CHAPTER-7 EXPERIMENTS AND TEST RESULTS FOR PROPOSED PREDICTION MODEL
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- Hilary Arnold
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1 CHAPTER-7 EXPERIMENTS AND TEST RESULTS FOR PROPOSED PREDICTION MODEL 7.1 Preprocessing Experiments and Results 7.2 Sessionization Experiments and Results 7.3 Pattern Discovery Experiments and Results 7.4 Conclusion Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 99
2 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 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)) or( cs-uri-stem like '%.php' and ( sc-status=200 or sc-status=304 or sc-status=306)) or ( cs-uri-stem like '%.aspx' and ( sc-status=200 or sc-status=304 or sc-status=306)) or ( (cs-uri-stem like '%.jpg' and timetaken >= )and( sc-status=200 or sc-status=304 or sc-status=306) ) or (( cs-uri-stem like '%.gif' and time-taken >= ) and( sc-status=200 or sc-status=304 or sc-status=306) ) or (( cs-uri-stem like Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 100
3 '%.avi' and time-taken >= ) and( sc-status=200 or sc-status=304 or sc-status=306)) or ( (cs-uristem like '%.dat' and time-taken >= )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. Figure 7.1 Snapshot of Microsoft Visual Log Parser Tool for Test-1 Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 101
4 Table 7.1 Log Data in W3C Field Format Log File Name Row Date Time C-ip s-site s-computer s-ip e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 102
5 Table 7.1 Log Data in W3C Field Format(Continue) Log File Name Row Date Time C-ip s-site s-computer s-ip e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW e:\dharmendra\logexample\iis.log : W3SVC1 ENVGISNEW Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 103
![(Figure 7.2 Filtered Log Entries) [3] Preprocessing Test-3:- Test Description: - To determine unique web objects and associated hit count.](/docs-images/39/19812696/images/6-0.png "Result: - Sample of result of above test is available in figure-7.")
6 (Figure 7.2 Filtered Log Entries) [3] Preprocessing Test-3:- Test Description: - To determine unique web objects and associated hit count. Result: - Sample of result of above test is available in figure-7.4 Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 104
7 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 or( cs-uri-stem like '%.php' and ( sc-status=200 or sc-status=304 or sc-status=306)) or ( cs-uri-stem like '%.aspx' and ( sc-status=200 or sc-status=304 or sc-status=306)) Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 105
8 or ( (cs-uri-stem like '%.jpg' and time-taken >= )and( sc-status=200 or sc-status=304 or sc-status=306) ) or (( cs-uri-stem like '%.gif' and time-taken >= ) and( sc-status=200 or sc-status=304 or sc-status=306) ) or (( cs-uri-stem like '%.avi' and time-taken >= ) and( sc-status=200 or sc-status=304 or sc-status=306)) or ( (cs-uri-stem like '%.dat' and time-taken >= )and( sc-status=200 or sc-status=304 or sc-status=306)) group by cs-uri-stem [4] 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. Similar kind of tests is carried out for other research for comparison purpose. Figure 7.6 describes the percentage accuracy of preprocessing phase. From figure it is found out that preprocessing accuracy of proposed model is quite less than other models as other models ignores binary objects like audio and video objects. In all other models sometimes valuable information in form of binary objects are removed and that is not the case of proposed model. Figure 7.7 describes the proportions of text objects and binary objects at every test levels so it Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 106
9 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. [5] 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 In this test it is assumed that average internet speed is 1.4 Mbps, average size of image ranges from 5 to 7 Mb and audio-video files starts from 20 Mb. Around 500 images and 100 videos data used in deciding threshold value. Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 107
![[5] 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.](/docs-images/47/19812696/images/page_9.jpg "Reference is http://www.numion.com/calculators/time.html. In this test it is assumed that average internet speed is 1.")
10 (Figure-7.4 Unique Web Objects and Hit Count) Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 108
11 (Figure-7.5 Final list of Web Objects) Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 109
12 Number of Objects (%) Accuracy Preprocessing Accuracy Proposed Model Other Model 96 Clean Accuracy (%) Proposed Model 97.6 Other Model 99.4 Models (Figure 7.6 Preprocessing Accuracy) Proportion of Objects Text Objects Binary Objects 0 After Test 2 After Test 3 After Test 4 Text Objects Binary Objects Tests (Figure 7.7 Proportion of Text Objects and Binary Objects) 7.2 Sessionization Experiments and Results In this research, for sessionization, strategy of cookie and sessionization heuristic is used. This strategy is similar as previous work. To perform testing of sessionization one customized software is developed and based on that numbers of sessions are generated from server raw log file. The result of test is describes in table 7.2. Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 110
13 Table 7.2 Sessionization Result Total Users 109 Total Unique IP 57 Total session 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 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 Next Dim colval As Integer For i = 1 To max_row colval = max_col + 1 Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 111
14 [2] Markov Test-2 For c = 1 To max_col Sheet1.Cells(i, colval + 1).Value = values(i, c) colval = colval + 1 Next Next End Sub 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. According to Markov Model prediction accuracy is increasing if higher order Markov Model is used. Prediction accuracy of first to tenth order Markov Model is depicted in following figure. From figure it is determined that prediction accuracy of tenth order model is about 66%. Table 7.3 describes hit ratio of first to tenth order Markov Model. From table it is determined that it is very difficult to get hit ratio equals to 1. Up to the seventh Markov Model the hit ratio tends to be negative and then after it slightly improve but not reach to an ideal value. Figure 7.9 describes same scenario in graphical representation form. Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 112
15 % Accuracy Prediction Accuracy First Second Third Fourth Fifth Sixth Seventh Eight Ninth Tenth Markov Chain Order (Figure 7.8 Prediction Accuracy of Markov Orders) Table 7.3 Markov Hit Ratio Series1 Markov Chain Hit Ratio First Second Third Fourth Fifth -1 Sixth -2 Seventh -7 Eight 8 Ninth 3 Tenth 2 Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 113
16 Hit Ratio Markovin Model Hit Ratio First Second Third Fourth Fifth Sixth Seventh Eight Ninth Tenth Hit Ratio Markov Chains (Figure 7.9 Markov Model Hit Ratio) 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 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 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 conditional formatting option. Metric generated in previous test result is used as an input. Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 114
17 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 Sessions Involved in each cluster Web Objects Referred in that Accuracy of pattern ,10 2,5,7,8,9,10,12,13,14, ,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, ,7,11,12,15,17 2,3,4,6,9,11,12,14,15,8,10,5,7, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 115
18 Table 7.4 Pattern Discovery based on Lavensthein Distance(Continue) Threshold Number of 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, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,4,15 2,4,6,8,9,10,12,14,15,3, ,20 7,6,5,2,1,9,10,12,14,11, ,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8, ,1,15 2,5,7,8,9,10,4,6,12,14,15,3, ,4,12,15,17 2,4,6,8,9,10,12,14,15,3, 5,11,15,1, ,4,11,15,17 2,4,6,8,9,10,12,14,15,3,1, ,5,19 5,7,9,11,12,13,14,15,2,3,8, ,2 6,8,9,12,15,2, ,2,4,7,10,11,12,17 6,8,9,12,15,2,5,4,10,14,3,11, 7,13, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 116
19 Table 7.4 Pattern Discovery based on Lavensthein Distance(Continue) Threshold Number of 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, ,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11, ,3,9,25 3,4,5,6,7,9,10,11,12,15,14,13,8,2, ,5,13, 5,7,9,11,12,13,14,15,2,3, 6, ,8 7,6,5,2,1,9,10,12,14, ,3 3,4,5,6,7,9,10,11,12,15,14, ,18 8,9,10,2,3,4,5,6,7,11,12,15,14,13 56 Standard Deviation Mean ,10 2,5,7,8,9,10,12,13,14,15, ,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, ,13,19 3,6,9,11,12,13,14,15,2,10,5,7, ,16 5,6,2,3,8,7,9,4,10,11,12,13,15 79 Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 117
20 Table 7.4 Pattern Discovery based on Lavensthein Distance(Continue) Threshold Number of Sessions Involved in each cluster Web Objects Referred in that Accuracy of pattern 8,20 7,6,5,2,1,9,10,12,14,11, ,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8, ,1 2,5,7,8,9, ,4,12,15,17 2,4,6,8,9,10,12,14,15,3,5,11,2,1, ,4,11,17 2,4,6,8,9,10,12,14,15,3, ,5,19 5,7,9,11,12,13,14,15,2,3,2,3,8, ,4,11,17 2,4,6,8,9,10,12,14,15, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11, ,3,9,25 3,4,5,6,7,9,10,11,12,15,14,13,8,2, ,5,13 5,7,9,11,12,13,14,15,2,3, 6, ,8 7,6,5,2,1,9,10,12,14, ,18 8,9,10,2,3,4,5,6,7,11,12,15,14,13 56 Standard Deviation Mean Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 118
21 Table 7.4 Pattern Discovery based on Lavensthein Distance(Continue) Threshold Number of Sessions Involved in each cluster Web Objects Referred in that Accuracy of pattern ,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, ,13,19 3,6,9,11,12,13,14,15,2,10,5,7, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,20 7,6,5,2,1,9,10,12,14,11, ,3 3,4,5,6,7,9,10,11,12,15,14, ,4,12,15,17 2,4,6,8,9,10,12,14,15,3,5,11,2,1, ,4,11,17 2,4,6,8,9,10,12,14,15,3, ,5 5,7,9,11,12,13,14,15,2, ,4,11,17 2,4,6,8,9,10,12,14,15, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11, ,3 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 6.02 Mean Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 119
22 Table 7.4 Pattern Discovery based on Lavensthein Distance(Continue) Threshold Number of Sessions Involved in each cluster Web Objects Referred in that Accuracy of pattern ,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, ,13,19 3,6,9,11,12,13,14,15,2,10,5,7, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,20 7,6,5,2,1,9,10,12,14,11, ,3 3,4,5,6,7,9,10,11,12,15,14, ,4,12,17 2,4,6,8,9,10,12,14,15,3,,5, 11,15,2,, ,4,11,17 2,4,6,8,9,10,12,14,15,3, ,5 5,7,9,11,12,13,14,15,2, ,4,17 2,4,6,8,9,10,12,14,15,3, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11, ,3 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 5.93 Mean ,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, ,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 Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 120
23 Table 7.4 Pattern Discovery based on Lavensthein Distance(Continue) Threshold Number of Sessions Involved in each cluster Web Objects Referred in that Accuracy of pattern 8,20 7,6,5,2,1,9,10,12,14,11, ,3 3,4,5,6,7,9,10,11,12,15,14, ,4,17 2,4,6,8,9,10,12,14,15,3, ,4,17 2,4,6,8,9,10,12,14,15,3, ,4 2,4,6,8,9,10,12,14,15, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,12 2,4,6,8,9,10,12,14,15,3,5,11,15,3,9,8,6, ,3 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 5.18 Mean ,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, ,20 7,6,5,2,1,9,10,12,14,11,13 88 Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 121
24 Table 7.4 Pattern Discovery based on Lavensthein Distance(Continue) Threshold Number of Sessions Involved in each cluster Web Objects Referred in that Accuracy of pattern 9,3 3,4,5,6,7,9,10,11,12,15,14, ,4,17 2,4,6,8,9,10,12,14,15,3, ,4 2,4,6,8,9,10,12,14,15, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11 2,4,6,8,9,10,12,14,15, ,3 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 5.26 Mean ,11,17 2,4,6,8,9,10,12,14,15,3, ,20 7,6,5,2,1,9,10,12,14,11, ,4,17 2,4,6,8,9,10,12,14,15,3, ,4,11 2,4,6,8,9,10,12,14,15, ,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 1.46 Mean Experiments on Needleman Wunsch Distance Measurement technique [1] Needleman Wunsch Test -1 Test Description: - To determine distance measure between web sessions according to Lavensthein distance measurement technique. Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 122
25 Tool used: - One online tool is used to determine distance measure between web sessions. Reference is 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. Thres hold 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 Number of Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern ,10,11,12,14,21,23,25 2,5,7,8,9,10,12,13,14,15,4,3,,6,1, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 123
26 Thres hold Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue) Number of 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, ,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, ,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, ,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, ,3,7,9,10,16,18,20,23 3,4,5,6,7,9,10,11,12,15,14,13,2,8, 13, ,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, ,3,4,7,9,10,15,18,20,24 3,4,5,6,7,9,10,11,12,15,14,13,2,,8, ,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, ,1,2,3,4,5,6,7,8,9,12,15,17,18,20,24 2,5,7,8,9,10,6, 12,15,3,4,11,14,13,3, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 124
27 Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue) Thres hold Number of 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, ,1,3,4,5,9,10,11,14,15,17,20 2,5,7,8,9,10,3,4,6,11,12,15,14,13, ,4,5,17,19 2,4,6,8,9,10,12,14,15,3,,5,7,11,13, ,1,9,11,12,17,19,25 2,5,7,8,9,10,6,4,5,11,12,15,14,13, 3, ,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, ,3,6,7,9,15,18,20,25 3,4,5,6,7,9,10,11,12,15,14,13,8,2, ,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, ,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, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 125
28 Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue) Thres hold Number of 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,, ,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, ,1,7,17,19,25 2,5,7,8,9,10,3,4,6,11,12,14,15,13,1, ,4,7 2,4,6,8,9,10,12,14,15,3, ,1,3,6,7,18,24,25 2,5,7,8,9,10,3,4,6,11,12,15,14,13, ,2,4,5,7,8,9,10,15,23 6,8,9,12,15,2,5,4,10,14,15,3,7,11,13, , 1,2,3,6,14,16,17,18,20,21,23 2,5,7,8,9,10,6,12,15,3,4,11, 14,13,1,, Standard Deviation Mean ,14 6,8,9,12,15,2,5,1,7, ,7,15,24 2,3,4,6,9,11,12,14,15,8, 10,1,13, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 126
29 Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue) Thres hold Number of 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, ,4,13,19 2,4,6,8,9,10,12,14,15,3, 11, 13,5, ,3,7,16,25 3,4,5,6,7,9,10,11,12,15,14,13,2,8, 1, ,2,4,6,8,10,15,16,18 6,8,9,12,2,5,4,10,14,15,3, 7,11, 13,1,14,, ,7,10,15,20 2,3,4,6,9,11,12,14,15,8,5,7,10, 13, ,3,4,10,12,15,18 3,4,5,6,7,9,10,11,12,15,14,13,2,8, ,3,4,7,8,9,15 3,4,5,6,7,9,10,11,12,15,14,13,2,8, 1 11,4,12,14,15,17 2,4,6,8,9,10,12,14,15,3,,5,11,,1, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 127
30 Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue) Thres hold Number of 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, ,5,19 5,7,9,11,12,13,14,15,2,3,,8, ,1,11 2,5,7,8,9,10,4,6,,12,14,15, ,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, ,6,7,18,25 3,8,7,9,4,6,10,11,12,13,15,2,,14,,5, ,4,11,12,15,19,25 2,4,6,8,9,10,12,14,15,3,,5,11,1,7,, ,3,7,9,16,25 3,4,5,6,7,9,10,11,12,15,14,13,2, 8, ,5,13,17 5,7,9,11,12,13,14,15,2,3,,6,10,4,8, ,4,8,15 2,4,6,8,9,10,12,14,15,3,7,5,2,1, ,25 8,2,1,3,4,5,7,9,10,11,12, ,2,15 6,8,9,12,15,2,5,4,10,14,3,2, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 128
31 Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue) Thres hold Number of 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, Standard Deviation 4.50 Mean ,14 6,8,9,12,15,2,5,1,7, ,9,18,25 6,4,5,,7,9,10,11,12,15,14,13,8,2,3,1 4,5,11,12,15,17,20 5,7,9,11,12,13,14,15,2,3,4,6,8,10, ,4,13,19 2,4,6,8,9,10,12,14,15,3, 11, 13,5, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,10 2,5,7,8,9,10,12,13,14, ,20 7,6,5,2,1,5,6,9,10,12,14,11,10,9,1 3,5 9,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8,, ,7,15 2,3,4,6,9,11,12,14,15,8,10, ,4,12,14,15,17 2,4,6,8,9,10,12,14,15,3,,5,11,,1,7 72 Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 129
32 Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue) Thres hold Number of 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, ,5,19 5,7,9,11,12,13,14,15,2,3,,8, ,1,11 2,5,7,8,9,10,4,6,,12,14,15, ,4,10,11,12,17 2,4,6,8,9,10,12,14,15,3,5,7, 13,, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11,15,3, ,3,9,25 3,4,5,6,7,9,10,11,12,15,14,13,,8,2, ,5,13 5,7,9,11,12,13,14,15,2,3,6, ,4,8 2,4,6,8,9,10,12,14,15,3, 7,5,1, ,25 8,2,1,3,4,5,7,9,10,11,12, ,3,18,23 3,4,5,6,7,9,10,11,12,15,14,13,8, 2, Standard Deviation 7.67 Mean ,14 6,8,9,12,15,2,5,1,7,10 71 Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 130
33 3,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3, 13 4,11,12,15,17 2,4,6,8,9,10,12,14,15,3,,5, 11, 1,7 5,13,19 3,6,9,11,12,13,14,15,2,14,10,5,7, 15, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,20 7,6,5,2,1,5,6,9,10,12,14,11,10,9,1 3,5 9,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8,, ,4,12,17 2,4,6,8,9,10,12,14,15,3,5, 11, ,4,11,17 2,4,6,8,9,10,12,14,15,3,, ,5,19 5,7,9,11,12,13,14,15,2,3,,8, ,1 2,5,7,8,9, ,4,17 2,4,6,8,9,10,12,14,15,3, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11,15,3, ,3,9 3,4,5,6,7,9,10,11,12,15,14, ,5,13 5,7,9,11,12,13,14,15,2,3,6, ,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 5.63 Mean Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 131
34 Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue) Thres hold Number of Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern ,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3, 13 4,11,12,15,17 2,4,6,8,9,10,12,14,15,3,,5, 11, 1,7 5,13,19 3,6,9,11,12,13,14,15,2,14,10,5,7, 15, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,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, ,4,12,17 2,4,6,8,9,10,12,14,15,3,5, 11, ,4,11,17 2,4,6,8,9,10,12,14,15,3,, ,5,19 5,7,9,11,12,13,14,15,2,3,,8, ,4 2,4,6,8,9,10,12,14,15, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,12 2,4,6,8,9,10,12,14,15,3, 5, ,3 3,4,5,6,7,9,10,11,12,15,14, ,5,13 5,7,9,11,12,13,14,15,2,3,6, ,8 7,6,5,2,1,9,10,12,14,11 88 Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 132
35 Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue) Thres hold Number of Sessions Involved in each cluster Web Objects Referred in that Accuracy of Pattern Standard Deviation 4.57 Mean ,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2,3, 13 4,11,12,15,17 2,4,6,8,9,10,12,14,15,3,,5, 11, 1, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,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, ,4,17 2,4,6,8,9,10,12,14,15,3, ,4,17 2,4,6,8,9,10,12,14,15,3, ,4 2,4,6,8,9,10,12,14,15, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,12 2,4,6,8,9,10,12,14,15,3, 5, ,3 3,4,5,6,7,9,10,11,12,15,14, ,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 3.60 Mean ,18 8,9,10,2,3,4,5,6,7,,11,12,15,14, ,15,17 2,4,6,8,9,10,12,14,15,3,1, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 133
36 Table 7.5 Patten Discovery based on Needleman Wunsch Distance(Continue) Thres hold Number of 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, ,20 7,6,5,2,1,5,6,9,10,12,14,11,10,9,1 3, ,17 2,4,6,8,9,10,12,14,15,3, ,4 2,4,6,8,9,10,12,14,15, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11 2,4,6,8,9,10,12,14,15, ,3 3,4,5,6,7,9,10,11,12,15,14, ,8 7,6,5,2,1,9,10,12,14,11 88 Standard Deviation 2.56 Mean ,17 2,4,6,8,9,10,12,14,15,3, ,11 2,4,6,8,9,10,12,14,15,3,7 91 Standard Deviation 0 Mean 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 Results: - One metric with distance value is generated as a result of this test. [2] Smith Waterman Test -2 Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 134
37 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 Sessions Involved in each cluster Web Objects Referred in that Accuracy of pattern ,3,4,9,10,11,12,14,15,17,18,25 3,4,5,6,7,9,10,11,12,15,14,13,2, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 135
38 Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) Thres hold Number of 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, ,1,7,9,18,23,25 2,5,7,8,9,10,3,4,6,11,12,14,15,13,3, ,1,2,7,10,11,12,15,17 2,5,7,8,9,10,6,12,15,3,4,11,14,13,3, ,10,13,19 2,5,7,8,9,10,12,13,14,15,3,6, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,3,4,11,13,15,17,18 3,4,5,6,7,9,10,11,12,15,14,13,2,,8, ,20,21 7,6,5,2,1,9,10,12,14,11,13,5, ,1,3,18 2,5,7,8,9,10,3,4,6,11,12,15,14, ,1,4,5,11,14,15,17,19,25 2,5,7,8,9,10,4,6,,12,14,15,3,,11,13, ,1,2,4,7,10,12,15,17 2,5,7,8,9,10,6,12,15,,4,,14,3,1113, ,1,4,11,17 2,5,7,8,9,10,4,6,,12,14,15, ,5,7,19 5,7,9,11,12,13,14,15,2,3,14,,4,6,, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 136
39 Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) Thres hold Number of 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, ,1,2,4,7,10,11,17 2,5,7,8,9,10,6,12,15,,4,14,3,,11, ,6 3,8,7,9,4,6,10,11,12,13, ,1,2,4,7,10,11,12,15 2,5,7,8,9,10,6,12,15,4,14,3,11,13, ,1,3,7,9,23,25 2,5,7,8,9,10,3,4,6,,11,12,15,14,13, ,5,10,13 5,7,9,11,12,13,14,15,2,3,8,10,,6,9, ,8,21 7,6,5,2,1,,9,10,12,14,11, ,8,20 7,6,5,2,1,,9,10,12,14,11, ,23 3,4,5,6,8,1,11, ,3,18,22 3,4,5,6,7,9,10,11,12,15,14,13,8, ,1,3,10,18 2,5,7,8,9,10,3,4,6,,11,12,15,14, Standard Deviation Mean Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 137
40 Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) Thres hold Number of Sessions Involved in each cluster Web Objects Referred in that Accuracy of pattern ,10,12,14,25 2,5,7,8,9,10,12,13,14,15,3,4,11,6, ,4,11,14,15,17 2,4,6,8,9,10,12,14,15,3,,5,1, ,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, ,13,19 3,6,9,11,12,13,14,15,2,10,5,7, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,13,18 3,6,9,11,12,13,14,15,2,10,8,4,5, ,20,21 7,6,5,2,1,9,10,12,14,11,13,5, ,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8, ,1,14,25 2,5,7,8,9,10,6,12,15,1,3,4,11, ,2,4,12,15,17 6,8,9,12,15,2,5,4,10,14,3,11,2,14,1, ,1,4,11,17 2,5,7,8,9,10,4,6,,12,14,15, ,5,7,19 5,7,9,11,12,13,14,15,2,3,14,,4,6,, ,1,2,10 2,5,7,8,9,10,6,,12,15,,13, ,2,4,11,17 6,8,9,12,15,2,5,4,10,14,3, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 138
41 Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) Thres hold Number of 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, ,2,4,11,12,15 6,8,9,12,15,2,5,4,10,,14,3,11,4, ,3,7,9,25 3,4,5,6,7,9,10,11,12,15,14,13,2,8, ,5,13 5,7,9,11,12,13,14,15,2,3,6, ,8,21 7,6,5,2,1,,9,10,12,14,11, ,8,20 7,6,5,2,1,,9,10,12,14,11, ,23 3,4,5,6,8,1,11, ,22 3,4,5,6,8,1,11, ,1,3,10,18 2,5,7,8,9,10,3,4,6,,11,12,15,14, Standard Deviation Mean ,10,14,25 2,5,7,8,9,10,12,13,14,15,6,1,3,4, ,14,15 6,8,9,12,15,2,5,1,7,10,4,14, ,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2, ,11,12,15,17 2,4,6,8,9,10,12,14,15,3,5,11,3,1, ,13,19 3,6,9,11,12,13,14,15,2,10,5,7, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,13,18 3,6,9,11,12,13,14,15,2,10,8,4,5, ,20 7,6,5,2,1,9,10,12,14,11, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 139
42 Thres hold Number of Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) Sessions Involved in Web Objects Referred in that Accuracy of pattern each cluster 9,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8, ,1 2,5,7,8,9, ,4,15,17 2,4,6,8,9,10,12,14,15,3,1, ,4,17 2,4,6,8,9,10,12,14,15,3, ,5,7,19 5,7,9,11,12,13,14,15,2,3,14,,4,6, ,1,2 2,5,7,8,9,10,6,12, ,2,4,11,17 6,8,9,12,15,2,5,4,10,14,3, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,12,15 2,4,6,8,9,10,12,14,15,3,5,11,3,1, ,3,7,9 3,4,5,6,7,9,10,11,12,15,14,13,2, ,5,13 5,7,9,11,12,13,14,15,2,3,6, ,8 7,6,5,2,1,9,10,12,14, ,1 2,5,7,8,9,10 83 Standard Deviation Mean ,10,14,25 2,5,7,8,9,10,12,13,14,15,6,1,3,4, ,14 6,8,9,12,15,2,5,7, ,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2, ,11,15,17 2,4,6,8,9,10,12,14,15,3,1, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 140
43 Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) Thres hold Number of 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, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,20 7,6,5,2,1,9,10,12,14,11, ,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8, ,1 2,5,7,8,9, ,4,15,17 2,4,6,8,9,10,12,14,15,3,1, ,5 5,7,9,11,12,13,14,15,2, ,1,2 2,5,7,8,9,10,6,12, ,4,11,17 2,4,6,8,9,10,12,14,15,3, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,15 2,4,6,8,9,10,12,14,15,3,1, ,3,9 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14, ,1 2,5,7,8,9,10 83 Standard Deviation 9.80 Mean ,10,14,25 2,5,7,8,9,10,12,13,14,15,6,1,3,4, ,14 6,8,9,12,15,2,5,7, ,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 141
44 Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) Thres hold Number of 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, ,13,19 3,6,9,11,12,13,14,15,2,10,5,7, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,20 7,6,5,2,1,9,10,12,14,11, ,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8, ,1 2,5,7,8,9, ,4,15,17 2,4,6,8,9,10,12,14,15,3,1, ,5 5,7,9,11,12,13,14,15,2, ,1,2 2,5,7,8,9,10,6,12, ,4,11,17 2,4,6,8,9,10,12,14,15,3, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,15 2,4,6,8,9,10,12,14,15,3,1, ,3,9 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14, ,1 2,5,7,8,9,10 83 Standard Deviation 9.80 Mean ,10,14,25 2,5,7,8,9,10,12,13,14,15,6,1,3,4, ,14 6,8,9,12,15,2,5,7, ,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 142
45 Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) Thres hold Number of 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, ,13,19 3,6,9,11,12,13,14,15,2,10,5,7, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,20 7,6,5,2,1,9,10,12,14,11, ,3,18 3,4,5,6,7,9,10,11,12,15,14,13,8, ,1 2,5,7,8,9, ,4,15,17 2,4,6,8,9,10,12,14,15,3,1, ,1,2 2,5,7,8,9,10,6,12, ,4,11,17 2,4,6,8,9,10,12,14,15,3, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,15 2,4,6,8,9,10,12,14,15,3,1, ,3,9 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14, ,1 2,5,7,8,9,10 83 Standard Deviation 9.25 Mean ,10,14,25 2,5,7,8,9,10,12,13,14,15,6,1,3,4, ,14 6,8,9,12,15,2,5,7, ,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2, ,11,15,17 2,4,6,8,9,10,12,14,15,3,1, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 143
46 Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) Thres hold Number of 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, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,20 7,6,5,2,1,9,10,12,14,11, ,3 3,4,5,6,7,9,10,11,12,15,14, ,1 2,5,7,8,9, ,4,15,17 2,4,6,8,9,10,12,14,15,3,1, ,1,2 2,5,7,8,9,10,6,12, ,4,11,17 2,4,6,8,9,10,12,14,15,3, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11,15 2,4,6,8,9,10,12,14,15,3,1, ,3 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14, ,1 2,5,7,8,9,10 83 Standard Deviation 9.26 Mean ,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, ,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2, ,11,17 2,4,6,8,9,10,12,14,15,3, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 144
47 Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) Thres hold Number of 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, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,20 7,6,5,2,1,9,10,12,14,11, ,3 3,4,5,6,7,9,10,11,12,15,14, ,1 2,5,7,8,9, ,4,17 2,4,6,8,9,10,12,14,15,3, ,1,2 2,5,7,8,9,10,6,12, ,6 3,8,7,9,4,6,10,11,12,13, ,4,11 2,4,6,8,9,10,12,14,15, ,3 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14, Standard Deviation 6.57 Mean ,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, ,9,18 6,4,5,7,9,10,11,12,15,14,13,8,2, ,11,17 2,4,6,8,9,10,12,14,15,3, ,19 5,7,9,11,12,13,14,15,2,3,8, ,16 5,6,2,3,8,7,9,4,10,11,12,13, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 145
48 Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) Thres hold Number of Sessions Involved in each cluster Web Objects Referred in that Accuracy of pattern 8,20 7,6,5,2,1,9,10,12,14,11, ,3 3,4,5,6,7,9,10,11,12,15,14, ,1 2,5,7,8,9, ,4 2,4,6,8,9,10,12,14,15, ,1,2 2,5,7,8,9,10,6,12, ,6 3,8,7,9,4,6,10,11,12,13, ,4 2,4,6,8,9,10,12,14,15, ,3 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14, Standard Deviation 6.73 Mean ,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, ,18 8,9,10,2,3,4,5,6,7,,11,12,15,14, ,11,17 2,4,6,8,9,10,12,14,15,3, ,19 5,7,9,11,12,13,14,15,2,3,8, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,20 7,6,5,2,1,9,10,12,14,11, ,1 2,5,7,8,9, ,4 2,4,6,8,9,10,12,14,15, ,1,2 2,5,7,8,9,10,6,12, ,6 3,8,7,9,4,6,10,11,12,13, Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 146
49 Table 7.6 Patten Discovery based on Smith Waterman Distance(Continue) 17,4 2,4,6,8,9,10,12,14,15, ,3 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14, Standard Deviation 6.86 Mean ,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, ,18 8,9,10,2,3,4,5,6,7,,11,12,15,14, ,11,17 2,4,6,8,9,10,12,14,15,3, ,19 5,7,9,11,12,13,14,15,2,3,8, ,16 5,6,2,3,8,7,9,4,10,11,12,13, ,20 7,6,5,2,1,9,10,12,14,11, ,1 2,5,7,8,9, ,4 2,4,6,8,9,10,12,14,15, ,1,2 2,5,7,8,9,10,6,12, ,6 3,8,7,9,4,6,10,11,12,13, ,4 2,4,6,8,9,10,12,14,15, ,3 3,4,5,6,7,9,10,11,12,15,14, ,5 5,7,9,11,12,13,14,15,2, ,8 7,6,5,2,1,9,10,12,14, Standard Deviation 6.86 Mean Prediction Model for Web Caching and Prefetching with Web Usage Mining to optimize web objects 147
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