5. Organization of Frequently
Accessed Pages

In the proposed approach we consider the pre-processed
log file of a website. After observing, the usages of web pages have computed
thefrequency of access for each web page
using the SBFC algorithm. There are three different types of organizations
considered in this paper. fig.4 is the organization of the web pages in the binary
search tree pattern, which is followed by many websites.

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Fig. 4.
Binary Search Tree organization of web pages

After constructing the initial
binary search tree structure for the most frequently accessed web pages, binary
max heap structure has been used. By performing the max heap operation, it
brings the most frequently accessed web pages to the root node. Fig.5 shows the
reorganization of the web pages using max heap structure.

Fig.
5. Max Heap organization of web pages

The
third way to reorganize the web pages using Fibonacci Max heap structure, which
is shown in the fig. 6.

Fig.
6. Fibonacci Maxheap organization of web
pages

We consider
the order in which the output is generated. Another different way is also
possible and organize all the pages in ascending order of their frequency
count, but the time taken to organize the pages itself is O(nlog n).

6. Search Cost

Based on the organization of three different
structures search cost has been calculated and it is given in table 2. The
search cost 17 has been considered how many attempts have been made to search
a particular page for the given website. The overall cost has been calculated
based on the frequency and a single hit.

Table 2.Search
Cost Calculation

Page
No

Frequency
Count

Binary
Search Tree

Binary
Max Heap Tree

Fibonacci
Max Heap Tree

Frequency

Search
Cost

Frequency

Search
Cost

Frequency

Search
Cost

Page1

233

233 * 1

233

233 * 3

669

233 * 1

233

Page 2

221

221 * 2

442

221 * 4

884

221 * 1

221

Page 3

471

471 * 2

942

471 * 2

942

471 * 1

471

Page 4

629

629 * 3

1887

629 * 1

629

629 * 1

629

Page 5

242

242 * 3

726

242 * 3

726

242 * 1

242

Page 6

410

410 * 4

1640

410 * 2

820

410 * 1

410

Page 7

238

238 * 4

952

238 * 3

714

238 * 1

238

Page 8

217

217 * 3

651

217 * 4

868

217 * 1

217

Page 9

351

351 * 5

1755

351 * 3

1053

351 * 1

351

Page 10

223

223 * 3

669

223 * 4

892

223 * 1

223

Total

9897

8197

3235

It is observed from the experimental results, the
interesting web pages (i.e.) the top most frequently accessed web pages are
nearer to the root. Reorganization requires less time as the Fibonacci Max heap
data structure is used in the proposed work. The website is well organized with
interesting and useful pages for the users 18, which are nearer to the root
node. It helps to improve the distance between web pages.

The Fig.7. shows the search cost of the three
methods considered in this paper. Clearly the diagram shows that the Fibonacci
Max heap gives a lowest total search cost of the frequently accessed web pages.

Fig.
7.Graphical representation of three organizations.

The search cost for
each page before and after the reorganization
of the website is made and represented in the fig. 7. Experimental results show
that the Fibonacci Max heap operation is suggestive to dynamic websites.
Dynamic websites are those websites that need
to be reorganized periodically based upon the season. Few examples of dynamic
websites are e-shopping, e-commerce, educational institutions etc. Users are
likely to access the web pages that are relevant to that particular season. The
proposed work on the reorganization of the website based on the Fibonacci Max
heap structure to bring frequently accessed web pages nearer to the root will
minimize the searching time.