%-------------Introduction
\chapter{Introduction}

\section{Overview}
Web archives preserve the content of the current World Wide Web for future use. 
The Web is growing rapidly, but so too is information frequently disappearing from the WWW.
In 1997, the founder of the Internet Archive stated that the average lifetime for a URL was 44 days \cite{10.2307/24993660}.
In 2001, a study showed that 47\% of the web pages became inaccessible within two years \cite{6970226}.
In 2012, another study stated that about 11 percent of resources shared online are lost, and that they continue to disappear at a rate of 0.02 percent per day \cite{10.1007/978-3-642-33290-6_14}.
We do not know how many parts of the WWW are vanishing right now, but expect the situation to be similar.

The act of preserving the WWW is crucial to record the history of human society.
For this reason, a growing number of memory/heritage institutions actively engage in web archive activities, e.g., Internet Archive, Common Crawl, and Library of Congress.
Though various institutions are dedicated to web archive activities, many of them are non-profit corporations.
Due to limitations in resources available, it would be helpful to have ways to improve their crawling accuracy and efficiency.

The essential activity of preserving the WWW is crawling, e.g., of known web sites or web pages, as well as new ones as they appear.
The existing crawling model in the web archive communities mainly adopts predefined crawling criteria for corresponding candidates, which are usually managed by human coordinators. As an example, at Virginia Tech, the University Library works with Archive-It, a web archive crawling system from the Internet Archive, for routine preservation of the Virginia Tech domain (vt.edu).
The following list gives some details about how the Virginia Tech library set up the crawling policies: 
\begin{itemize}
\item Crawls are managed by the Digital Preservation Coordinator.
\item Crawls are performed biannually and as needed.
\item The scope of the crawl searches for hyperlinks four levels from the original seed.
\item Requests for adding a seed to the Web Archive can be directed to the Digital Preservation Coordinator.
\end{itemize}

This typical example shows that the crawling policy is often designed with a fixed frequency and a certain level of hyperlinks to be discovered. For preservers that are familiar with their collection, these rules could potentially capture most of the changes as desired with proper resources dedicated to the purpose. 

Besides user-defined special collections, the other major part of web archive activity is the general crawling over the internet, a principal activity of search engine companies like Google or Microsoft.
In general, more frequent crawls will lead to better accuracy.
At the same time, more frequent crawls also could waste  computational resources if occurring beyond the web change frequency.
Accordingly, general crawls usually adopt dynamic rules to adapt to web site behavior.
Different kinds of web sites behave differently: social network web sites often change dynamically and could change every second; News websites could be updated every hour.
Further, more news often leads to more web pages within the site.
Information resources like wikis are continually changing based on the user behavior.
These examples show that Web sites in different categories should be looked at differently when modeling their changing behavior.
Through suitable modeling, the changing behavior of web sites can lead to a better crawling process that decides when the crawler should revisit the web site or page and so keeps up with all the changes.

In our project, we focus on the problem of modeling the changing behavior of news web sites such as CNN and ABC.
We propose to build a model that can learn the historical information from web archives about the news web page/site change behavior, and use the model to predict future web changes as an indicator for a crawling scheduler to cover potential new information.
We specifically explore reinforcement learning algorithms to solve our problem.
We compare the result of our approach with existing methods.

\section{Research Questions}

This project addresses problems related to modeling news web page/site changes for crawling scheduling by answering the following research questions:

\paragraph{RQ1:} \textit{Can we automatically and effectively identify the unique copies of a web page in a news website from its archive?}

\paragraph{RQ2:} \textit{Can we automatically and effectively identify the unique site structure of a news website from its archive?}

\paragraph{RQ3:} \textit{Can we use the archived information for future web page/site change prediction?}

\paragraph{RQ4:} \textit{Can a deep learning model through reinforcement learning techniques surpass the existing methods on predicting the future changes of a web page/site?}
