Web Usage Miningand Using Ontology for Capturing Web Usage Semantic

İsmail Hakkı Toroslu Middle East Technical University Department of Computer Engineering Ankara, Turkey Web Usage Mining and Using Ontology for Capturing Web Usage Semantic

08/28/11 PART I A New Approach for Reactive Web Usage Data Processing

Web Mining
Previous Session Reconstruction Heuristics
Smart-SRA
Agent Simulator
Experimental Results
Conclusion
OUTLINE

Web Mining
Data Mining: Discover and retrieve useful and interesting patterns from a large dataset.
Web mining: Dataset is the huge web data.
Dimensions:
Web content mining
Web structure mining
Web usage mining

Web Usage Mining (WUM) Application of data mining techniques to web log data in order to discover user access patterns. Example User Web Access Log Web Mining 4130 200 HTTP/1.0 C.html GET [25/Apr/2005:03:04:48–05] 144.123.121.23 2050 200 HTTP/1.0 B.html GET [25/Apr/2005:03:04:43–05] 144.123.121.23 3290 200 HTTP/1.0 A.html GET [25/Apr/2005:03:04:41–05] 144.123.121.23 Number of Bytes Transmitted Success of Return Code Protocol URL Method Request Time IP Address

Phases of Web Usage Mining Web Mining Pre-Processing Pattern Analysis Raw Server log User session File Rules and Patterns Interesting Knowledge Applications Session Reconstruction Heuristics Pattern Discovery Apriori, GSP, SPADE

Session Reconstruction
Sessions are reconstructed by using heuristics that select and group requests belonging to the same user session
Types:
Reactive: processing requests after they are handled by the web server,
Proactive: processing occurs during the interactive browsing of the web site by the user

Time-oriented heuristics
Navigation-oriented heuristic
New Reactive Session Reconstruction Technique: Smart-SRA Combines these heuristics with "site topology" information in order to increase the accuracy of the reconstructed sessions Previous Session Reconstruction Heuristics

Example Web Topology Graph Example Web Page Request Sequence Previous Session Reconstruction Heuristics 47 32 29 15 6 0 Timestamp P 23 P 34 P 49 P 13 P 20 P 1 Page

Time-oriented heuristics -1
Total session time: duration of a discovered session is limited with a threshold
Discovered Sessions (30 mins):
[P 1 , P 20 , P 13 , P 49 ]
[P 34 , P 23 ]
Previous Session Reconstruction Heuristics 47 32 29 15 6 0 Timestamp P 23 P 34 P 49 P 13 P 20 P 1 Page

Time-oriented Heuristics -2
Page-stay time: time spent on any page is limited with a threshold
Discovered Sessions ( 10 mins):
[P 1 , P 20 , P 13 ]
[P 49 , P 34 ]
[P 23 ]
Previous Session Reconstruction Heuristics 47 32 29 15 6 0 Timestamp P 23 P 34 P 49 P 13 P 20 P 1 Page

Navigation-Oriented Heuristic
Adding page WP N+1 to a session [WP 1 , WP 2 , …, WP N ]
If WP N has a hyperlink to WP N+1
[WP 1 , WP 2 , …, WP N , WP N+1 ]
If WP N does not have a hyperlink to WP N+1
and WP Kmax is the nearest page having a hyperlink to WP N+1 add backward browser moves
[WP 1 , WP 2 ,…, WP N , WP N-1 , WP N-2 ,..., WP Kmax , WP N+1 ]

Navigation-Oriented Heuristic Previous Session Reconstruction Heuristics [P 1 , P 20 , P 1 , P 13 , P 49 , P 13 , P 34 , P 23 ] P 23 Link[P 34 , P 23 ] =1 [P 1 , P 20 , P 1 , P 13 , P 49 , P 13 , P 34 ] P 34 Link[P 49 , P 34 ] = 0 Link[P 13 , P 34 ] = 1 [P 1 , P 20 , P 1 , P 13 , P 49 ] P 49 Link[P 13 , P 49 ] = 1 [P 1 , P 20 , P 1 , P 13 ] P 13 Link[P 20 , P 13 ] = 0 Link[P 1 , P 13 ] = 1 [P 1 , P 20 ] P 20 Link[P 1 , P 20 ] = 1 [P 1 ] P 1 [ ] New Page Condition Curent Session

Smart-SRA
Phase 1: Shorter request sequences are constructed by using overall session duration time and page-stay time criteria
Satisfies the overall session duration time limit
Phase 2: Candidate sessions are partitioned into maximal sub-sessions such that:
between each consecutive page pair in a session there is a hyperlink from the previous page to the next page
the page stay time criteria is also satisfied
Adds referrer constraints of the topology rule while eliminating the need for inserting backward browser movements.
Contains Two Phases:

1. Determine the web pages without any referrer (on its left) and remove them from the candidate session
2. For each one of these pages
For each previously constructed session
If there is a hyperlink from the last page of the session to the web page, then append the web page to the session
(if the page stay time constraint is satisfied)
3. Remove non-maximal sessions
Smart-SRA Steps of Phase 2
Process a candidate session from left to right by repeating
the following steps until the candidate session is empty:

Example Candidate Session Example Web Topology Smart-SRA 15 14 12 9 6 0 Timestamp P 23 P 34 P 49 P 13 P 20 P 1 Page

Smart-SRA [P 1 , P 13 , P 34 , P 23 ] , [P 1 , P 13 , P 49 , P 23 ] [P 1 , P 20 , P 23 ] [P 1 ,P 13 ,P 34 ], [P 1 , P 13 , P 49 ] [P 1 , P 20 ] New Session Set (after) [P 1 , P 13 , P 34 , P 23 ] [P 1 , P 13 , P 49 , P 23 ], [P 1 , P 20 , P 23 ] [P 1 ,P 13 ,P 34 ] [P 1 , P 13 , P 49 ] Temp Session Set {P 23 } {P 49 , P 34 } Temp Page Set [P 1 ,P 13 ,P 34 ] [P 1 , P 13 , P 49 ] [P 1 , P 20 ] [P 1 ,P 20 ] [P 1 ,P 13 ] New Session Set (before) [P 23 ] [P 49 , P 34 , P 23 ] Candidate Session 4 3 Iteration [P 1 ,P 20 ] [P 1 ,P 13 ] [P 1 ] New Session Set (after) [P 1 ,P 20 ] [P 1 ,P 13 ] [P 1 ] Temp Session Set {P 20 , P 13 } {P 1 } Temp Page Set [P 1 ] New Session Set (before) [P 20 , P 13 , P 49 , P 34 , P 23 ] [P 1 , P 20 , P 13 , P 49 , P 34 , P 23 ] Candidate Session 2 1 Iteration

Agent Simulator
Models the behavior of web users and generates web user navigation and the log data kept by the web server
Used to compare the performances of alternative session reconstruction heuristics
Uses 4 Primitive behaviors for simulating complex navigation of web user.

Web user can start a new session with any one of the possible entry pages of the web site Agent Simulator User-Behavior I

Web user can select a new page having a link from the most recently accessed page P 13 P 1 P 49 P 20 P 23 P 34 2 1 Agent Simulator User-Behavior II

Web user can select as the next page having a link from any one of the previously browsed pages Agent Simulator User-Behavior III P 13 P 1 P 49 P 20 P 23 P 34 2 1 3 4 5

Web user can terminate the session Agent Simulator User-Behavior IV P 13 P 1 P 49 P 20 P 23 P 34 2 1 3 4 5 6

Parameters for simulating behavior of web user
Session Termination Probability (STP)
Link from Previous pages Probability (LPP)
New Initial page Probability (NIP)
Agent Simulator

Heuristics Tested
Time oriented heuristic (heur1)
(total time < 30 min)
Time oriented heuristic (heur2)
(page stay < 10 min)
Navigation oriented heuristic (heur3)
Smart-SRA heuristic (heur4)

Accuracy
Reconstructed session H captures
a real session R
if R occurs as a subsequence of H (R ⊏H)
R = [P1, P3, P5]
H = [P9, P1, P3, P5 , P8] => R ⊏H
H = [P1, P9 , P3, P5, P8] => R ⋢H

Parameters for generating user sessions and web topology Experimental Results 30% 0%-90% NIP : Fixed & Range 30% 0%-90% LPP : Fixed & Range 5% 1%-20% STP : Fixed & Range 10000 Number of agents 0,5 min Deviation for page stay time 2,2 min Average number of page stay time 15 Average number of outdegree 300 Number of web pages (nodes) in topology

Accuracy vs. STP Experimental Results

Accuracy vs LPP Experimental Results

Accuracy vs. NIP Experimental Results

Conclusion
New session reconstruction heuristic: Smart-SRA
Does not allow sequences with unrelated consecutive requests (no hyperlink between the previous one to the next one)
No artificial browser (back) requests insertion in order to prevent unrelated consecutive requests
Only maximal sessions
Agent simulator
Accuracy measure
Experimental results show Smart-SRA outperforms previous heuristics

08/28/11 PART II Semantically Enriched Event Based Model f or W eb Usage Mining

Introduction
Related Work
Semantic Event Based Sessions
Formal Definition of Semantic Events
Algorithms for Mining Semantic Event Patterns
Con c lusion
08/28/11 OUTLINE

Traditional WUM is based on pageviews,
but user interaction model is changing
Users do not care about pageviews,
but they use web site to achieve high level goals such as
Finding and viewing a video
Buying tickets
Searching for the nearest Italian restaurant
Listening to a song, etc
08/28/11 Introduction

We should analyze usage data in a series of “events”
Search Mediterranean Restaurants
S earch Italian Restaurants
View the reviews for Restaurant A
View the reviews for Restaurant B
C lick the web site link of Restaurant A
Incorporating semantic knowledge in the process is the logical choice
A method should be devised to capture user behavior
Usage data should be mapped to semantic space
An algorithm should be developed to exploit semantic relations

In this work we propose methods for:
tracking and logging domain level events
i njecting semantic to events
semantic ordering of events
an algorithm for computing sequences of frequent events
Proposed system tested with 2 web sites
Music Streaming Site
Mobile Network Operator’s Site

Events are conceptual actions
that the user performs to achieve a certain a ff ect
Events are used to capture business actions
that are deﬁned in the site’s domain
The site admin is responsible for
defining and tracking events
Events are tracked via JavaScript client
08/28/11 Semantic Event Based Sessions

E xample event s :
Play a video even t
Add to shopping cart event
A dd friend action
Sometimes we may be interested in
properties of events, such as
“ query” property of a “search event”
“ category” property of a “view video event”

Every event is defined as an object.
Objects can have properties which relate an object
with another object or
with a datatype value
The relations between objects are captured in a tree
Each individual and property is a node
Object property nodes have object as a parent and a child

A sample event from a hypothetical video viewing site
08/28/11

Events can be used to capture all relevant actions of the user including plain pageviews
With a mapping of events to the web site’s ontology we can define ‘semantic events’
Events are mapped to semantic space by using the class and property names in the ontology
As a result of this mapping, the data to be mined is
An ontology containing the terminological part
Logs containing semantic objects
08/28/11 Events as Semantic Objects

A Session is an ordered set of atomtrees that corresponds to events for a single user in a certain browsing activity
An Atom-tree is a tree of connected atoms. The atom tree represents a domain event in the web site's ontology
An Atom is either an individual of a class, a datatype property assertion, or an object property assertion
08/28/11 Definitions

A Pattern is an ordered set of atomtrees
A session S supports the pattern Q
iff Q is a subsequence of S
where isMoreGeneralThan relation is used instead of equality in determining the subsequence relation
08/28/11 Definitions

08/28/11

For a given set of session, the problem is to find the set of patterns with support greater than the threshold value, minSupport
Two phase Apriori-like algorithm
First phase finds frequent atomtrees (patterns containing single atomtree)
Second phase searches for frequent atomtree patterns
08/28/11 Algorithm

Apriori property:
If atomtree a 1 is more general than atomtree a 2 , then the support of a 1 is greater than the support of a 2
getMostGeneralForms generates the set of trees,
more general than the given atomtree
not less general than any other atomtree generated
For level-wise search, a one-step refinement operator, is defined over the set of individual atoms, object and datatype property assertion atoms.
08/28/11 Phase I: Find Frequent Atomtrees

Given an atom, one-step refinement operator refines another atom, by refining for subclass, sub property or refining a child of the node.
A one-step refinement over the set of atom-trees returns a set of atom-trees by either
Refining a single node
Adding the most general form of a node
One-step refinement takes two atom-trees and returns atom-trees that are more similar forms of the second towards the first

INPUT : Session data containing semantic events
OUTPUT : List of frequent atom-trees
generate the initial candidate set using
getMostGeneralForm of all events
iterate until no candidates can be generated
{
compare candidate set with the data set
for each atom-tree in the data set
{
increment the frequency of each atom-tree in candidate set
that is more general than the atom-tree
}
filter the candidates that are less frequent that minSupport
generate next candidate set using oneStepRefinement operator
on the current candidate set atom-trees
}

Similar to GSP
The taxonomy introduced by isMoreGeneralThan relation is used
Data set is converted:
each frequent atom-tree is mapped to an integer hash
each atom-tree in a session is replaced by a set of hashes of the atom-tree and its ancestors
Subsequence relation is modified to respect set inclusion
08/28/11 Phase II: Find Frequent Sequences

INPUT : session data containing semantic events and frequent atom trees from phase one
OUTPUT : list of frequent atom-trees
convert data set and frequent atom-trees to hashes
while the candidate set is not empty
{
generate candidate set from previous frequent patterns
count candidates
select frequent candidates
}
reconvert patterns
08/28/11 Phase II: Find Frequent Sequences

Two sites are tested:
A music streaming site
Single-page, AJAX based music listening site
280K events in 75K sessions
Events are tracked via Java Script client
A mobile network operator’s site
Content-heavy, mostly static, high traffic web site
1M pageviews in 175K sessions
Events are extracted from access logs
08/28/11 Experiments

08/28/11 Music Streaming Site - Events

38.9% of the sessions: user made a search
9.3% of the sessions: user removed a song from her playlist
95.5% of the sessions: user made an action about a song
27.5% of the sessions: user added a song to playlist
139 frequent patterns are found
Frequent pattern of length 2 describes
a search is performed after playing a particular song
Frequent pattern of length 6 describes
sequential removal of songs from playlist
(due to the lack of ‘clear playlist’ button in the interface)
08/28/11 Music Streaming Site - Patterns

08/28/11
Two days of logs
More than 1 million pageviews occurred in 175K sessions.
Semi-automatically generated ontology
A total of 503 class
7-level hierarchy
Mobile Network Operator Site - Events

08/28/11 Mobile Network Operator Site - Ontology

10% of the sessions: at least one search action
38% of the sessions: page not categorized in the ontology is visited
71% of the sessions: user visits the home page (interesting)
Some other subjectively interesting patterns
user’s browsing behaviors between subclasses of content class
users visited home page then jumped to some specific content
users searched and moved on to specific category
08/28/11 Mobile Network Operator Site - Patterns

Proposed system is
More generic than some of the previous semantic web usage mining attempts
Captures usage model more correctly
Intuitive and sound
Uses most of the ontology constructs
Applicable to real web sites with varying domains
Parallelizable and suitable for MapReduce
08/28/11 Conclusions

Web Usage Miningand Using Ontology for Capturing Web Usage Semantic

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Web Usage Miningand Using Ontology for Capturing Web Usage Semantic Presentation Transcript