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Maintaining Knowledge-Bases of Navigational Patterns from Streams of

Navigational Sequences

Ajumobi Udechukwu, Ken Barker, Reda Alhajj

Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA’05)

Advisor ： Jia-Ling Koh

Speaker ： Chun-Wei Hsieh

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Introduction

Navigational patterns: traversal patterns

Two broad techniques for mining

navigational patterns– 1. level-wise, apriori-based techniques– 2. tree-based techniques

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Methodology

Sliding window Batch-update strategy

– Batch: the web log in the base time unit

Example

B1

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B1 B2B1 B2 B3B1 B2 B3 B4B1 B2 B3 B4 B5B1 B2 B3 B4 B5 B6

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Adapted GST

Adapted generalized suffix tree Appending a stop symbol to all strings Mining without thresholds

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1,12,1

1,22,2

1,32,3

LQ R$$R Q

R$

1,42,43,3

$

3,1

$

3,1

$

1,11,2

1,3

LQR$$RQ

R$

1,4

$

1,12,1

1,22,2

1,32,3

LQR$$RQ

R$

1,42,4

$

Adapted GST

LQR

1,1LQR$

1,11,2LQR$$RQ

1,11,2

1,3

LQR$$RQ

R$

LQR LQ

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Adapted GST

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The Challenge of Adapted GST

”LQ” occurs in B1 with support count of 4

and “L” occurs independently in B2 with support count of 2

Total count of “L” should be 4 + 2

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AC-NAP tree 1

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AC-NAP tree 2

Output all node labels and counts to a database

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Maintaining patterns within a window

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Maintaining patterns within a window

Count total support

Remove out_of_date patterns

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Experiments

OS: Microsoft Windows XP professional edition CPU: 2GHz Intel Pentium 4 RAM: 512MB Program language: Java DBMS: MySQL Data: real-world web logs of ”msnbc.com”

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Experiments

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Experiments

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Experiments