For discovering the new URI of a missing web page, lexical signatures, which consist of a small number of words chosen to represent the "aboutness" of a page, have been previously proposed. However, prior methods relied on computing the lexical signature before the page was lost, or using cached or archived versions of the page to calculate a lexical signature. We demonstrate a system of constructing a lexical signature for a page from its link neighborhood, that is the "backlinks", or pages that link to the missing page. After testing various methods, we show that one can construct a lexical signature for a missing web page using only ten backlink pages. Further, we show that only the first level of backlinks are useful in this effort. The text that the backlinks use to point to the missing page is used as input for the creation of a four-word lexical signature. That lexical signature is shown to successfully find the target URI in over half of the test cases.
Klein and Nelson @cite_0 proposed a method to use LSs calculated from archived or cached versions of a page to calculate a lexical signature. They showed that a five-term or a seven-term lexical signature would produce the best results. A seven-term LS did best at finding the URI in the first result, whereas a five-term LS performed best at finding the URI somewhere in the first page.
@cite_0
1518577300 2117629987 2021198451 2140696861
A lexical signature (LS) is a small set of terms derived from a document that capture the "aboutness" of that document. A LS generated from a web page can be used to discover that page at a different URL as well as to find relevant pages in the Internet. From a set of randomly selected URLs we took all their copies from the Internet Archive between 1996 and 2007 and generated their LSs. We conducted an overlap analysis of terms in all LSs and found only small overlaps in the early years (1996 i¾? 2000) but increasing numbers in the more recent past (from 2003 on). We measured the performance of all LSs in dependence of the number of terms they consist of. We found that LSs created more recently perform better than early LSs created between 1996 and 2000. All LSs created from year 2000 on show a similar pattern in their performance curve. Our results show that 5-, 6- and 7-term LSs perform best with returning the URLs of interest in the top ten of the result set. In about 50 of all cases these URLs are returned as the number one result and in 30 of all times we considered the URLs as not discoved. A lexical signature (LS) consisting of several key words from a Web document is often sufficient information for finding the document later, even if its URL has changed. We conduct a large-scale empirical study of nine methods for generating lexical signatures, including Phelps and Wilensky's original proposal (PW), seven of our own static variations, and one new dynamic method. We examine their performance on the Web over a 10-month period, and on a TREC data set, evaluating their ability to both (1) uniquely identify the original (possibly modified) document, and (2) locate other relevant documents if the original is lost. Lexical signatures chosen to minimize document frequency (DF) are good at unique identification but poor at finding relevant documents. PW works well on the relatively small TREC data set, but acts almost identically to DF on the Web, which contains billions of documents. Term-frequency-based lexical signatures (TF) are very easy to compute and often perform well, but are highly dependent on the ranking system of the search engine used. The term-frequency inverse-document-frequency- (TFIDF-) based method and hybrid methods (which combine DF with TF or TFIDF) seem to be the most promising candidates among static methods for generating effective lexical signatures. We propose a dynamic LS generator called Test & Select (TS) to mitigate LS conflict. TS outperforms all eight static methods in terms of both extracting the desired document and finding relevant information, over three different search engines. All LS methods show significant performance degradation as documents in the corpus are edited. For discovering the new URI of a missing web page, lexical signatures, which consist of a small number of words chosen to represent the "aboutness" of a page, have been previously proposed. However, prior methods relied on computing the lexical signature before the page was lost, or using cached or archived versions of the page to calculate a lexical signature. We demonstrate a system of constructing a lexical signature for a page from its link neighborhood, that is the "backlinks", or pages that link to the missing page. After testing various methods, we show that one can construct a lexical signature for a missing web page using only ten backlink pages. Further, we show that only the first level of backlinks are useful in this effort. The text that the backlinks use to point to the missing page is used as input for the creation of a four-word lexical signature. That lexical signature is shown to successfully find the target URI in more than half of the test cases. One way to detect intruders on the Internet is to compare the similarity of two thumbprints. A thumbprint is a summary of a connection that characterizes the connection. The packet gap thumbprint consists of sequences of non-negative real number representing the time gaps between "send" packets. This paper formalized definitions of similarity between two non-negative real number sequences, by introducing spl epsiv -similarity, partial sum and longest spl epsiv -similar subsequence (LSS). Length of LSS is a measurement of similarity between two sequences. The longest spl epsiv -similar subsequence (LSS) problem is a generalization of the well known longest common subsequence (LCS) problem. The goal of this paper is to find an optimal solution to the LSS problem. We analyzed the property of partial sums and proposed to focus on the minimum matched partial sum which leads to an optimal solution to LSS while reduce the problem space. As the LSS problem has optimal structure, we proposed an algorithm based on dynamic programming technique. Time complexity of this algorithm is O(m sup 2 n sup 2 ). By using a property of the partial sums, we reduced the time complexity to O(mn(m+n)).