WIRED - Web Analytics Week

1
WIRED - Web Analytics Week

• WIRED System Evaluations due now
• Web Logs overview
• Web Analytics
• Understanding Queries
• Tracking Users
• Web Log Reliability
• Web Log Data Mining KDD

2
Web Analytics

• Evaluation of Web Information Retrieval ( Web
  Information Seeking)
• What can we learn?
• IR systems use
• Web server administration
• Who are the users?
• Types of users
• User situations
• How does it affect or help IR?

3
Web Server Overview

• Any application that can serve files using the
  HTTP protocol
• Text, HTML, XHTML, XML
• Graphics
• CGI, applets, serlets
• other media MIME types
• Apache or MS IIS that serve primarily Web pages
• Servers create ASCII text log files showing
• Date, time, bytes transferred, (cache status)
• Status/error codes, user IP address, (domain
  name)
• Server method, URI, misc comments

4
Web Log Overview

• Access Log
• Logs information such as page served or time
  served
• Referer Log
• Logs name of the server and page that links to
  current served page
• Not always
• Can be from any Web site
• Agent Log
• Logs browser type and operating system
• Mozilla
• Windows

5
What can we learn from Web logs?

• Every time a Web browser requests a file, it gets
  logged
• Where the user came from
• What kind of browser used to access the server
• Referring URL
• Every time a page gets served, it gets logged
• Request time, serve time, bytes transferred, URI,
  status code

6
Web Log Analysis in Action

• UT Web log reports
• (Figures in parentheses refer to the 7 days to
  28-Mar-2004 0300).
• Successful requests 39,826,634 (39,596,364)
• Average successful requests per day 5,690,083
  (5,656,623)
• Successful requests for pages 4,189,081
  (4,154,717)
• Average successful requests for pages per day
  598,499 (593,530)
• Failed requests 442,129 (439,467)
• Redirected requests 1,101,849 (1,093,606)
• Distinct files requested 479,022 (473,341)
• Corrupt logfile lines 427
• Data transferred 278.504 Gbytes (276.650 Gbytes)
• Average data transferred per day 39.790 Gbytes
  (39.521 Gbytes)

7
Problems with Web Servers

• Actual user or intent not known
• Paths difficult to determine
• Infrequent access challenging to uncover
• No State Information
• Server Hits not Representative
• Counters inaccurate
• DOS, Floods, Bandwidth can Stop intended usage
• Robots, etc.
• ISP Proxy servers
• 5.3 Unsound inferences from data that is logged
  Haigh Megarity, 1998.

8
Web Server Configuration

• Unique file directory names at a glance
  analysis
• Hierarchical directory structure
• Redirect CGI to find referrer
• Use a database
• store web content
• record usage data with context of content logged
• Create state information with programming
• Servlets, ActiveX, Javascript
• Custom server or log format
• Log rollover, report frequency, special case
  testing

9
Log File Format

• Extended Log File Format - W3C Working Draft
  WD-logfile-960323
• 192.117.240.3 - - 24/Jul/1998000004 -0400
• "GET /10/3/a3-160-e.html HTTP/1.0" 200 2308
  "http//www.amicus.nlc-bnc.ca/wbin/resanet/itemdis
  p/l0/d1/r1/e0/h10/i11683503"
• "Mozilla/2.0 (compatible MSIE 3.01 Windows
  95)"
• Every server generates slightly different logs
• Versions operating system issues
• Admin tweaks to log formats
• Extended Log Format most common
• WWW Consortium Standards ( apache)

10
Lets Look at some logs

• http//www.ischool.utexas.edu/analog-monthly.html
• http//www.ischool.utexas.edu/analog-weekly.html

11
Log Analysis Tools

• Analog
• Webalizer
• Sawmill
• WebTrends
• AWStats
• WWWStat
• GetStats
• Perl Scripts
• Data Mining Business Intelligence tools

12
WebTrends

• A whole industry of analytics
• Most popular commercial application

13
Measuring Web Site Usage

• Now that the Web is a primary source,
  understanding its use is critical
• Little external cues that the Web site is being
  used
• What - pages and their content/subject
• How - browsers
• Who - userid or IP
• When - trends, daily, weekly, yearly
• Where - the user is and what page they came from

14
What you cant measure?

• Who the user is
• Always
• If the users needs have changed
• If theyre using the information
• Browsing vs. Reading vs. Acting on the
  information
• Changes to site and how they affect each user
• Pages not used at all - and why

15
Analysis of a Very Large Search Log

• What kinds of patterns can we find?
• Request query and results page
• 280 GB Six Weeks of Web Queries
• Almost 1 Billion Search Requests, 850K valid,
  575K queries
• 285 Million User Sessions (cookie issues)
• Large volume, less trendy
• Why are unique queries important?
• Web Users
• Use Short Queries in short sessions - 63.7 one
  request
• Mostly Look at the First Ten Results only
• Seldom Modify Queries
• Traditional IR Isnt Accurately Describing Web
  Search
• Phrase Searching Could Be Augmented
• Silverstein, Henzinger, Marais, Moricz (1998)

16
Analysis of a Very Large Search Log

• 2.35 Average Terms Per Query
• 0 20.6 (?)
• 1 25.8
• 2 26.0 72.4
• Operators Per Query
• 0 79.6
• Terms Predictable
• First Set of Results Viewed Only 85
• Some (Single Term Phrase) Query Correlation
• Augmentation
• Taxonomy Input
• Robots vs. Humans

17
Web Analytics and IR?

• Knowing access patterns of users
• Lists of search terms
• Numbers of words
• Words, concepts to add (synonyms)
• Types of queries
• Success of searching a site
• Was a result link clicked on?
• How many pp/user after a search?
• Is a new or better search interface needed?

18
Real Life Information Retrieval

• 51K Queries from Excite (1997)
• Search Terms 2.21
• Number of Terms
• 1 31 2 31 3 18 (80 Combined)
• Logic Modifiers (by User)
• Infrequent
• AND, , -
• Logic Modifiers (by Query)
• 6 of Users
• Less Than 10 of Queries
• Lots of Mistakes
• Uniqueness of Queries
• 35 successive
• 22 modified
• 43 identical

19
Real Life Information Retrieval

• Queries per user 2.8
• Sessions
• Flawed Analysis (User ID)
• Some Revisits to Query (Result Page Revisits)
• Page Views
• Accurate, but not by User
• Use of Relevance Feedback (more like this)
• Not Used Much (11)
• Terms Used Typical frequent
• Mistakes
• Typos
• Misspellings
• Bad (Advanced) Query Formulation
• Jansen, B. J., Spink, A., Bateman, J.,
  Saracevic, T. (1998)

20
KDD for Extracting Knowledge

• Knowledge extraction, information discovery,
  information extraction, data archeology, data
  pattern processing, OLAP, HV statistical analysis
• Sounds as if knowledge is there to be found.
• User and usage context help find the knowledge
• Hypothesis before analysis
• Why KDD, why now?
• Data storage, analysis costs
• Visualization

21
KDD Process

• Database for structured data and queries
• How structured, alorithms for queries
• How results can be understood and visualized
• Iterative Interactive, hypothesis driven
  hypothesis generating

22
KDD Efforts

• Data Cleaning
• Formulating the Questions
• Finding useful features to represent the data
  p30
• Models
• Classification to fit data into pre-defined
  classes
• Regressions to fit predictions values
• Clustering to class sets found in data
• Summarization to briefly describe data
• Dependency discovery of variable relationships
• Sequence analysis for time or interaction patterns

23
Data Prep for Mining the WWW

• Processing the data before mining
• WEBMINER system - site toplogy
• Cleaning
• User identification
• Session identification (episodes)
• Path completion

24
(No Transcript)
25
Web Usage Mining

• VL Verification
• Data Mining to Discover Patterns of Use
• Pre-Processing
• Pattern Discovery
• Pattern Analysis
• Site Analysis, Not User Analysis
• Srivastava, J., Cooley, R., Deshpande, M., Tan,
  P.N. - 2000

26
Web Usage Discovery

• Content
• Text
• Graphics
• Features
• Structure
• Content Organization
• Templates and Tags
• Usage
• Patterns
• Page References
• Dates and Times
• User Profile
• Demographics
• Customer Information

27
Web Usage Collection

• Types of Data
• Web Servers
• Proxies
• Web Clients
• Data Abstractions
• Sessions
• Episodes
• Clickstreams
• Page Views
• The Tools for Web Use Verification

28
Web Usage Preprocessing

• Usage Preprocessing
• Understanding the Web Use Activities of the Site
• Extract from Logs
• Content Preprocessing
• Converting Content Into Formats for Processing
• Understanding Content (Working with Dev Team)
• Structure Preprocessing
• Mining Links and Navigation from Site
• Understanding Page Content and Link Structures

29
Web Usage Pattern Discovery

• Clustering for Similarities
• Pages
• Users
• Links
• Classification
• Mapping Data to Pre-defined Classes
• Rule Discovery
• Rule Rules
• Computation Intensive
• Many Paths to the Similar Answers
• Pattern Detection
• Ordering By Time
• Predicting Use With Time

30
Web Usage Mining as Evaluation?

• Mining Goals
• Improved Design
• Improved Delivery
• Improved Content
• Personalization (XMod Data)
• System Improvement (Tech Data)
• Site Modification (IA Data)
• Business Intelligence (Market Data)
• Usage Characterization (User Behavior Data)

31
Web Analytics Wrap-up

• What can we learn about users?
• What can we learn about services?
• How can we help users improve their use?
• How can IR models benefit from this analysis?
• What kind of improvements in Web IR systems and
  their interfaces can be take from this?