2013-12-09
Practice Final Question 9.
Originally Posted By: isaiasrm
Group:
Kosta Rashev
Isaias Reyes
Chen ying Kuo
9). Briefly explain the OPIC crawling algorithm.
OPIC stands for Online Page Importance Calculation. Initially, some cash is distributed to each page and each page when it is crawled distributes its current cash equally to all pages it points to. Cash can be defined as the numerical value allotted to each page. The static nodes of the matrix represent the web pages. This is recorded in the history of the page. The importance of a page is then obtained from the credit history of the page. The idea is that the flow of cash through a page is proportional to its importance. At each step, an estimate of any page k's importance is (H[k]+C[k]) / (G+1), where H[k] represents history, C[k] represents cash and G is the total cash accumulated. The algorithm is executed over decided number of iterations until the acceptable rate of convergence is achieved.
Kosta Rashev
Isaias Reyes
Chen ying Kuo
9). Briefly explain the OPIC crawling algorithm.
OPIC stands for Online Page Importance Calculation. Initially, some cash is distributed to each page and each page when it is crawled distributes its current cash equally to all pages it points to. Cash can be defined as the numerical value allotted to each page. The static nodes of the matrix represent the web pages. This is recorded in the history of the page. The importance of a page is then obtained from the credit history of the page. The idea is that the flow of cash through a page is proportional to its importance. At each step, an estimate of any page k's importance is (H[k]+C[k]) / (G+1), where H[k] represents history, C[k] represents cash and G is the total cash accumulated. The algorithm is executed over decided number of iterations until the acceptable rate of convergence is achieved.
'''Originally Posted By: isaiasrm'''
Group:<br>Kosta Rashev<br>Isaias Reyes <br>Chen ying Kuo<br><br>9). Briefly explain the OPIC crawling algorithm.<br><br>OPIC stands for Online Page Importance Calculation. Initially, some cash is distributed to each page and each page when it is crawled distributes its current cash equally to all pages it points to. Cash can be defined as the numerical value allotted to each page. The static nodes of the matrix represent the web pages. This is recorded in the history of the page. The importance of a page is then obtained from the credit history of the page. The idea is that the flow of cash through a page is proportional to its importance. At each step, an estimate of any page k's importance is (H[k]+C[k]) / (G+1), where H[k] represents history, C[k] represents cash and G is the total cash accumulated. The algorithm is executed over decided number of iterations until the acceptable rate of convergence is achieved.
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