What Is Log Analyis

2,249 views
2,035 views

Published on

A presentation that I gave at the Query Log Analysis: From Research to Best Practice Workshop 27 - 28 May 20098 in London, UK http://ir.shef.ac.uk/cloughie/qlaw2009/index.html

Published in: Education, Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
2,249
On SlideShare
0
From Embeds
0
Number of Embeds
9
Actions
Shares
0
Downloads
53
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

What Is Log Analyis

  1. 1. What is Web log analysis ? Jim Jansen College of Information Sciences and Technology The Pennsylvania State University [email_address] Let’s make this a discussion!
  2. 2. Outline <ul><li>Definition </li></ul><ul><li>Examples </li></ul><ul><li>Theory and Essential Construct </li></ul><ul><li>Data Collection </li></ul><ul><li>Method </li></ul><ul><li>Discussion </li></ul>
  3. 3. Web log analysis is part of the domain of … <ul><li>... Web analytics </li></ul><ul><li>The Web Analytics Association (WAA) defines Web analytics as the measurement, collection, analysis, and reporting of Internet data for the purposes of understanding and optimizing Web usage ( http://www.webanalyticsassociation.org/ ) </li></ul><ul><li>Shares common theoretical and methodology characteristics with all forms of log analysis (e.g., Intranet logs, systems logs, OPAC logs, search logs, etc.) </li></ul>
  4. 4. W3C Extended Log Format W3C Extended Log Format -Variety of fields for examining visitors to Web sites. Other common format is NCSA Separate Log that is composed of three logs ( Common log – actions on the server, Referral log – where they came from, and Agent log – stuff about the client computer)
  5. 5. Variety of tools help make sense of this log data
  6. 6. Other Log Examples … Search Logs have some common fields, such as time, queries, results, etc. We can enrich the log with additional fields.
  7. 7. Keyword advertising logs provides calculated metrics
  8. 8. Twitter log Tweets with author in XML
  9. 9. Theoretical Foundations <ul><li>Part of the behaviorism paradigm </li></ul><ul><li>Behaviorism – an approach focused on the outward behavioral aspects of thought and emphases the observed behaviors </li></ul><ul><li>Behaviorism – Pavlov, Watson, & Skinner </li></ul>Burrhus Frederic Skinner John B. Watson Ivan Petrovich Pavlov
  10. 10. Behaviorism Characteristics <ul><li>Inductive , data-driven and characterized by empirical observation of measurable behavior </li></ul><ul><li>Grounded on somebody doing something in a situation ( all the environmental and situational features are embedded behaviors) </li></ul><ul><li>Critics of behaviorism as a psychological theory have issues with rejection of mental processes . I agree - people are more than “ mediators between behavior and the environment ” (Skinner, 1993, p 428) </li></ul>
  11. 11. What is a Behavior? <ul><li>… an observable activity of a person, animal, team, organization, or system. </li></ul><ul><li>One can classify behaviors into three general categories. Behaviors are </li></ul><ul><li>something that one can detect and record </li></ul><ul><li>actions or specific goal-driven events with some purpose other than the specific action that is observable </li></ul><ul><li>reactive responses to environmental stimuli </li></ul>
  12. 12. What is a Behavior? <ul><li>Behavior is the essential construct of the behaviorism and of log research </li></ul><ul><li>Logs record behaviors of users and systems (records behavior but can’t tell affective , cognitive , or situational aspects) </li></ul><ul><li>A behavior is the key variable (i.e., an entity representing a set of events where each event may have a different value ) </li></ul>
  13. 13. Ethograms <ul><li>a taxonomy or index of behavioral patterns </li></ul><ul><li>details the different forms of behavior that an user exhibits </li></ul><ul><li>categories of behavior are objective , discrete , not overlapping . This makes the definitions of each behavior (and category of behaviors) clear, detailed and distinguishable from each other </li></ul>
  14. 14. Behavior Description of the behavior What about the data collection method? User implemented the OR assistance. OR User implemented the AND assistance. AND User implemented the RELEVANCE FEEDBACK assistance. Relevance Feedback User implemented the PREVIOUS QUERIES assistance. Previous Queries User implemented the SYNONYMS assistance. Synonyms User implemented the SPELLING assistance. Spelling User implemented the PHRASE assistance. Phrase Interaction in which the user entered, modified, or submitted a query, utilizing assistance offered by the application. Implement Assistance Interaction in which the user viewed the assistance offered by the application. View/Implement assistance   User saved a relevant document. Save User printed a relevant document. Print User copy-pasted all of, a portion of, or the URL to a relevant document. Copy - Paste User bookmarked a relevant document. Bookmark Interaction such as print, save, bookmark, or copy. Relevance action   User switched between two open browsers or closed a browser window. Switch /Close browser window User opened a new browser. Open new browser Interaction in which the user opened, closed, or switched browsers. Browser   User clicked the Home button. Home User clicked the Back button. Back Interaction in which the user activated a navigation button on the browser, such as Back or Home. Navigation   Interaction in which the user created a folder to store relevant URLs. Create Favorites Folder Interaction in which the user used the FIND feature of the browser. Find Feature in Document Interaction in which the user entered, modified, or submitted a query without visibly incorporating assistance from the system. This category includes submitting the original query, which was always the first interaction with system. Execute Query Interaction in which the user initiated an action in the interface. Execute   User did not scroll the document. Without Scrolling User scrolled the document. With Scrolling Interaction in which the user viewed or scrolled a particular document in the results listings. View document   User selected a specific results page. GoTo in Set of Results List User moved to the Previous results page. Previous in Set of Results List User moved to the Next results page. Next in Set of Results List Interaction in which the user clicked on a URL of one of the results in the results page. Click URL (in results listing) Interaction in which the user makes a selection in the results listing. Selection   User was looking for results, but there were no results in the listing. but No Results in Window User did not scroll the results page. Without Scrolling User scrolled the results page. With Scrolling Interaction in which the user viewed or scrolled one or more pages from the results listing. If a results page was present and the user did not scroll, we counted this as a View Results Page. View results Description Behavior Example of an Ethogram
  15. 15. <ul><li>can view the data collected in log files as trace data . </li></ul><ul><li>people conducting the activities of their daily lives many times create things, create marks, induce wear, or reduce some existing material . </li></ul><ul><li>Within the confines of research, these things, marks, and wear become data </li></ul><ul><li>Classically, trace data are the physical remains of people’s interaction </li></ul>Data Collection: Trace Data Wear on a carpet Trash heap Computer storage media
  16. 16. Trace Data <ul><li>In the past, trace data was often time consuming to gather and process, making such data costly. </li></ul><ul><li>logging software makes collecting trace data easy and cheap </li></ul><ul><li>Log data is controlled accretion data , where the researcher or some other entity alters the environment in order to create the accretion data </li></ul><ul><li>With the user of client apps (such as desktop search bars), the collection of data is nearly unlimited from a technology perspective </li></ul>What is cool about trace data for researchers?
  17. 17. Data Collection <ul><li>Log data has significant advantages as a data collection approach for the study and investigation of behaviors, including: </li></ul><ul><li>Scale : not a limiting factor as in lab user studies </li></ul><ul><li>Power : large sample size for inference testing; in fact, so large must account for the size effect </li></ul><ul><li>Scope : naturalistic; researchers can investigate range of interactions in a multi-variable context </li></ul><ul><li>Location : can collectin distributed environments </li></ul><ul><li>Duration : collect log data over an extended period </li></ul>
  18. 18. Methodological Foundations <ul><li>Use of logs to collect trace data is an unobtrusive methods (a.k.a., non-reactive or low-constraint). Unobtrusive methods … </li></ul><ul><li>allows data collection without directly interfering into the context and </li></ul><ul><li>does not require a direct response from participants </li></ul>Customer Behavior (video) Chemistry (surface marking)
  19. 19. Methodological Foundations <ul><li>Three justifications for unobtrusive methods: </li></ul><ul><li>Uncertainty principle : researchers interjected into an environment become part of the system </li></ul><ul><li>Observer effect : difference that is made to an activity or a person’s behaviors by being observed </li></ul><ul><li>Observer bias : observers overemphasize behavior they expect to find and fail to notice behavior they do not expect </li></ul><ul><li>Trace data helps in overcoming the Uncertainty principle , Observer effect , and Observer bias in the data collection. Note for data collection but not data analysis </li></ul>Example: ethnography studies (where the researcher “bird dogs” a study participant Example: no one searches for porn in a lab study of Web searching Example: is why medical trials are double blind rather than single blind
  20. 20. Methodological Foundations <ul><li>Inherent characteristics in the method of log data collection; Web analytics has issues to address as a result: </li></ul><ul><li>Abstraction – how does one relate low-level data to higher-level concepts? </li></ul><ul><li>Selection – how does one separate the necessary from unnecessary data? </li></ul><ul><li>Reduction – how does one reduce the complexity and size of the data set? </li></ul><ul><li>Context – how does one interpret the significance of events? </li></ul><ul><li>Evolution – how can one collect data without impacting application deployment or use? </li></ul>
  21. 21. Recap of Web Analytics Type of Data Data Collection Key Construct Theoretical Foundation Behaviorism Behavior Unobtrusive Trace Query Response Click User Computer
  22. 22. Research <ul><li>Book: Jansen, B. J., Spink, A., and Taksa, I. (2009) Handbook of Research on Web Log Analysis , Hershey, PA: Idea Group Publishing </li></ul><ul><ul><li>First chapter on theory of log analysis is free! </li></ul></ul><ul><li>Lecture: Jansen, B. J. (Forthcoming) Understanding User – Web Interactions via Web Analytics . Morgan-Claypool Lecture Series . Gary. Marchionini (Ed). Morgan-Claypool: San Rafael, CA. </li></ul><ul><ul><li>manuscript about Web Analytics, soup to nuts </li></ul></ul>
  23. 23. Thank you! (open for questions and further discussion) Jim Jansen College of Information Sciences and Technology The Pennsylvania State University [email_address]

×