Abstract:
In the current business context, evaluation of the
web page views or web pages accesses is gaining a huge
recognition where it has become a vital need in planning of
computer resource allocations and estimation process of
upcoming revenue and advertising growth. One of the
essential tasks of the hosting service provider is to allocate
servers to each of the websites to maintain a certain level of
quality of service for different levels of incoming requests at
each point of time, and optimize the use of server resources,
while maximizing its profits. In order to have a proactive
management of resources within the servers it is required to
build an accurate forecasting process of web accesses per
unit time. As a time series, the web access patterns of the
users exhibit not even different levels of short time random
fluctuations but also periodic patterns that evolve randomly
from one period to another. In this paper, we focus on
extracting trends and web access patterns from page view or
page access series using data mining techniques and their
applicability on predicting the future web page accesses.
Based on the time series regression techniques which are
used to analyse and forecast web accesses, we found that
Sequential Minimal Optimization and Linear Regression
algorithms were providing more accurate results in
forecasting.
