CHAPTER-7 EXPERIMENTS AND TEST RESULTS FOR PROPOSED PREDICTION MODEL

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Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 99

CHAPTER-7

EXPERIMENTS AND TEST

RESULTS FOR PROPOSED

PREDICTION MODEL

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This chapter will deal with all experiments are conducted through out the current

research. Prediction Model of web caching and perfecting consists of main three phases:

Preprocessing, Sessionization, Pattern discovery and analysis; this chapter will discuss all

experiments and associated results in all phases. Different tools and methods are used in

proposed research for different phases.

7.1 Preprocessing Experiments and Results

Preprocessing phase is experimented for current research and past research did by

many authors and then comparison is done based on both approaches. Number of tests is

conducted in this phase and they are narrated as under:

(1) Preprocessing Test-1:-



Test Description: - Parse row log file into appropriate fields of W3C Extended form.

Row log file is available at following path of personal computer

E:\Dharmendra\logexample\iis.log.



Result: - Sample of result of above test is available in Table 7.1.



Result Analysis: - Result got from above test is according to requirement of

proposed research. This result can be used for further processing. Total 5000 raw

are affected by this test.



Query used for: - In Microsoft Log Parser, appropriate environment has to set up to

execute query based on type of log data.

**Select * from e:\dharmendra\logexample\iis.log;**

Snapshot of Microsoft Visual Log Parser tool for test-1 is described in figure 7.1.

(2) Preprocessing Test-2 :-



Test Description: - Remove unnecessary web objects access by users.



Result: - Sample of result of above test is available in figure 7.2.



Result Analysis: - Result generated is perfect. This result can be used for further

processing. Total 2990 raw affected by above query from raw log file having 15 days

transactions.



Query used for: - Following query is executed to get result.

select LogFilename,date,time,c-ip,s-ip,cs-uri-stem,sc-status,time-taken from

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Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 101 '%.avi' and time-taken >= 600000) and( sc-status=200 or sc-status=304 or sc-status=306)) or ( (cs-uri-stem like '%.dat' and time-taken >= 600000)and( sc-status=200 or sc-status=304 or sc-status=306)) Figure 7.3 describes snapshot of tool with query and result of test-2.

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Table 7.1 Log Data in W3C Field Format

Log File Name Row Date Time C-ip s-site s-computer s-ip

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Table 7.1 Log Data in W3C Field Format(Continue)

Log File Name Row Date Time C-ip s-site s-computer s-ip

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(Figure 7.2 Filtered Log Entries)

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Preprocessing Test-3

:-

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

Result Analysis: - Result generated is perfect. This result can be used for further

processing. Total 490 unique web objects found from total 2990 web objects.



Query used for:- Following query is executed to get result.

select distinct cs-uri-stem, count(cs-uri-stem) from e:\dharmendra\logexample\iis.log

where (cs-uri-stem like '%.htm' and ( sc-status=200 or sc-status=304 or sc-status=306) )

or( cs-uri-stem like '%.asp' and ( sc-status=200 or sc-status=304 or sc-status=306))

Figure 7.3 Snapshot of Microsoft Visual Log Parser Tool for Test-2

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or ( (cs-uri-stem like '%.jpg' and time-taken >= 360000)and( sc-status=200 or sc-status=304 or

sc-status=306) )

or (( cs-uri-stem like '%.gif' and time-taken >= 360000) and( sc-status=200 or sc-status=304 or

sc-status=306) )

or (( cs-uri-stem like '%.avi' and time-taken >= 600000) and( sc-status=200 or sc-status=304 or

sc-status=306))

or ( (cs-uri-stem like '%.dat' and time-taken >= 600000)and( sc-status=200 or sc-status=304 or

sc-status=306)) group by cs-uri-stem

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Preprocessing Test-4

:-



Test Description: - To remove web objects which does not fulfill the condition of

threshold value.



Result: - Sample of result of above test is available in figure-7.5.



Result Analysis: - Total 120 raw is retrieved from above test, which fulfills condition

of threshold.



Query used for: - For this test 4, Microsoft excel tool is used. Following steps are

used to accomplish this test.

(i) First Max function is applied for data which is generated by test 3 to calculate

highest value of hit rate. Highest hit rate generated from data is 62.

= MAX (A1: A 491)

(ii) Threshold value is derived by following formula.

**= (62 * 0.10)**

(iii) Advanced filtered feature is used to filter only those records which Fulfill

condition of threshold value.

(iv) Lastly, records are arranged in descending order of hit ratio by sorting feature

of Microsoft excel.

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is analyzed that how binary objects are important in process of preprocessing. During

preprocessing stage one test is carried out to decide threshold value of binary objects like audio

and video.

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Preprocessing Test-5

:-



Test Description: - To decide threshold value of image and video file.



Tool used: - One online tool is used to determine load time of image and video.

Reference is

http://www.numion.com/calculators/time.html

.

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Preprocessing Accuracy

97.6 99.4 96 97 98 99 100 Models (% ) A c c ura c y Proposed Model Other Model Proposed Model 97.6 Other Model 99.4 Clean Accuracy (%)

(Figure 7.6 Preprocessing Accuracy)

Proportion of Objects

0 500 1000 1500 2000 Tests N um be r of O bj e c ts Text Objects Binary Objects Text Objects 1114 132 27 Binary Objects 1876 358 93

After Test 2 After Test 3 After Test 4

(Figure 7.7 Proportion of Text Objects and Binary Objects)

7.2 Sessionization Experiments and Results

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Table 7.2 Sessionization Result

Total Users

109 Total Unique IP

57 Total session

167 7.3 Pattern Discovery Experiments and Results

In proposed research, pattern discovery is done based on Markov Model and proposed

model. Markov Model accepts inputs as a web sessions and generates outputs in terms of

numbers of web objects based on appropriate ordering of model. There are number of tests are

carried out to generate appropriate output based on Markov Model.

7.3.1 Pattern Discovery Experiments based on Markov Model

[1] Markov Test-1:-



Test Description: - To generate occurrence matrix that determines occurrences of

particular web object from current state.



Result:- Occurrence Matrix is generated ( Refer Table 5.3 )



Tools Used: - Microsoft Excel Tool is used for this experiment. One Macro is

generating to determine number of occurrences.



Macro Code:- Following code is generated for that.

Sub Occurence1 ()

Dim c As Long

Dim r As Long

Dim max_col As Long

Dim max_row As Long

max_row = Sheet1.UsedRange.Rows.Count

max_col = Sheet1.UsedRange.Columns.Count

Dim values(50, 50) As Integer

For r = 1 To max_row

For c = 2 To max_col - 1

If (Sheet1.Cells(r, c) <> Sheet1.Cells(r, c + 1)) Then

values(Sheet1.Cells(r,

c).Value,

Sheet1.Cells(r,

c

+

1).Value)

=

values(Sheet1.Cells(r, c).Value, Sheet1.Cells(r, c + 1).Value) + 1

End If

Next c

For c = 1 To max_col

Sheet1.Cells(i, colval + 1).Value = values(i, c)

colval = colval + 1

End Sub

[2] Markov Test-2



Test Description: - To generate transition probability matrix based on current state.

In order to generate transition probability matrix number of tests is carried out.

(a) Test 1:- Determine summation of number of occurrences from current state

to all other states.



Tools Used:- Microsoft Excel



Query: - SUM(X: Y) Where X and Y are cell numbers.



Result: - It generates summation figure from current state to all

other states.

(b) Test 2:- Generate transition probability from current state to all other

states.



Tools Used:- Microsoft Excel



Query: - SUM(X: Y)/ N Where N is addition that is generated from

test-1.



Result: - It generates transition probability value of every cell from

one cell to another.

(c) Test 3:- To determine maximum value of transition probability in order to

predict next web object.



Tools Used:- Microsoft Excel



Query: - MAX(X: Y)



Result: - Prediction of Next Web Object.

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Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 113 Prediction Accuracy 0 10 20 30 40 50 60 70

First Second Third Fourth Fifth Sixth Seventh Eight Ninth Tenth Markov Chain Order

% A c c ura c y Series1

(Figure 7.8 Prediction Accuracy of Markov Orders)

Table 7.3 Markov Hit Ratio

Markov Chain

Hit Ratio

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Markovin Model Hit Ratio

-8 -6 -4 -2 0 2 4 6 8 10 Firs t Sec ond _Third Four th Fifth _Sixth Sev enth _Eight _Ninth _Tent h Markov Chains H it R a ti o Hit Ratio

(Figure 7.9 Markov Model Hit Ratio)

7.3.2 Pattern Discovery Experiments based on Proposed Model

In proposed model pattern discovery is done based on appropriate formation of web sessions.

To perform web sessions new approach is discovered in proposed research. According to new

approach web sessions are formed based on distance measurement techniques. Proposed

research identified several distance measurement techniques relevant to web caching and

prefetchning. Numbers of experiments are conducted for every distance measurement

techniques.

7.3.2.1 Experiments on Lavensthein Distance Measurement technique

[1] Lavensthein Test -1



Test Description: - To determine distance measure between web sessions according

to Lavensthein distance measurement technique.



Tool used: - One online tool is used to determine distance measure between web

sessions. Reference is

http://asecuritysite.com/forensics/simstring

.



Results: - One metric with distance value is generated as a result of this test.

[2] Lavensthein Test -2



Test Description: - To determine proximity of different web sessions according to

Lavensthein measurement technique.



Tool used: - Microsoft Excel tool is used to determine proximity based on

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

Results: - As results of this test number of sessions involved in each cluster is

determined based on particular threshold value.

[3] Lavensthein Test -3



Test Description: - To determine accuracy of pattern.



Tool used: - Microsoft Excel tool is used to determine accuracy of pattern. Accuracy

of pattern is determine by taking average of each permutation combination web

session pair.



Results: - Accuracy value is generating for each pattern.

[4] Lavensthein Test-4



Test Description: - To determine mean and standard deviation in order to take

appropriate action.



Tool used: - Microsoft Excel tool is used to determine mean and standard deviation

of patterns generated at specific threshold value.



Results: - Mean and standard deviation of patterns are generated as a result of test.

Table 7.4 describes the conclusion of all above tests. Table describes threshold value, number

of web sessions in particular cluster, mean and standard deviation of all patterns.

Table 7.4 Patten Discovery based on Lavensthein Distance

Threshold Number of Clusters

Sessions Involved in each cluster

Web Objects Referred in that Accuracy of pattern

50 22 1,10 2,5,7,8,9,10,12,13,14,15 55

2,14,15 6,8,9,12,15,5,1,7,10,2,4,14,3 38

3,9,18,23 6,4,5,7,9,10,11,12,15,14,13,8,2,3,13,1 55.66

4,7,11,12,15,17 2,3,4,6,9,11,12,14,15,8,10,5,7,10

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Table 7.4 Pattern Discovery based on Lavensthein Distance(Continue) Threshold Number of

Clusters

Web Objects Referred in that Accuracy of pattern 5,13,19 3,6,9,11,12,13,14,15,2,10,5,7,8 68.33 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 79 7,4,15 2,4,6,8,9,10,12,14,15,3,1 60.33 8,20 7,6,5,2,1,9,10,12,14,11,13 88 9,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8,2 49 10,1,15 2,5,7,8,9,10,4,6,12,14,15,3,1 46 11,4,12,15,17 2,4,6,8,9,10,12,14,15,3, 5,11,15,1,7 72.5 12,4,11,15,17 2,4,6,8,9,10,12,14,15,3,1,7 72.5 13,5,19 5,7,9,11,12,13,14,15,2,3,8,6 68.33 14,2 6,8,9,12,15,2,5 50 15,2,4,7,10,11,12,17 6,8,9,12,15,2,5,4,10,14,3,11, 7,13,

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Clusters

Web Objects Referred in that Accuracy of pattern 16,6 3,8,7,9,4,6,10,11,12,13,15 79 17,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11,1 72.5 18,3,9,25 3,4,5,6,7,9,10,11,12,15,14,13,8,2,1 56.83 19,5,13, 5,7,9,11,12,13,14,15,2,3, 6,10 68.33 20,8 7,6,5,2,1,9,10,12,14,11 88 23,3 3,4,5,6,7,9,10,11,12,15,14,13 50 25,18 8,9,10,2,3,4,5,6,7,11,12,15,14,13 56 Standard Deviation 13.63 Mean 63.13 55 18 1,10 2,5,7,8,9,10,12,13,14,15,10 55 3,9,18 6,4,5, 7,9,10,11,12,15,14,13, 8,2,3,6 70 4,11,12,15,17 2,4,6,8,9,10,12,14,15,3,5,11,1,7 72.5 5,13,19 3,6,9,11,12,13,14,15,2,10,5,7,8 68.33 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15

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Clusters

Web Objects Referred in that Accuracy of pattern 8,20 7,6,5,2,1,9,10,12,14,11,13 88 9,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8,2 70 10,1 2,5,7,8,9,10 55 11,4,12,15,17 2,4,6,8,9,10,12,14,15,3,5,11,2,1,7 72.5 12,4,11,17 2,4,6,8,9,10,12,14,15,3,7 78.16 13,5,19 5,7,9,11,12,13,14,15,2,3,2,3,8,6 68.33 15,4,11,17 2,4,6,8,9,10,12,14,15,3 77 16,6 3,8,7,9,4,6,10,11,12,13,15 79 17,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11,1 72.5 18,3,9,25 3,4,5,6,7,9,10,11,12,15,14,13,8,2,1 56.83 19,5,13 5,7,9,11,12,13,14,15,2,3, 6,10 68.33 20,8 7,6,5,2,1,9,10,12,14,11 88 25,18 8,9,10,2,3,4,5,6,7,11,12,15,14,13 56 Standard Deviation 10.17 Mean

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Clusters

Web Objects Referred in that Accuracy of pattern 60 15 3,9,18 6,4,5, 7,9,10,11,12,15,14,13, 8,2,3,6 70 4,11,12,15,17 2,4,6,8,9,10,12,14,15,3,5,11,1,7 72.5 5,13,19 3,6,9,11,12,13,14,15,2,10,5,7,8 68.33 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 79 8,20 7,6,5,2,1,9,10,12,14,11,13 88 9,3 3,4,5,6,7,9,10,11,12,15,14,13 79 11,4,12,15,17 2,4,6,8,9,10,12,14,15,3,5,11,2,1,7 72.5 12,4,11,17 2,4,6,8,9,10,12,14,15,3,7 78.16 13,5 5,7,9,11,12,13,14,15,2,3 69 15,4,11,17 2,4,6,8,9,10,12,14,15,3 77 16,6 3,8,7,9,4,6,10,11,12,13,15 79 17,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11,1 72.5 18,3 3,4,5,6,7,9,10,11,12,15,14,13 75 19,5 5,7,9,11,12,13,14,15,2,3 79 20,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 6.02 Mean

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Clusters

Web Objects Referred in that Accuracy of pattern 65 15 3,9,18 6,4,5, 7,9,10,11,12,15,14,13, 8,2,3,6 70 4,11,12,15,17 2,4,6,8,9,10,12,14,15,3,5,11,1,7 72.5 5,13,19 3,6,9,11,12,13,14,15,2,10,5,7,8 68.33 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 79 8,20 7,6,5,2,1,9,10,12,14,11,13 88 9,3 3,4,5,6,7,9,10,11,12,15,14,13 79 11,4,12,17 2,4,6,8,9,10,12,14,15,3, ,5, 11,15,2,,7 78.16 12,4,11,17 2,4,6,8,9,10,12,14,15,3,7 78.16 13,5 5,7,9,11,12,13,14,15,2,3 69 15,4,17 2,4,6,8,9,10,12,14,15,3, 7 77 16,6 3,8,7,9,4,6,10,11,12,13,15 79 17,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11,1 72.5 18,3 3,4,5,6,7,9,10,11,12,15,14,13 75 19,5 5,7,9,11,12,13,14,15,2,3 79 20,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 5.93 Mean 76.84 70 14 3,9,18 6,4,5, 7,9,10,11,12,15,14,13, 8,2,3,6 70 4,11,12,15,17 2,4,6,8,9,10,12,14,15,3,5,11,1,7 72.5 5,19 5,7,9,11,12,13,14,15,2,3,8,6 79 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15

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Clusters

Web Objects Referred in that Accuracy of pattern 8,20 7,6,5,2,1,9,10,12,14,11,13 88 9,3 3,4,5,6,7,9,10,11,12,15,14,13 79 11,4,17 2,4,6,8,9,10,12,14,15,3,7 85.33 12,4,17 2,4,6,8,9,10,12,14,15,3,7 77 15,4 2,4,6,8,9,10,12,14,15,3 77 16,6 3,8,7,9,4,6,10,11,12,13,15 79 17,4,11,12 2,4,6,8,9,10,12,14,15,3,5,11,15,3,9,8,6,10 78.16 18,3 3,4,5,6,7,9,10,11,12,15,14,13 75 19,5 5,7,9,11,12,13,14,15,2,3 79 20,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 5.18 Mean 78.99 75 13 3,9,18 6,4,5, 7,9,10,11,12,15,14,13, 8,2,3,6 70 4,11,15,17 2,4,6,8,9,10,12,14,15,3,1,7 77 5,19 5,7,9,11,12,13,14,15,2,3,8,6 79 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 79 8,20 7,6,5,2,1,9,10,12,14,11,13

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Clusters

Web Objects Referred in that Accuracy of pattern 9,3 3,4,5,6,7,9,10,11,12,15,14,13 79 11,4,17 2,4,6,8,9,10,12,14,15,3,7 85.33 15,4 2,4,6,8,9,10,12,14,15,3 77 16,6 3,8,7,9,4,6,10,11,12,13,15 79 17,4,11 2,4,6,8,9,10,12,14,15,3 85.33 18,3 3,4,5,6,7,9,10,11,12,15,14,13 75 19,5 5,7,9,11,12,13,14,15,2,3 79 20,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 5.26 Mean 80.05 80 5 4,11,17 2,4,6,8,9,10,12,14,15,3,7 85.33 8,20 7,6,5,2,1,9,10,12,14,11,13 88 11,4,17 2,4,6,8,9,10,12,14,15,3,7 85.33 17,4,11 2,4,6,8,9,10,12,14,15,3 85.33 20,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 1.46 Mean 86.398

7.3.2.2 Experiments on Needleman Wunsch Distance Measurement technique

[1] Needleman Wunsch Test -1

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

Tool used: - One online tool is used to determine distance measure between web

sessions. Reference is

http://asecuritysite.com/forensics/simstring

.



Results: - One metric with distance value is generated as a result of this test.

[2] Needleman Wunsch Test -2



Test Description: - To determine proximity of different web sessions according to

Needleman Wunsch measurement technique.



Tool used: - Microsoft Excel tool is used to determine proximity based on

conditional formatting option. Metric generated in previous test result is used as an

input.



Results: - As results of this test number of sessions involved in each cluster is

determined based on particular threshold value.

[3] Needleman Wunsch Test -3



Test Description: - To determine accuracy of pattern.



Tool used: - Microsoft Excel tool is used to determine accuracy of pattern. Accuracy

of pattern is determined by taking average of each permutation combination web

session pair.



Results: - Accuracy value is generating for each pattern.

[4] Needleman Wunsch Test-4



Test Description: - To determine mean and standard deviation in order to take

appropriate action.



Tool used: - Microsoft Excel tool is used to determine mean and standard deviation

of patterns generated at specific threshold value.



Results: - Mean and standard deviation of patterns are generated as a result of test.

Table 7.5 describes the conclusion of all above tests according to Needleman Wunsch distance

measurement technique. Table describes all fields that are generated as a result of all above

tests.

Table 7.5 Patten Discovery based on Needleman Wunsch Distance

Thres hold

Number of Clusters

Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern 55 25 1,10,11,12,14,21,23,25 2,5,7,8,9,10,12,13,14,15,4,3,

,6,1,11

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Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue)

Thres hold

Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern 2,3,5,7,10,15,18,24,25 3,4,5,6,7,9,10,11,12,15,14,13, 2,8,1 58.8 3,2,6,7,8,9,10,12,15,16,18,20,23,25 6,8,9,12,15,2,5,3,7, 4,10,11,13, 14,1 58.30 4,5,7,8,9,10,11,12,13,15,17,19,20,22, 24 5,7,9,11,12,13,14,15,2,3,14,4,6,8, 1,10 59 5,2,4,9,10,12,13,15,19,20,24 6,8,9,12,15,2,5,4,10,14,3, 7,11, 14,13,11,12 59.01 6,3,7,9,10,16,18,20,23 3,4,5,6,7,9,10,11,12,15,14,13,2,8, 13,1 60.36 7,2,3,4,6,8,10,15,16,18,19,20,21,22,2 3,24 6,8,9,12,15,2,3,4,5,7,10,11,14,13, 1, 2, 56.16 8,3,4,7,9,10,15,18,20,24 3,4,5,6,7,9,10,11,12,15,14,13,2, ,8 ,1 61.68 9,3,4,5,6,8,10,11,12,14,15,16,17,18,2 0,24 3,4,5,6,7,9,10,11,12,15,14,13,2,,8 ,1 58.83 10,1,2,3,4,5,6,7,8,9,12,15,17,18,20,24 2,5,7,8,9,10,6, 12,15,3,4,11,14,13,3,1

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Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue)

Thres hold

Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern 11,1,4,9,12,14,15,17,19 2,5,7,8,9,10,4,6,12,14,15,3,11,13, 1 63.23 12,1,3,4,5,9,10,11,14,15,17,20 2,5,7,8,9,10,3,4,6,11,12,15,14,13, 1 61.05 13,4,5,17,19 2,4,6,8,9,10,12,14,15,3,,5,7,11,13 , 66.7 14,1,9,11,12,17,19,25 2,5,7,8,9,10,6,4,5,11,12,15,14,13, 3,1 60.07 15,2,3,4,5,7,8,9,10,11,12,16,17,18,20, 24 6,8,9,12,15,2,5,3,4,7,10,11,14,13, 2,1,12 59.02 16,3,6,7,9,15,18,20,25 3,4,5,6,7,9,10,11,12,15,14,13,8,2, 1 61.30 17,4,9,10,11,12,13,14,15,19,21,25 2,4,6,8,9,10,12,14,15,3,5,7,,11,13 ,,15,1 59.5 18,2,3,6,7,8,9,10,15,16,20,23,25 6,8,9,12,15,2,5,3,4,7,11,14,13,3,, 4,10,13,12,1

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Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue)

Thres hold

Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern 19,4,5,7,11,13,14,17,21 2,4,6,8,9,10,12,14,15,3,5,7,11,13, ,1 59.5 20,3,4,5,6,7,8,9,10,12,15,16,18,25 3,4,5,6,7,9,10,11,12,15,14,13,2, ,8,1 59.49 21,1,7,17,19,25 2,5,7,8,9,10,3,4,6 ,11,12,14,15,13,1, 56.06 22,4,7 2,4,6,8,9,10,12,14,15,3,11 58.33 23,1,3,6,7,18,24,25 2,5,7,8,9,10,3,4,6,11,12,15,14,13, 1 57.75 24,2,4,5,7,8,9,10,15,23 6,8,9,12,15,2,5,4 ,10,14,15,3,7,11,13,1 58.13 25, 1,2,3,6,14,16,17,18,20,21,23 2,5,7,8,9,10,6,12,15,3,4,11, 14,13,1,,3 55.69 Standard Deviation 2.36368829 Mean 59.2616 60 23 1,14 6,8,9,12,15,2,5,1,7,10 71 2,7,15,24 2,3,4,6,9,11,12,14,15,8, 10,1,13,1

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Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue)

Thres hold

Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern 3,6,9,10,18,25 3,8,7,9,4,6,10,11,12,13,15,,5,14,2 ,1 64 4,5,7,9,10,11,12,15,17,20 5,7,9,11,12,13,14,15,2,3,,4,6, 8,10,1 62.84 5,4,13,19 2,4,6,8,9,10,12,14,15,3, 11, 13,5,7 68.5 6,3,7,16,25 3,4,5,6,7,9,10,11,12,15,14,13,2,8, 1, 63.6 7,2,4,6,8,10,15,16,18 6,8,9,12,2,5,4,10 ,14,15,3, 7,11, 13,1,14, ,2 59.69 8,7,10,15,20 2,3,4,6,9,11,12,14,15,8,5,7,10, 13,1 64.8 9,3,4,10,12,15,18 3,4,5,6,7,9,10,11,12,15,14,13,2,8, 1 65.47 10,3,4,7,8,9,15 3,4,5,6,7,9,10,11,12,15,14,13,2,8, 1 63 11,4,12,14,15,17 2,4,6,8,9,10,12,14,15,3,,5,11,,1,7

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Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue)

Thres hold

Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern 12,4,9,11,15,17 2,4,6,8,9,10,12,14,15,3,,5,7,11,13 ,1 73.26 13,5,19 5,7,9,11,12,13,14,15,2,3,,8,6 77 14,1,11 2,5,7,8,9,10,4,6, ,12,14,15,3 66.33 15,2,4,7,8,9,10,11,12,17,20,24 6,8,9,12,15,2,5,4, ,10,14,3,11, ,7, 1,,14,,13,2,1 61.42 16,6,7,18,25 3,8,7,9,4,6,10,11,12,13,15,2,,14, ,5, 1 64.6 17,4,11,12,15,19,25 2,4,6,8,9,10,12,14,15,3,,5,11,1,7,, 13 66.52 18,3,7,9,16,25 3,4,5,6,7,9,10,11,12,15,14,13,2, 8,1 63.66 19,5,13,17 5,7,9,11,12,13,14,15,2,3,,6,10,4,8 ,7 66.66 20,4,8,15 2,4,6,8,9,10,12,14,15,3,7,5,2,1, 11 70.33 23,25 8,2,1,3,4,5,7,9,10,11,12,13 65 24,2,15 6,8,9,12,15,2,5,4,10,14,3,2,1

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Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue)

Thres hold

Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern 25,3,6,16,17,18,23 3,4,5,6,7,9,10,11,12,15,14,13,8,,2 ,1 61.42 Standard Deviation _4.50 Mean 65.89 65 21 1,14 6,8,9,12,15,2,5,1,7,10 71 3,9,18,25 6,4,5,,7,9,10,11,12,15,14,13,8,2,3 ,1 71 4,5,11,12,15,17,20 5,7,9,11,12,13,14,15,2,3,4,6,8,10, 1 67.19 5,4,13,19 2,4,6,8,9,10,12,14,15,3, 11, 13,5,7 68.5 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 89 7,10 2,5,7,8,9,10,12,13,14,15 68 8,20 7,6,5,2,1,5,6,9,10,12,14,11,10,9,1 3,5 88 9,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8, ,2 80.66 10,7,15 2,3,4,6,9,11,12,14,15,8,10,1 65.66 11,4,12,14,15,17 2,4,6,8,9,10,12,14,15,3,,5,11,,1,7

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Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue)

Thres hold

Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern 12,4,11,15,17 2,4,6,8,9,10,12,14,15,3,1, 7 79.7 13,5,19 5,7,9,11,12,13,14,15,2,3,,8,6 77 14,1,11 2,5,7,8,9,10,4,6, ,12,14,15,3 66.33 15,4,10,11,12,17 2,4,6,8,9,10,12,14,15,3,5,7, 13, ,11 71.6 16,6 3,8,7,9,4,6,10,11,12,13,15 89 17,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11,15,3, 1 77.8 18,3,9,25 3,4,5,6,7,9,10,11,12,15,14,13,,8,2 ,1 71 19,5,13 5,7,9,11,12,13,14,15,2,3,6,10 77 20,4,8 2,4,6,8,9,10,12,14,15,3, 7,5,1,11 71.33 23,25 8,2,1,3,4,5,7,9,10,11,12,13 65 25,3,18,23 3,4,5,6,7,9,10,11,12,15,14,13,8, 2,1 66.83 Standard Deviation 7.67 Mean 74.33 70 17 1,14 6,8,9,12,15,2,5,1,7,10

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Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue)

Thres hold

Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern 75 15 3,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3, 13 80.66 4,11,12,15,17 2,4,6,8,9,10,12,14,15,3, ,5, 11, 1,7 77.8 5,13,19 3,6,9,11,12,13,14,15,2,14,10,5,7, 15,8 77 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 89 8,20 7,6,5,2,1,5,6,9,10,12,14,11,10,9,1 3,5 88 9,3 3,4,5,6,7,9,10,11,12,15,14,13 82 11,4,12,17 2,4,6,8,9,10,12,14,15,3,5, 11,7 83.66 12,4,11,17 2,4,6,8,9,10,12,14,15,3,,7 83.66 13,5,19 5,7,9,11,12,13,14,15,2,3,,8,6 77 15,4 2,4,6,8,9,10,12,14,15,3 85 16,6 3,8,7,9,4,6,10,11,12,13,15 89 17,4,11,12 2,4,6,8,9,10,12,14,15,3, 5, 11 83.66 18,3 3,4,5,6,7,9,10,11,12,15,14,13 88 19,5,13 5,7,9,11,12,13,14,15,2,3,6,10 77 20,8 7,6,5,2,1,9,10,12,14,11

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Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue)

Thres hold

Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern Standard Deviation 4.57 Mean 83.29 80 12 3,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3, 13 80.66 4,11,12,15,17 2,4,6,8,9,10,12,14,15,3, ,5, 11, 1,7 77.8 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 89 8,20 7,6,5,2,1,5,6,9,10,12,14,11,10,9,1 3,5 88 9,3 3,4,5,6,7,9,10,11,12,15,14,13 82 11,4,17 2,4,6,8,9,10,12,14,15,3, 7 86.33 12,4,17 2,4,6,8,9,10,12,14,15,3, 7 84 15,4 2,4,6,8,9,10,12,14,15,3 85 16,6 3,8,7,9,4,6,10,11,12,13,15 89 17,4,11,12 2,4,6,8,9,10,12,14,15,3, 5, 11 83.66 18,3 3,4,5,6,7,9,10,11,12,15,14,13 88 20,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 3.60 Mean 85.12 85 10 3,18 8,9,10,2,3,4,5,6,7,,11,12,15,14,13 88 4,15,17 2,4,6,8,9,10,12,14,15,3,1,7

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Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue)

Thres hold

Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 89 8,20 7,6,5,2,1,5,6,9,10,12,14,11,10,9,1 3,5 88 11,17 2,4,6,8,9,10,12,14,15,3,7 91 15,4 2,4,6,8,9,10,12,14,15,3 85 16,6 3,8,7,9,4,6,10,11,12,13,15 89 17,4,11 2,4,6,8,9,10,12,14,15,3 86.33 18,3 3,4,5,6,7,9,10,11,12,15,14,13 88 20,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 2.56 Mean 87.39 90 2 11,17 2,4,6,8,9,10,12,14,15,3,7 91 17,11 2,4,6,8,9,10,12,14,15,3,7 91 Standard Deviation 0 Mean 91

7.3.2.3 Experiments on Smith Waterman Distance Measurement technique

[1] Smith Waterman Test -1



Test Description: - To determine distance measure between web sessions according

to Lavensthein distance measurement technique.



Tool used: - One online tool is used to determine distance measure between web

sessions. Reference is

http://asecuritysite.com/forensics/simstring

.



Results: - One metric with distance value is generated as a result of this test.

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

Test Description: - To determine proximity of different web sessions according to

Smith Waterman measurement technique.



Tool used: - Microsoft Excel tool is used to determine proximity based on

conditional formatting option. Metric generated in previous test result is used as an

input.



Results: - As results of this test number of sessions involved in each cluster is

determined based on particular threshold value.

[3] Smith Waterman Test -3



Test Description: - To determine accuracy of pattern.



Tool used: - Microsoft Excel tool is used to determine accuracy of pattern. Accuracy

of pattern is determined by taking average of each permutation combination web

session pair.



Results: - Accuracy value is generating for each pattern.

[4] Smith Waterman Test-4



Test Description: - To determine mean and standard deviation in order to take

appropriate action.



Tool used: - Microsoft Excel tool is used to determine mean and standard deviation

of patterns generated at specific threshold value.



Results: - Mean and standard deviation of patterns are generated as a result of test.

Table 7.6 describes the conclusion of all above tests according to Smith Waterman distance

measurement technique. Table describes all fields that are generated as a result of all above

tests. Figure 7.10 describes pattern accuracy based on all distance measurement techniques

used in proposed work. Result shows that Smith Waterman distance measurement techniques

reach to 100 percent accuracy level. Figure 7.11 describes hit ratio based on Lavensthein

distance measurement technique. Figure 7.12 shows the hit ratio results based on Needleman

Wunsch distance measurement technique. Figure 7.13 describes results of hit ratio based on

Smith Waterman distance measurement technique. From the results of hit ratio it is derived

that Smith Waterman distance measurement technique gives an ideal value of hit ratio that is

nearer to 1.

Table 7.6 Patten Discovery based on Smith Waterman Distance

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

50 24 1,3,4,9,10,11,12,14,15,17,18,25 3,4,5,6,7,9,10,11,12,15,14,13,2,8

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Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

2,4,11,14,15,17 2,4,6,8,9,10,12,14,15,3,,5,1,7 65.64 3,1,7,9,18,23,25 2,5,7,8,9,10,3,4,6,11,12,14,15,13,3, 1 52.38 4,1,2,7,10,11,12,15,17 2,5,7,8,9,10,6,12,15,3,4,11,14,13,3,1 55.61 5,10,13,19 2,5,7,8,9,10,12,13,14,15,3,6,11 61.88 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 100 7,3,4,11,13,15,17,18 3,4,5,6,7,9,10,11,12,15,14,13,2,,8 ,1 49.85 8,20,21 7,6,5,2,1,9,10,12,14,11,13,5,3 71.33 9,1,3,18 2,5,7,8,9,10,3,4,6,11,12,15,14,13 70.16 10,1,4,5,11,14,15,17,19,25 2,5,7,8,9,10,4,6,,12,14,15,3,,11,13,1 49.55 11,1,2,4,7,10,12,15,17 2,5,7,8,9,10,6,12,15,,4,,14,3,1113, 1 55.61 12,1,4,11,17 2,5,7,8,9,10,4,6,,12,14,15,3 67.5 13,5,7,19 5,7,9,11,12,13,14,15,2,3,14,,4,6,,8

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Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

14,1,2,10 2,5,7,8,9,10,6,,12,15,,13,14 70.66 15,1,2,4,7,10,11,17 2,5,7,8,9,10,6,12,15,,4,14,3,,11,13 57.78 16,6 3,8,7,9,4,6,10,11,12,13,15 100 17,1,2,4,7,10,11,12,15 2,5,7,8,9,10,6,12,15,4,14,3,11,13,1 55.61 18,1,3,7,9,23,25 2,5,7,8,9,10,3,4,6,,11,12,15,14,13,1 52.38 19,5,10,13 5,7,9,11,12,13,14,15,2,3,8,10,,6,9,14 61.88 20,8,21 7,6,5,2,1,,9,10,12,14,11,3 71.33 21,8,20 7,6,5,2,1,,9,10,12,14,11,3 71.33 22,23 3,4,5,6,8,1,11,12 57 23,3,18,22 3,4,5,6,7,9,10,11,12,15,14,13,8,2 57.16 25,1,3,10,18 2,5,7,8,9,10,3,4,6,,11,12,15,14,13 62.2

Standard Deviation

13.40 Mean

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Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

55 24 1,10,12,14,25 2,5,7,8,9,10,12,13,14,15,3,4,11,6,1 65.12 2,4,11,14,15,17 2,4,6,8,9,10,12,14,15,3,,5,1,7 65.64 3,9,18,25 6,4,5,7,9,10,11,12,15,14,13,8,2,3,1 75 4,2,11,12,15,17 6,8,9,12,15,2,5,4,10,14,3,11,1,7 69.06 5,13,19 3,6,9,11,12,13,14,15,2,10,5,7,8 78.33 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 100 7,13,18 3,6,9,11,12,13,14,15,2,10,8,4,5,7 47.66 8,20,21 7,6,5,2,1,9,10,12,14,11,13,5,3 71.33 9,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8,2 90.33 10,1,14,25 2,5,7,8,9,10,6,12,15,1,3,4,11,13 73.83 11,2,4,12,15,17 6,8,9,12,15,2,5,4,10,14,3,11,2,14,1,7 69.06 12,1,4,11,17 2,5,7,8,9,10,4,6,,12,14,15,3 67.5 13,5,7,19 5,7,9,11,12,13,14,15,2,3,14,,4,6,,8 64.16 14,1,2,10 2,5,7,8,9,10,6,,12,15,,13,14 70.66 15,2,4,11,17 6,8,9,12,15,2,5,4,10,14,3,7

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Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

16,6 3,8,7,9,4,6,10,11,12,13,15 100 17,2,4,11,12,15 6,8,9,12,15,2,5,4,10,,14,3,11,4,1 69.06 18,3,7,9,25 3,4,5,6,7,9,10,11,12,15,14,13,2,8,1 61.3 19,5,13 5,7,9,11,12,13,14,15,2,3,6,10 78.33 20,8,21 7,6,5,2,1,,9,10,12,14,11,3 71.33 21,8,20 7,6,5,2,1,,9,10,12,14,11,3 71.33 22,23 3,4,5,6,8,1,11,12 57 23,22 3,4,5,6,8,1,11,12 57 25,1,3,10,18 2,5,7,8,9,10,3,4,6,,11,12,15,14,13 62.2

Standard Deviation

12.27 Mean

71.34

60 21 1,10,14,25 2,5,7,8,9,10,12,13,14,15,6,1,3,4,11 72.5 2,14,15 6,8,9,12,15,2,5,1,7,10,4,14,3 67.33 3,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3 90.33 4,11,12,15,17 2,4,6,8,9,10,12,14,15,3,5,11,3,1,7 75.8 5,13,19 3,6,9,11,12,13,14,15,2,10,5,7,8 78.33 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 100 7,13,18 3,6,9,11,12,13,14,15,2,10,8,4,5,7 47.66 8,20 7,6,5,2,1,9,10,12,14,11,13

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Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

9,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8,2 90.33 10,1 2,5,7,8,9,10 100 11,4,15,17 2,4,6,8,9,10,12,14,15,3,1,7 84.71 12,4,17 2,4,6,8,9,10,12,14,15,3,7 74.33 13,5,7,19 5,7,9,11,12,13,14,15,2,3,14,,4,6,8 64.16 14,1,2 2,5,7,8,9,10,6,12,15 77.66 15,2,4,11,17 6,8,9,12,15,2,5,4,10,14,3,7 73.8 16,6 3,8,7,9,4,6,10,11,12,13,15 100 17,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11,3,1,7 75.8 18,3,7,9 3,4,5,6,7,9,10,11,12,15,14,13,2,8 70.16 19,5,13 5,7,9,11,12,13,14,15,2,3,6,10 78.33 20,8 7,6,5,2,1,9,10,12,14,11 100 25,1 2,5,7,8,9,10 83

Standard Deviation

14.04 Mean

81.15

65 19 1,10,14,25 2,5,7,8,9,10,12,13,14,15,6,1,3,4,11 72.5 2,14 6,8,9,12,15,2,5,7,10 100 3,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3 90.33 4,11,15,17 2,4,6,8,9,10,12,14,15,3,1,7

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Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

5,13,19 3,6,9,11,12,13,14,15,2,10,5,7,8 78.33 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 100 8,20 7,6,5,2,1,9,10,12,14,11,13 100 9,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8,2 90.33 10,1 2,5,7,8,9,10 100 11,4,15,17 2,4,6,8,9,10,12,14,15,3,1,7 84.71 13,5 5,7,9,11,12,13,14,15,2,3 73 14,1,2 2,5,7,8,9,10,6,12,15 77.66 15,4,11,17 2,4,6,8,9,10,12,14,15,3,7 84.71 16,6 3,8,7,9,4,6,10,11,12,13,15 100 17,4,11,15 2,4,6,8,9,10,12,14,15,3,1,7 84.71 18,3,9 3,4,5,6,7,9,10,11,12,15,14,13 90.33 19,5 5,7,9,11,12,13,14,15,2,3 100 20,8 7,6,5,2,1,9,10,12,14,11 100 25,1 2,5,7,8,9,10 83

Standard Deviation

9.80 Mean

89.17

70 19 1,10,14,25 2,5,7,8,9,10,12,13,14,15,6,1,3,4,11 72.5 2,14 6,8,9,12,15,2,5,7,10 100 3,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3

( Table Continue to next page)

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Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

4,11,15,17 2,4,6,8,9,10,12,14,15,3,1,7 84.71 5,13,19 3,6,9,11,12,13,14,15,2,10,5,7,8 78.33 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 100 8,20 7,6,5,2,1,9,10,12,14,11,13 100 9,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8,2 90.33 10,1 2,5,7,8,9,10 100 11,4,15,17 2,4,6,8,9,10,12,14,15,3,1,7 84.71 13,5 5,7,9,11,12,13,14,15,2,3 73 14,1,2 2,5,7,8,9,10,6,12,15 77.66 15,4,11,17 2,4,6,8,9,10,12,14,15,3,7 84.71 16,6 3,8,7,9,4,6,10,11,12,13,15 100 17,4,11,15 2,4,6,8,9,10,12,14,15,3,1,7 84.71 18,3,9 3,4,5,6,7,9,10,11,12,15,14,13 90.33 19,5 5,7,9,11,12,13,14,15,2,3 100 20,8 7,6,5,2,1,9,10,12,14,11 100 25,1 2,5,7,8,9,10 83

Standard Deviation

9.80 Mean

89.17

75 18 1,10,14,25 2,5,7,8,9,10,12,13,14,15,6,1,3,4,11 72.5 2,14 6,8,9,12,15,2,5,7,10 100 3,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3

( Table Continue to next page)

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Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

4,11,15,17 2,4,6,8,9,10,12,14,15,3,1,7 84.71 5,13,19 3,6,9,11,12,13,14,15,2,10,5,7,8 78.33 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 100 8,20 7,6,5,2,1,9,10,12,14,11,13 100 9,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8,2 90.33 10,1 2,5,7,8,9,10 100 11,4,15,17 2,4,6,8,9,10,12,14,15,3,1,7 84.71 14,1,2 2,5,7,8,9,10,6,12,15 77.66 15,4,11,17 2,4,6,8,9,10,12,14,15,3,7 84.71 16,6 3,8,7,9,4,6,10,11,12,13,15 100 17,4,11,15 2,4,6,8,9,10,12,14,15,3,1,7 84.71 18,3,9 3,4,5,6,7,9,10,11,12,15,14,13 90.33 19,5 5,7,9,11,12,13,14,15,2,3 100 20,8 7,6,5,2,1,9,10,12,14,11 100 25,1 2,5,7,8,9,10 83

Standard Deviation

9.25 Mean

90.07

80 18 1,10,14,25 2,5,7,8,9,10,12,13,14,15,6,1,3,4,11 72.5 2,14 6,8,9,12,15,2,5,7,10 100 3,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3 90.33 4,11,15,17 2,4,6,8,9,10,12,14,15,3,1,7

( Table Continue to next page)

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Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

5,19 5,7,9,11,12,13,14,15,2,3,8,6 100 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 100 8,20 7,6,5,2,1,9,10,12,14,11,13 100 9,3 3,4,5,6,7,9,10,11,12,15,14,13 92 10,1 2,5,7,8,9,10 100 11,4,15,17 2,4,6,8,9,10,12,14,15,3,1,7 84.71 14,1,2 2,5,7,8,9,10,6,12,15 77.66 15,4,11,17 2,4,6,8,9,10,12,14,15,3,7 84.71 16,6 3,8,7,9,4,6,10,11,12,13,15 100 17,4,11,15 2,4,6,8,9,10,12,14,15,3,1,7 84.71 18,3 3,4,5,6,7,9,10,11,12,15,14,13 100 19,5 5,7,9,11,12,13,14,15,2,3 100 20,8 7,6,5,2,1,9,10,12,14,11 100 25,1 2,5,7,8,9,10 83

Standard Deviation

9.26 Mean

91.90

85 16 1,10,14 2,5,7,8,9,10,12,13,14,15,6,1 85 2,14 6,8,9,12,15,2,5,7,10 100 3,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3 90.33 4,11,17 2,4,6,8,9,10,12,14,15,3,7

( Table Continue to next page)

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Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

5,19 5,7,9,11,12,13,14,15,2,3,8,6 100 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15 100 8,20 7,6,5,2,1,9,10,12,14,11,13 100 9,3 3,4,5,6,7,9,10,11,12,15,14,13 92 10,1 2,5,7,8,9,10 100 11,4,17 2,4,6,8,9,10,12,14,15,3,7 95.66 14,1,2 2,5,7,8,9,10,6,12,15 77.66 16,6 3,8,7,9,4,6,10,11,12,13,15 100 17,4,11 2,4,6,8,9,10,12,14,15,3 95.66 18,3 3,4,5,6,7,9,10,11,12,15,14,13 100 19,5 5,7,9,11,12,13,14,15,2,3 100 20,8 7,6,5,2,1,9,10,12,14,11 100

Standard Deviation

6.57 Mean

95.74

90 16 1,10,14 2,5,7,8,9,10,12,13,14,15,6,1 85 2,14 6,8,9,12,15,2,5,7,10 100 3,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3 90.33 4,11,17 2,4,6,8,9,10,12,14,15,3,7 95.66 5,19 5,7,9,11,12,13,14,15,2,3,8,6 100 6,16 5,6,2,3,8,7,9,4,10,11,12,13,15

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Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

Thres

hold

Number

of

Clusters

Sessions Involved in

each cluster

Web Objects Referred in that

Accuracy of pattern

(49)

Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue)

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Pattern Accuracy based on Distance Metric

0 20 40 60 80 100 120 Thre shol d Va lue 50 55 60 65 70 75 80 85 90 95 ₁₀0 Threshold Value A c c ura c y of P a tt e rn Levensthtein Needleman Wunsch Smith Waterman

(Figure 7.10 Pattern Accuracy based on Distance Metric)

Lavensthein Hit Ratio

1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 50 60 70 80 90 100 Threshold Value H it R a ti o Hit Ratio

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Needleman Wunsch Hit Ratio

1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 50 55 60 65 70 75 80 85 90 95 Threshold Value H it R a ti o Hit Ratio

(Figure 7.12 Hit Ratio based on Needleman Wunsch Distance Measurement Technique)

Smith Waterman Hit Ratio

1 1.2 1.4 1.6 1.8 2 2.2 2.4 50 60 70 80 90 100 Threshold Value H it R a ti o Hit Ratio

(Figure 7.13 Hit Ratio based on Smith Waterman Distance Measurement Technique)

7.4 Conclusion

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