Download Web Log, Text, and Other Data Mining

Web Log, Text, and Other
Data Mining
Wayne Kao
What is Data Mining?
• “Automated extraction of hidden
predictive information from large
databases” -Kurt Thearling
• “Quickly and thoroughly explore
mountains of data, isolating the
valuable, usable information -- the
business intelligence” -SPSS site
Possible Questions (Chi)
• Usage
– How has info been accessed? How
frequently? What’s popular?
– How do people enter the site? Where do
people spend time? How long do they
spend there?
– How do people travel within a site? What
are the [un]popular paths?
– Who are the people accessing the site?
From what geographical location? From
what domains?
Possible Questions (cont)
• Structural
– What information has been added?
Modified? Remained the same but moved?
• Usage + Structural
– How is new info accessed? When does it
become popular?
– How does introducing new information
change navigation patterns? Can people
still navigate there to the desired info?
– Do people look for deleted information?
Usability Testing
Common usability testing techniques:
•
•
•
•
Design
Interviews
Ethnographic and/or lab-style observations
Surveys
Focus groups
Good qualitative data
Evaluate
Problems with these techniques:
Prototype
• Time and effort are costly
• Small sample sizes – quantitative results? (Spool)
How can we get usability testing more involved in the
design cycles, so we can find problems and potential
problems earlier?
Remote Usability (Waterson)
• Analyze clickstreams in the context of the
task and user intentions
• Human observers not present
• Want methods that are
– Easy to deploy on any website
– Compatible with range of OS and browsers
• Mobile computing adds further usability
challenges
– Small screen sizes
– Limited and/or new interaction techniques
– Devices are used in environments beyond
the desktop
Apache Web Log
205.188.209.10 - - [29/Mar/2002:03:58:06 -0800] "GET
/~sophal/whole5.gif HTTP/1.0" 200 9609
"http://www.csua.berkeley.edu/~sophal/whole.html" "Mozilla/4.0
(compatible; MSIE 5.0; AOL 6.0; Windows 98; DigExt)"
216.35.116.26 - - [29/Mar/2002:03:59:40 -0800] "GET
/~alexlam/resume.html HTTP/1.0" 200 2674 "-" "Mozilla/5.0
(Slurp/cat; [email protected];
http://www.inktomi.com/slurp.html)“
202.155.20.142 - - [29/Mar/2002:03:00:14 -0800] "GET
/~tahir/indextop.html HTTP/1.1" 200 3510
"http://www.csua.berkeley.edu/~tahir/" "Mozilla/4.0 (compatible;
MSIE 6.0; Windows NT 5.1)“
202.155.20.142 - - [29/Mar/2002:03:00:14 -0800] "GET
/~tahir/animate.js HTTP/1.1" 200 14261
"http://www.csua.berkeley.edu/~tahir/indextop.html" "Mozilla/4.0
(compatible; MSIE 6.0; Windows NT 5.1)“
Analog - One traditional tool
• Reports number of requests, info about
client machines, entry/exit points,
charts (Chi et al.)
• Generated on a daily basis
• Typical stats
• Prettier stats
Readings
• “Visualizing the Evolution of Web Ecologies”
Chi et al., Xerox PARC, 1998
• “Visualizing Association Rules for Text Mining”
Wong, Whitney, & Thomas, Pacific Northwest, 1999
• “VISVIP: 3D Visualization of Paths through
Web Sites”
Cugini & Scholtz, National Institute of Standards and Technology, 1999
• “Case Study: E-Commerce Clickstream
Visualization
Brainerd & Becker, Blue Martini Software, 2001
• “What Did They Do? Understanding
Clickstreams with the WebQuilt Visualization
System”
Waterson et al., UC Berkeley, 2002
Readings
• “Visualizing the Evolution of Web Ecologies”
Chi et al., Xerox PARC, 1998
• “Visualizing Association Rules for Text Mining”
Wong, Whitney, & Thomas, Pacific Northwest, 1999
• “VISVIP: 3D Visualization of Paths through
Web Sites”
Cugini & Scholtz, National Institute of Standards and Technology, 1999
• “Case Study: E-Commerce Clickstream
Visualization
Brainerd & Becker, Blue Martini Software, 2001
• “What Did They Do? Understanding
Clickstreams with the WebQuilt Visualization
System”
Waterson et al., UC Berkeley, 2002
Evolution of Web Ecologies
• Rather than hits, focus intermediate
representation on (C)ontent, (U)sage, and
(T)opology, sorted by URL.
– URL1:
• {day1: <link> <link> …}
• {day2: <link> <link> …}
– URL2:
• {day1: <link> <link> …}
• Visualize an entire web site in a small amount
of space
• Show temporal changes
Disk Tree Visualization
• Breadth first traversal
• Each ring represents a tree level
• All leaf nodes guaranteed some angular
space (360 / # leaves)
Tree links
line mark in X and Y
Page access
frequency
line size/brightness
Lifecycle stage
color: new, continued,
deleted
Disk Tree Visualization (cont)
• Pros
– No occlusion problems since it’s 2D plane
– Can use the 3rd dimension for other info
(e.g. time)
– Aesthetically pleasing to the eye (?)
• Cons
– Difficult to see any page-level detail
– Confusing color choices
Time Tube Visualization
• Put Disk Trees along spatial axis
• Rotated so that each slice gets equal
screen area
• Focus+context
• Animation: Can fly through tube,
mapping time onto time
Interaction Model
•
•
•
•
•
Can rotate slices with a button click
Can focus a slice by clicking on it
Flicking gestures move slices around
Right-clicking zooms to an area
Mouseovers display more information
about a node in a side window
• Can bring up pages in the browser
• Animation of slices
Real-world Analyzes
• Deadwood: Shows pages
becoming [un]popular
• Shows effects of a redesign
Real-world Analyzes (cont)
• Added items are being used
• Deleted items aren’t
negatively impacting the rest
of the site
Comments
• Gives only a broad view of the data
with no real way to get at the specifics
• Interaction seems very advanced
• Not sure how intuitive the whole idea of
a circular tree is – seems kind of
gratuitous
Readings
• “Visualizing the Evolution of Web Ecologies”
Chi et al., Xerox PARC, 1998
• “Visualizing Association Rules for Text Mining”
Wong, Whitney, & Thomas, Pacific Northwest, 1999
• “VISVIP: 3D Visualization of Paths through
Web Sites”
Cugini & Scholtz, National Institute of Standards and Technology, 1999
• “Case Study: E-Commerce Clickstream
Visualization
Brainerd & Becker, Blue Martini Software, 2001
• “What Did They Do? Understanding
Clickstreams with the WebQuilt Visualization
System”
Waterson et al., UC Berkeley, 2002
Association Rule?
• Quantitative rule that describes
associations between sets of items
– Not qualitative because no domain
knowledge necessary for text mining
• Implication X  Y where
– X: set of antecedent items
– Y: consequent item
• Example: 80% of people who buy
diapers and baby powder also buy baby
oil.
Association Rule? (cont)
• Support/predictability/conditional
probability
– Percentage of items in the total set that
satisfies the union of items in the
antecedent and in the consequent item
• Confidence/prevalence/joint probability
– Percentage of articles that satisfy both the
antecendent and the consequent item
Association Rule Visualization
• Must visualize
– Antecedent items & consequent items
– Associations between antecedent and
consequent
– Rules' support
– Confidence
• Traditional ways of visualizing it
– 2D matrix
– Directed graph
2D Matrix (figure 1)
• Antecedent and consequent items on
axes
• Metadata icons in the cells that connect
the antecedent to consequent contain
support and confidence values
Association rule: B

C
2D Matrix (cont)
• Pros: one-to-one binary relationships
• Cons:
– Hard to see association rules in many-to-one
relationships (A+BC or AC and BC)
– Grouping antecedents adds complexity
– Object occulusion
Directed graph
• nodes = items
• edges =
associations
• Cons:
– Dozen or more items
 tangled display
– Selecting edges to
display multiple rules
requires significant
human interaction
Confusing?
“Novel” Technique
• Matrix: rule-to-item
– rows = topics
– columns = item associations
– blue/red = antecedent and consequent
• Bar graph = confidence/support
• Can use queries to filter
• Mouse zooming to support
context/focus
“Novel” Technique Advantages
• Handles hundreds of multiple antecedent
association rules
• View topics and associations simultaneously
• Individual items clearly shown
• No antecedent groups
• Few occulusions because metadata is plotted
at the far end and bar graph is scaled
• No screen swapping, animation, or serious
interaction required
“Novel” Technique Demo
• Demo shows scalability
• ~9 MB news article corpus of 100,000+
documents
• Use word and concept-based text engines
• Words evaluated on whether they’re
interesting depending on their position in
documents
• Suffices removed and common prepositions,
pronouns, adj’s, gerunds ignored
• Build a table of antecedents, consequents,
confidences, and supports -> feed into viz
Conclusions
• Rule-to-item association
• Very clear visualization if limited to a
few dozen rules
• Most web log visualizations jump to
using a graph; this paper forces you to
think twice.
Readings
• “Visualizing the Evolution of Web Ecologies”
Chi et al., Xerox PARC, 1998
• “Visualizing Association Rules for Text Mining”
Wong, Whitney, & Thomas, Pacific Northwest, 1999
• “VISVIP: 3D Visualization of Paths through
Web Sites”
Cugini & Scholtz, National Institute of Standards and Technology, 1999
• “Case Study: E-Commerce Clickstream
Visualization
Brainerd & Becker, Blue Martini Software, 2001
• “What Did They Do? Understanding
Clickstreams with the WebQuilt Visualization
System”
Waterson et al., UC Berkeley, 2002
VISVIP
• Captures individual movement between
pages rather than aggregates
• Shows paths - sequence of URLs
Topology
• Directed graph
• Force-directed algorithm
– Spring-like force
– Nodes repel each other with force inversely
proportional to the distance between them
(i.e. closer nodes means closer pages)
– Final force pulls nodes toward center
Content
• URLs abbreviated
– http://sims.berkeley.edu/~bob/pics/large/a
bd.gif  ge/abd
• Color-coded by content type
• Mouseover reveals all the abbreviated
information
Simplification
• Common problems
– Noise nodes not significant to paths image and mailto nodes
– Over-connectivity - link back to home page
or company logo
• Solutions
– Delete all edges connected to a node
– Make one node the graph root
– Focus on a subset of the graph
Path Sequence
• Showing subject paths as straight lines
didn't work
– Hard to follow single jagged path
– Multiple paths overlapped
• Spline representation
– Each path is a smooth curve overlaid on
the graph
– Colors for groups of subjects (e.g. novices)
Path Sequence (cont)
• User path-oriented layouts
– Simpler structure than when path is laid
over a graph of the entire site
Path Timing
• Vertical bar with base
on node, its height
proportional to time
spent on page
• Animation runs through
pages at 10-30 times
real-time
• Select a node to get
detailed stats
Comments
• Capturing individual movements pretty
innovative
• Curved user paths and reorienting the
layout based on user paths
• Overall graph viz not too clear
• Good tips for creating a web log mining
viz
Readings
• “Visualizing the Evolution of Web Ecologies”
Chi et al., Xerox PARC, 1998
• “Visualizing Association Rules for Text Mining”
Wong, Whitney, & Thomas, Pacific Northwest, 1999
• “VISVIP: 3D Visualization of Paths through
Web Sites”
Cugini & Scholtz, National Institute of Standards and Technology, 1999
• “Case Study: E-Commerce Clickstream
Visualization
Brainerd & Becker, Blue Martini Software, 2001
• “What Did They Do? Understanding
Clickstreams with the WebQuilt Visualization
System”
Waterson et al., UC Berkeley, 2002
Clickstream Visualizer
• Aggregate nodes
using an icon (e.g.
all the checkout
pages)
• Edges represent
transitions
– Wider means more
transitions
Customer Segments
• Collect
– Clickstream
– Purchase history
– Demographic data
• Associates customer data with their
clickstream (scary...)
• Different color for each customer
segment
Filtering
• Using the mouse or table control, can
filter by
– Edge weight
– Node selection
• Example: select checkout nodes and
see if users are exiting from nodes
Layout
Using third party Tom Sawyer package
1. Hierarchical from higher-out degree to
higher-in degree
– Mirrors actual flow of site users
– The default
2. Circular
– Puts related nodes into circles
– Shows relationships between groups of
pages
Layout (cont)
• Aggregation based on file system path
(good idea?)
Initial Findings
• Gender shopping
differences
(intriguing...)
Initial Findings (cont)
• Checkout process
analysis
• Newsletter hurting
sales
Comments
• Visualizing clickstreams with
demographic data
• Grouping pages by type
• Best use of color
• Icons an interesting way of reducing
complexity
Readings
• “Visualizing the Evolution of Web Ecologies”
Chi et al., Xerox PARC, 1998
• “Visualizing Association Rules for Text Mining”
Wong, Whitney, & Thomas, Pacific Northwest, 1999
• “VISVIP: 3D Visualization of Paths through
Web Sites”
Cugini & Scholtz, National Institute of Standards and Technology, 1999
• “Case Study: E-Commerce Clickstream
Visualization
Brainerd & Becker, Blue Martini Software, 2001
• “What Did They Do? Understanding
Clickstreams with the WebQuilt Visualization
System”
Waterson et al., UC Berkeley, 2002
System Design
•
•
•
•
•
Log data with proxy
Infer actions
Aggregate data
Layout graph
Display interactive visualization
Capturing Interaction
• Typical HTTP request…
Client Browser
Web Server
Capturing Interaction (cont)
• WebQuilt captures interaction with a proxy
– Proxies have typically been used for caching and
firewalls
Client Browser
Proxy
WebQuilt
Log
Web Server
Capturing Interaction (cont)
• If a page says:
<A HREF=“coolpage.html">
• Change it to:
<A
HREF="http://webquiltproxy.cs.berkeley
.edu/webquilt?replace=http://www.spiff
ypages.com/coolpage.html&tid=1&linki
d=13">
Capturing Interaction (cont)
• Pros:
– Don’t need access to servers
– Can analyze sites without permission from
the server
– Can gather clickstreams from a variety of
devices including PDAs, phones,
desktop computers
• Cons:
– No access direct to the client
Visualization
Interactive, zoomable directed graph
• Nodes = web pages
• Edges = aggregate traffic
between pages
Java-based SATIN toolkit for
gesturing & zooming interaction
Image rendering of web pages:
• JacoZoom Java callable wrappers
around an ActiveX component
• MSIE window
Directed graph
• Nodes: visited pages
– Color marks entry and exit
nodes
• Arrows: traversed links
– Thicker: more heavily
traversed
– Color
• Red/yellow: Time spend
before clicking
• Blue: optimal path chosen by
designer
Controls
• Slider: Zoom in and out
• Checkboxes: Filter paths to display
Pages
• Zooming in shows page thumbnails
• Arrows
– Originate from actual links or the Back
button
– Translucent & don’t cover details
Layout
Layout system flexible…
1. Edge-weighted depth-first traversal
–
–
Most visited path along top
Recursively place less followed paths below
2. Grid positioning
– Organizes distance between nodes
– Avoid overlapping nodes
Interaction
• Selecting nodes
• Zooming in and out
• Navigational gestures
Inferring & Aggregating
• Take log files and infer actions, such as
when the back button is pressed
– Can infer back button pressed, but not
combinations of back and forward
– Extensible framework to add other inferred
actions
• Aggregate information, preserving
individual paths
Running a WebQuilt Remote
Usability Test
• Recruit users
• Design and distribute tasks (via email)
• Auto-collect! Watch and wait as users
perform tasks and proxy logs data
• Visualize, analyze
• Use the results to change design
Pilot Usability Study
• Edmunds.com PDA web site
• Visor Handspring equipped with a
OmniSky wireless modem
• 10 users asked to find…
– Anti-lock brake information on the latest
Nissan Sentra model
– The Nissan dealer closest to them.
In the Lab vs. Out in the Wild
Comparing in-lab usability testing with WebQuilt remote
usability testing
• 5 users were tested in the lab
• 5 were given the device and asked to perform the
task at their convenience
• All task directions, demographic data, and follow up
questionnaire data was presented and collected in
web forms as part of the WebQuilt testing
framework.
Classifying Usability Issues
Lab: Tester observations, participant comments and
questionnaire data
Remote: WebQuilt visualization and questionnaire data
Four categories of issues
•
•
•
•
Browser
Device
Test design
Site design
• Six severity levels
• 0 indicates comment
• 1-5 where 1 is a very minor issue and 5 is a critical issue
Findings
Browser
Interact before load (3)
No forward button (2)









Site Design
Falsely completed task (4)
Long download times (4)
Ping-pong behavior (3)
Interact before load (3)
Too much scrolling (2)
Save address functionality
not clear (1)
Back button navigation (0)
Would like more features (0)
Finds site useful (0)
Device
Difficulty with input in
questionnaire (3)
Difficulty scrolling (2)
Device errors unrelated to
testing (1)
Tried writing on screen (0)
Test Design
 Falsely completed task (4)
 Difficulty
remembering
task description (3)
 Difficulty with input in
questionnaire (3)
 Questionnaire
wording
problems (3)
 Forgot how to end task (1)
 Confusing task description
(1)
Findings
• WebQuilt methodology is promising for
uncovering site design related issues.
• 1/3 of the issues were device or browser
related.
• Browser and device issues can not be captured
automatically with WebQuilt unless they cause an
interaction with the server
• can be revealed via the questionnaire data.
Testing Concerns
• What to do when problems with running the
test occur?
• Understanding user motivation is still
ambiguous: Curiosity vs. confusion?
• Gathering qualitative feedback on mobile
devices is difficult
– PDA input difficult
– Phones have potential for audio
Comments
• Zooming/filtering great for showing
overview and page-level details
– Can put screenshots directly into the viz
• Layout in relation to intended path
• Study compares remote usability tests
to traditional tests - promising
• Proxy logging very cool
Future Work
• Expanded mobile device interaction capture,
specifically net-enabled cell phones
• Improve filtering capabilities, integrating
questionnaire and demographic data
• Clever algorithms to simplify graph layout
• Improved quantitative reporting
• Improved controls/interaction
• More rigorous evaluation with designers and
usability experts
Concluding Comments
• Many incremental improvements in web
log/data mining viz (using a graph,
using demographic data, etc.)
• Would be really good to see a study of
usability engineers and web developers
comparing the tools themselves