6. A computer-readable medium having computer-executable instructions stored thereon to execute a data access model, comprising: a log that collects a plurality of past web data access patterns for a computer user, wherein each past web data access pattern comprises a web page accessed by the user on a computer and a context associated with each web page; a classifier component to perform automatic topic classification of the web page accessed by the user a context component to record the context associated with each web site; and a predictive component to determine web future data access patterns based at least in part on the past web data access patterns, the topic classification of the web page accessed by the user, and the recorded context, and based at least in part on interest of the user in a page from among the past web data access patterns, the interest in the page being computed as I(p)=L(p)*Constant 1 +D(p)*Constant 2 , where I(p) is interest in a page p, L(p) is links followed from page p, D(p) is an average number of seconds spent in sessions starting with page p, and constants Constant 1 and Constant 2 are selected to equate an average of links followed from page p with an average session time length of approximately 30 seconds.
6. A computer-readable medium having computer-executable instructions stored thereon to execute a data access model, comprising: a log that collects a plurality of past web data access patterns for a computer user, wherein each past web data access pattern comprises a web page accessed by the user on a computer and a context associated with each web page; a classifier component to perform automatic topic classification of the web page accessed by the user a context component to record the context associated with each web site; and a predictive component to determine web future data access patterns based at least in part on the past web data access patterns, the topic classification of the web page accessed by the user, and the recorded context, and based at least in part on interest of the user in a page from among the past web data access patterns, the interest in the page being computed as I(p)=L(p)*Constant 1 +D(p)*Constant 2 , where I(p) is interest in a page p, L(p) is links followed from page p, D(p) is an average number of seconds spent in sessions starting with page p, and constants Constant 1 and Constant 2 are selected to equate an average of links followed from page p with an average session time length of approximately 30 seconds. 17. The model of claim 6 , further comprising the classifier employing a vector-based learning algorithm.