for the
summer of
2000
We took
him to
lunch at our
favorite
Dim Sum
place to say
farewell
At table L-R: Dapeng, Gordon, Tom,
Jim, Don, Vicky, Patrick, Jim
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.
Instruments: CERN – LHC
Peta 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
CERN Tier 0
Information Avalanche
• Both
– better observational instruments and
– Better simulations
are producing a data avalanche
• Examples
Image courtesy of C. Meneveau & A. Szalay @ JHU
– 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
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
The Big Picture
Experiments &
Instruments
Other Archives
Literature
questions
facts
facts
?
answers
Simulations
The Big Problems
•
•
•
•
•
•
Data ingest
Managing a petabyte
Common schema
How to organize it?
How to reorganize it
How to coexist with others
• Query and Vis tools
• Support/training
• Performance
– Execute queries in a minute
– Batch query scheduling
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 search
parallel data search and analysis
• This is where databases can help
• Next generation technique: Data Exploration
– Bring the analysis to the data!
The Speed Problem
• Many users want to search the whole DB
ad 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.
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
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.
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, …
Outline
Historical trends imply that in 20 years:
1. we can store everything in cyberspace.
The personal petabyte.
2. computers will have natural interfaces
speech recognition/synthesis
vision, object recognition beyond OCR
Implications
1. The information avalanche will only get
worse.
2. The user interface will change:
less typing,
more writing, talking, gesturing,
more seeing and hearing
3. Organizing, summarizing, prioritizing
information is a key technology.
Yotta
Zetta
Exa
Peta
We are here
Tera
Giga
Mega
Kilo