Transcript Information Visualization: Principles, Promise, and

Designing and Evaluating Search
Interfaces
Prof. Marti Hearst
School of Information
UC Berkeley
1
Outline
• Why is Supporting Search Difficult?
• What Works?
• How to Evaluate?
2
Why is Supporting Search Difficult?
•
•
•
•
•
Everything is fair game
Abstractions are difficult to represent
The vocabulary disconnect
Users’ lack of understanding of the technology
Clutter vs. Information
3
Everything is Fair Game
• The scope of what people search for is all
of human knowledge and experience.
– Other interfaces are more constrained
(word processing, formulas, etc)
• Interfaces must accommodate human
differences in:
–
–
–
–
Knowledge / life experience
Cultural background and expectations
Reading / scanning ability and style
Methods of looking for things (pilers vs. filers)
4
Abstractions Are Hard to Represent
• Text describes abstract concepts
– Difficult to show the contents of text in a visual or
compact manner
• Exercise:
– How would you show the preamble of the US Constitution
visually?
– How would you show the contents of Joyce’s Ulysses
visually? How would you distinguish it from Homer’s The
Odyssey or McCourt’s Angela’s Ashes?
• The point: it is difficult to show text without using text
5
Vocabulary Disconnect
– If you ask a set of people to describe a set of things
there is little overlap in the results.
6
The Vocabulary Problem
Data sets examined (and # of participants)
– Main verbs used by typists to describe the kinds of
edits that they do (48)
– Commands for a hypothetical “message decoder”
computer program (100)
– First word used to describe 50 common objects
(337)
– Categories for 64 classified ads (30)
– First keywords for a each of a set of recipes (24)
Furnas, Landauer, Gomez, Dumais: The Vocabulary Problem in Human-System Communication.
7
Commun. ACM 30(11): 964-971 (1987)
The Vocabulary Problem
These are really bad results
– If one person assigns the name, the probability of it
NOT matching with another person’s is about 80%
– What if we pick the most commonly chosen words as
the standard? Still not good:
Furnas, Landauer, Gomez, Dumais: The Vocabulary Problem in Human-System Communication.
8
Commun. ACM 30(11): 964-971 (1987)
Lack of Technical Understanding
• Most people don’t understand the underlying
methods by which search engines work.
9
People Don’t Understand Search Technology
A study of 100 randomly-chosen people found:
– 14% never type a url directly into the address bar
• Several tried to use the address bar, but did it wrong
– Put spaces between words
– Combinations of dots and spaces
– “nursing spectrum.com” “consumer reports.com”
– Several use search form with no spaces
• “plumber’slocal9” “capitalhealthsystem”
– People do not understand the use of quotes
• Only 16% use quotes
• Of these, some use them incorrectly
– Around all of the words, making results too restrictive
– “lactose intolerance –recipies”
» Here the – excludes the recipes
– People don’t make use of “advanced” features
• Only 1 used “find in page”
• Only 2 used Google cache
Hargattai, Classifying and Coding Online Actions, Social Science Computer
Review 22(2), 2004 210-227.
10
People Don’t Understand Search Technology
Without appropriate explanations, most of 14
people had strong misconceptions about:
• ANDing vs ORing of search terms
– Some assumed ANDing search engine indexed a smaller
collection; most had no explanation at all
• For empty results for query “to be or not to be”
– 9 of 14 could not explain in a method that remotely
resembled stop word removal
• For term order variation “boat fire” vs. “fire boat”
– Only 5 out of 14 expected different results
– Understanding was vague, e.g.:
» “Lycos separates the two words and searches for the
meaning, instead of what’re your looking for. Google
understands the meaning of the phrase.”
Muramatsu & Pratt, “Transparent Queries: Investigating Users’
Mental Models of Search Engines, SIGIR 2001.
11
What Works?
12
Cool Doesn’t Cut It
• It’s very difficult to design a search interface
that users prefer over the standard
• Some ideas have a strong WOW factor
– Examples:
• Kartoo
• Groxis
• Hyperbolic tree
– But they don’t pass the “will you use it” test
• Even some simpler ideas fall by the wayside
– Example:
• Visual ranking indicators for results set listings
13
Early Visual Rank Indicators
14
15
16
Metadata Matters
• When used correctly, text to describe text,
images, video, etc. works well
• “Searchers” often turn into “browsers” with
appropriate links
• However, metadata has many perils
– The Kosher Recipe Incident
17
Small Details Matter
• UIs for search especially require great care
in small details
– In part due to the text-heavy nature of search
– A tension between more information and
introducing clutter
• How and where to place things important
– People tend to scan or skim
– Only a small percentage reads instructions
18
Small Details Matter
• UIs for search especially require endless tiny adjustments
– In part due to the text-heavy nature of search
• Example:
– In an earlier version of the Google Spellchecker, people
didn’t always see the suggested correction
• Used a long sentence at the top of the page:
“If you didn’t find what you were looking for …”
• People complained they got results, but not the right results.
• In reality, the spellchecker had suggested an appropriate
correction.
Interview with Marissa Mayer by Mark Hurst:
http://www.goodexperience.com/columns/02/1015google.html
19
Small Details Matter
• The fix:
– Analyzed logs, saw people didn’t see the correction:
• clicked on first search result,
• didn’t find what they were looking for (came right back to the
search page
• scrolled to the bottom of the page, did not find anything
• and then complained directly to Google
– Solution was to repeat the spelling suggestion at the
bottom of the page.
• More adjustments:
– The message is shorter, and different on the top vs. the
bottom
Interview with Marissa Mayer by Mark Hurst:
http://www.goodexperience.com/columns/02/1015google.html
20
21
Small Details Matter
• Layout, font, and whitespace for informationcentric interfaces requires very careful design
• Example:
– Photo thumbnails
– Search results summaries
22
What Works for Search Interfaces?
• Query term highlighting
– in results listings
– in retrieved documents
• Term Suggestions (if done right)
• Sorting of search results according to important criteria
(date, author)
• Grouping of results according to well-organized category
labels (see Flamenco)
• DWIM only if highly accurate:
– Spelling correction/suggestions
– Simple relevance feedback (more-like-this)
– Certain types of term expansion
• So far: not really visualization
Hearst et al: Finding the Flow in Web Site Search, CACM 45(9), 2002.
23
Highlighting Query Terms
• Boldface or color
• Adjacency of terms with relevant context is a
useful cue.
24
25
26
Highlighted query term hits
using Google toolbar
Microso
US
found!
Blackout
found!
PGA
don’t know
Microsoft
don’t know
27
How to Introduce New Features?
• Example: Yahoo “shortcuts”
– Search engines now provide groups of enriched
content
• Automatically infer related information, such as sports
statistics
– Accessed via keywords
• User can quickly specify very specific information
– united 570 (flight arrival time)
– map “san francisco”
• We’re heading back to command languages!
28
29
30
31
32
Introducing New Features
• A general technique: scaffolding
• Scaffolding:
– Facilitate a student’s ability to build on prior
knowledge and internalize new information.
– The activities provided in scaffolding instruction are
just beyond the level of what the learner can do
already.
– Learning the new concept moves the learner up one
“step” on the conceptual “ladder”
33
Scaffolding Example
• The problem: how do people learn about these
fantastic but unknown options?
• Example: scaffolding the definition function
– Where to put a suggestion for a definition?
– Google used to simply hyperlink it next to the
statistics for the word.
– Now a hint appears to alert people to the feature.
34
Unlikely to notice the function here
35
Scaffolding to teach what is available
36
Query Term Suggestions
37
Query Reformulation
• Query reformulation:
– After receiving unsuccessful results, users modify
their initial queries and submit new ones intended to
more accurately reflect their information needs.
• Web search logs show that searchers often
reformulate their queries
– A study of 985 Web user search sessions found
• 33% went beyond the first query
• Of these, ~35% retained the same number of terms
while 19% had 1 more term and 16% had 1 fewer
Use of query reformulation and relevance feedback by Excite users,
38
Spink, Janson & Ozmultu, Internet Research 10(4), 2001
Query Reformulation
• Many studies show that if users engage in
relevance feedback, the results are much
better.
– In one study, participants did 17-34% better with RF
– They also did better if they could see the RF terms
than if the system did it automatically (DWIM)
• But the effort required for doing so is usually a
roadblock.
– Before the web and in most research, searches have
to select MANY relevant documents or MANY terms.
Koenemann & Belkin, A Case for Interaction: A Study of Interactive
Information Retrieval Behavior and Effectiveness, CHI’96
39
Query Reformulation
• What happens when the web search engines
suggests new terms?
• Web log analysis study using the Prisma term
suggestion system:
Anick, Using Terminological Feedback for Web Search Refinement –
A Log-based Study, SIGIR’03.
40
Query Reformulation Study
• Feedback terms were displayed to 15,133 user sessions.
– Of these, 14% used at least one feedback term
– For all sessions, 56% involved some degree of query
refinement
• Within this subset, use of the feedback terms was 25%
– By user id, ~16% of users applied feedback terms at least
once on any given day
• Looking at a 2-week session of feedback users:
– Of the 2,318 users who used it once, 47% used it again in
the same 2-week window.
• Comparison was also done to a baseline group that was
not offered feedback terms.
– Both groups ended up making a page-selection click at the
same rate.
Anick, Using Terminological Feedback for Web Search Refinement –
A Log-based Study, SIGIR’03.
41
Query Reformulation Study
Anick, Using Terminological Feedback for Web Search Refinement –
A Log-based Study, SIGIR’03.
42
Query Reformulation Study
• Other observations
– Users prefer refinements that contain the initial
query terms
– Presentation order does have an influence on term
uptake
Anick, Using Terminological Feedback for Web Search Refinement –
A Log-based Study, SIGIR’03.
43
Prognosis: Query Reformulation
• Researchers have always known it can be helpful, but
the methods proposed for user interaction were too
cumbersome
– Had to select many documents and then do feedback
– Had to select many terms
– Was based on statistical ranking methods which are hard
for people to understand
• RF is promising for web-based searching
– The dominance of AND-based searching makes it easier to
understand the effects of RF
– Automated systems built on the assumption that the user
will only add one term now work reasonably well
– This kind of interface is simple
45
Supporting the Search Process
• We should differentiate among searching:
– The Web
– Personal information
– Large collections of like information
• Different cues useful for each
• Different interfaces needed
• Examples
– The “Stuff I’ve Seen” Project
– The Flamenco Project
46
The “Stuff I’ve Seen” project
• Did intense studies of how people work
• Used the results to design an integrated search
framework
• Did extensive evaluations of alternative designs
– The following slides are modifications of ones supplied
by Sue Dumais, reproduced with permission.
Dumais, Cutrell, Cadiz, Jancke, Sarin and Robbins, Stuff I've Seen:
A system for personal information retrieval and re-use. SIGIR 2003.
47
Searching Over Personal Information
• Many locations, interfaces for finding things
(e.g., web, mail, local files, help, history, notes)
Slide adapted from Sue Dumais.
48
The “Stuff I’ve Seen” project
• Unified index of items touched recently by user
–
–
–
–
All types of information, e.g., files of all types, email, calendar,
contacts, web pages, etc.
Full-text index of content plus metadata attributes (e.g., creation
time, author, title, size)
Automatic and immediate update of index
Rich UI possibilities, since it’s your content
 Search only over things already seen
 Re-use vs. initial discovery
Slide adapted from Sue Dumais.
49
SIS Interface
Slide adapted from Sue Dumais
50
Search With SIS
Slide adapted from Sue Dumais
51
Evaluating SIS
• Internal deployment
– ~1500 downloads
– Users include: program management, test, sales,
development, administrative, executives, etc.
• Research techniques
–
–
–
–
–
Free-form feedback
Questionnaires; Structured interviews
Usage patterns from log data
UI experiments (randomly deploy different versions)
Lab studies for richer UI (e.g., timeline, trends)
• But even here must work with users’ own content
Slide adapted from Sue Dumais
52
SIS Usage Data
Detailed analysis for 234 people, 6 weeks usage
• Personal store characteristics
– 5k – 100k items; index <150 meg
• Query characteristics
– Short queries (1.59 words)
– Few advanced operators or fielded search in query box (7.5%)
– Frequent use of query iteration (48%)
• 50% refined queries involve filters – type, date most common
• 35% refined queries involve changes to query
• 13% refined queries involve re-sort
• Query content
– Importance of people
• 29% of the queries involve people’s names
Slide adapted from Sue Dumais
53
SIS Usage Data, cont’d
– 76% Email
– 14% Web pages
– 10% Files
• Age of items opened
– 7% today
– 22% within the last week
– 46% within the last month
• Ease of finding information
– Easier after SIS for web, email, files
– Non-SIS search decreases for web,
email, files
Log(Freq) = -0.68 * log(DaysSinceSeen) + 2.02
120
100
Frequency
Characteristics of items opened
• File types opened
80
60
40
20
0
0
500
1000
1500
2000
2500
Days Since Item First Seen
6
5
Pre-usage
Post-usage
4
3
2
1
0
Files
Slide adapted from Sue Dumais
Email
Web Pages
54
SIS Usage, cont’d
UI Usage
• Small effects of Top/Side,
Previews
• Sort order
3500
Number of Queries
Issued
– Date by far the most common sort
field, even for people who had
Okapi Rank as default
– Importance of time
– Few searches for “best” match;
many other criteria …
3000
2500
Date
Rank
2000
1500
1000
500
0
Date
Rank
Starting Default Sort Column
Slide adapted from Sue Dumais
55
Web Sites and Collections
A report by Forrester research in 2001 showed
that while 76% of firms rated search as
“extremely important” only 24% consider their
Web site’s search to be “extremely useful”.
Johnson, K., Manning, H., Hagen, P.R., and Dorsey, M. Specialize Your Site's Search. Forrester Research,
(Dec. 2001), Cambridge, MA; www.forrester.com/ER/Research/Report/Summary/0,1338,13322,00
56
There are many ways to do it
wrong
• Examples:
– Melvyl online catalog:
• no way to browse enormous category listings
– Audible.com, BooksOnTape.com, and BrillianceAudio:
• no way to browse a given category and simultaneosly
select unabridged versions
– Amazon.com:
• has finally gotten browsing over multiple kinds of features
working; this is a recent development
• but still restricted on what can be added into the query
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
The Flamenco Project
• Incorporating Faceted Hierarchical Metadata
into Interfaces for Large Collections
• Key Goals:
– Support integrated browsing and keyword search
• Provide an experience of “browsing the shelves”
– Add power and flexibility without introducing
confusion or a feeling of “clutter”
– Allow users to take the path most natural to them
• Method:
– User-centered design, including needs assessment
and many iterations of design and testing
Yee, Swearingen, Li, Hearst, Faceted Metadata for Image
Search and Browsing, Proceedings of CHI 2003.
72
Some Challenges
• Users don’t like new search interfaces.
• How to show lots more information without
overwhelming or confusing?
• Our approach:
– Integrate the search seamlessly into the information
architecture.
– Use proper HCI methodologies.
– Use faceted metadata
73
The Flamenco Interface
• Hierarchical facets
• Chess metaphor
– Opening
– Middle game
– End game
•
•
•
•
Tightly Integrated Search
Expand as well as Refine
Intermediate pages for large categories
For this design, small details *really* matter
74
75
76
77
78
79
80
81
82
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What is Tricky About This?
• It is easy to do it poorly
– Yahoo directory structure
• It is hard to be not overwhelming
– Most users prefer simplicity unless complexity really
makes a difference
• It is hard to “make it flow”
– Can it feel like “browsing the shelves”?
84
Using HCI Methodology
• Identify Target Population
– Architects, city planners
• Needs assessment.
– Interviewed architects and conducted contextual inquiries.
• Lo-fi prototyping.
– Showed paper prototype to 3 professional architects.
• Design / Study Round 1.
– Simple interactive version. Users liked metadata idea.
• Design / Study Round 2:
– Developed 4 different detailed versions; evaluated with 11
architects; results somewhat positive but many problems identified.
Matrix emerged as a good idea.
• Metadata revision.
– Compressed and simplified the metadata hierarchies
85
Using HCI Methodology
• Design / Study Round 3.
– New version based on results of Round 2
– Highly positive user response
• Identified new user population/collection
– Students and scholars of art history
– Fine arts images
• Study Round 4
– Compare the metadata system to a strong,
representative baseline
86
Most Recent Usability Study
• Participants & Collection
– 32 Art History Students
– ~35,000 images from SF Fine Arts Museum
• Study Design
– Within-subjects
• Each participant sees both interfaces
• Balanced in terms of order and tasks
– Participants assess each interface after use
– Afterwards they compare them directly
• Data recorded in behavior logs, server logs, paper-surveys;
one or two experienced testers at each trial.
• Used 9 point Likert scales.
• Session took about 1.5 hours; pay was $15/hour
87
The Baseline System
• Floogle
• Take the best of the existing keyword-based
image search systems
88
Comparison of
Common Image Search Systems
System
Collection
Google
Web
20
No
27
AltaVista
Web
15
No
8
Corbis
Photos
9-36
No
8
Getty
Photos,
Art
12-90
Yes
6
MS Office
Photos,
Clip art
6-100
Yes
N/A
Thinker
Fine arts
images
10
Yes
4
BASELINE
Fine arts
images
40
Yes
N/A
# Results
/page
Categor #
ies?
Familiar
89
sword
sword
90
91
92
93
Evaluation Quandary
• How to assess the success of browsing?
– Timing is usually not a good indicator
– People often spend longer when browsing is going
well.
• Not the case for directed search
– Can look for comprehensiveness and correctness
(precision and recall) …
– … But subjective measures seem to be most
important here.
94
Hypotheses
• We attempted to design tasks to test the
following hypotheses:
– Participants will experience greater search
satisfaction, feel greater confidence in the results,
produce higher recall, and encounter fewer dead
ends using FC over Baseline
– FC will perceived to be more useful and flexible than
Baseline
– Participants will feel more familiar with the contents
of the collection after using FC
– Participants will use FC to create multi-faceted
queries
95
Four Types of Tasks
– Unstructured (3): Search for images of interest
– Structured Task (11-14): Gather materials for an art
history essay on a given topic, e.g.
• Find all woodcuts created in the US
• Choose the decade with the most
• Select one of the artists in this periods and show all of
their woodcuts
• Choose a subject depicted in these works and find
another artist who treated the same subject in a
different way.
– Structured Task (10): compare related images
• Find images by artists from 2 different countries that
depict conflict between groups.
– Unstructured (5): search for images of interest
96
Other Points
• Participants were NOT walked through the
interfaces.
• The wording of Task 2 reflected the metadata;
not the case for Task 3
• Within tasks, queries were not different in
difficulty (t’s<1.7, p >0.05 according to post-task questions)
• Flamenco is and order of magnitude slower than
Floogle on average.
– In task 2 users were allowed 3 more minutes in FC
than in Baseline.
– Time spent in tasks 2 and 3 were significantly longer in
FC (about 2 min more).
97
Results
• Participants felt significantly more confident
they had found all relevant images using FC
(Task 2: t(62)=2.18, p<.05; Task 3: t(62)=2.03, p<.05)
• Participants felt significantly more satisfied
with the results
(Task 2: t(62)=3.78, p<.001; Task 3: t(62)=2.03, p<.05)
• Recall scores:
– Task2a: In Baseline 57% of participants found all
relevant results, in FC 81% found all.
– Task 2b: In Baseline 21% found all relevant, in FC
77% found all.
98
Post-Interface Assessments
All significant at p<.05 except simple and overwhelming
99
Perceived Uses of Interfaces
What is interface useful for?
9.00
7.97
7.91
8.00
7.00
6.64
6.44
6.00
5.47
6.16
5.91
4.91
5.00
Baseline
SHASTA
DENALI
4.00
3.00
2.00
FC
1.00
0.00
Useful for my
coursework
Useful for
exploring an
unfamiliar
collection
Useful for finding Useful for seeing
a particular image relationships b/w
images
100
Post-Test Comparison
Baseline
Which Interface Preferable For:
15
Find images of roses
2
Find all works from a given period
1
Find pictures by 2 artists in same media
FC
16
30
29
Overall Assessment:
More useful for your tasks
Easiest to use
Most flexible
More likely to result in dead ends
Helped you learn more
Overall preference
4
28
8
23
6
24
28
3
1
31
2
29
101
Facet Usage
• Facets driven largely by task content
– Multiple facets 45% of time in structured tasks
• For unstructured tasks,
–
–
–
–
–
Artists (17%)
Date (15%)
Location (15%)
Others ranged from 5-12%
Multiple facets 19% of time
• From end game, expansion from
– Artists (39%)
– Media (29%)
– Shapes (19%)
102
Qualitative Observations
• Baseline:
– Simplicity, similarity to Google a plus
– Also noted the usefulness of the category links
• FC:
– Starting page “well-organized”, gave “ideas for what to
search for”
– Query previews were commented on explicitly by 9
participants
– Commented on matrix prompting where to go next
• 3 were confused about what the matrix shows
– Generally liked the grouping and organizing
– End game links seemed useful; 9 explicitly remarked
positively on the guidance provided there.
– Often get requests to use the system in future
103
Study Results Summary
• Overwhelmingly positive results for the
faceted metadata interface.
• Somewhat heavy use of multiple facets.
• Strong preference over the current state of
the art.
• This result not seen in similarity-based image
search interfaces.
• Hypotheses are supported.
104
Summary
• Usability studies done on 3 collections:
– Recipes: 13,000 items
– Architecture Images: 40,000 items
– Fine Arts Images: 35,000 items
• Conclusions:
– Users like and are successful with the dynamic
faceted hierarchical metadata, especially for
browsing tasks
– Very positive results, in contrast with studies on
earlier iterations
– Note: it seems you have to care about the contents
of the collection to like the interface
105
Using DWIM
• DWIM – Do What I Mean
– Refers to systems that try to be “smart” by guessing users’
unstated intentions or desires
• Examples:
– Automatically augment my query with related terms
– Automatically suggest spelling corrections
– Automatically load web pages that might be relevant to
the one I’m looking at
– Automatically file my incoming email into folders
– Pop up a paperclip that tells me what kind of help I need.
• THE CRITICAL POINT:
– Users love DWIM when it really works
– Users DESPISE it when it doesn’t
• unless not very intrusive
106
DWIM that Works
• Amazon’s “customers who bought X also bought Y”
– And many other recommendation-related features
107
DWIM Example:
Spelling Correction/Suggestion
• Google’s spelling suggestions are highly
accurate
• But this wasn’t always the case.
– Google introduced a version that wasn’t very
accurate. People hated it. They pulled it. (According
to a talk by Marissa Mayer of Google.)
– Later they introduced a version that worked well. People
love it.
• But don’t get too pushy.
– For a while if the user got very few results, the page was
automatically replaced with the results of the spelling
correction
– This was removed, presumably due to negative responses
Information from a talk by Marissa Mayer of Google
108
What We’ve Covered
• Introduction
– Why is designing for search difficult?
• How to Design for Search
–
–
–
–
–
HCI and iterative design
What works?
Small details matter
Scaffolding
The Role of DWIM
• Core Problems
– Query specification and refinement
– Browsing and searching collections
109
Final Words
• User interfaces for search remains a
fascinating and challenging field
• Search has taken a primary role in the web
and internet business
• Thus, we can continue to expect fascinating
developments, and maybe some
breakthroughs, in the next few years!
110
Thank you!
Marti Hearst
http://www.ischool.berkeley.edu/~hearst
111
References
Anick, Using Terminological Feedback for Web Search Refinement –A Log-based Study,
SIGIR’03.
Bates, The Berry-Picking Search: UI Design, in “User Interface Design”, Thimbley (ED),
Addison-Wesley 1990
Chen, Houston, Sewell, and Schatz, JASIS 49(7)
Chen and Yu, Empirical studies of information visualization: a meta-analysis, IJHCS
53(5),2000
Dumais, Cutrell, Cadiz, Jancke, Sarin and Robbins, Stuff I've Seen: A system for personal
information retrieval and re-use. SIGIR 2003.
Furnas, Landauer, Gomez, Dumais: The Vocabulary Problem in Human-System
Communication. Commun. ACM 30(11): 964-971 (1987)
Hargattai, Classifying and Coding Online Actions, Social Science Computer Review 22(2),
2004 210-227.
Hearst, English, Sinha, Swearingen, Yee. Finding the Flow in Web Site Search, CACM
45(9), 2002.
Hearst, User Interfaces and Visualization, Chapter 10 of Modern Information Retrieval,
Baeza-Yates and Rebeiro-Nato (Eds), Addison-Wesley 1999.
Johnson, Manning, Hagen, and Dorsey. Specialize Your Site's Search. Forrester
Research, (Dec. 2001), Cambridge, MA
112
References
Koenemann & Belkin, A Case for Interaction: A Study of Interactive Information
Retrieval Behavior and Effectiveness, CHI’96
Marissa Mayer Interview by Mark Hurst:
http://www.goodexperience.com/columns/02/1015google.html
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