arXiv subjects

Mark Levene

Publications and source records attributed to Mark Levene.

40 records · Page 3Linked to original sources

arXiv2003

Search and Navigation in Relational Databases

We present a new application for keyword search within relational databases, which uses a novel algorithm to solve the join discovery problem by finding Memex-like trails through the graph of foreign key dependencies. It differs from previous efforts in the algorithms used, in the presentation mechanism and in the use of primary-key only database queries at query-time to maintain a fast response for users. We present examples using the DBLP data set.

cs.DB↗

arXiv2003

The Best Trail Algorithm for Assisted Navigation of Web Sites

We present an algorithm called the Best Trail Algorithm, which helps solve the hypertext navigation problem by automating the construction of memex-like trails through the corpus. The algorithm performs a probabilistic best-first expansion of a set of navigation trees to find relevant and compact trails. We describe the implementation of the algorithm, scoring methods for trails, filtering algorithms and a new metric called \emph{potential gain} which measures the potential of a page for future navigation opportunities.

cs.DS↗

arXiv2002

A Two-Player Game of Life

We present a new extension of Conway's game of life for two players, which we call p2life. P2life allows one of two types of token, black or white, to inhabit a cell, and adds competitive elements into the birth and survival rules of the original game. We solve the mean-field equation for p2life and determine by simulation that the asymptotic density of p2life approaches 0.0362.

cond-mat.soft↗

arXiv2002

A Stochastic Model for the Evolution of the Web

Recently several authors have proposed stochastic models of the growth of the Web graph that give rise to power-law distributions. These models are based on the notion of preferential attachment leading to the ``rich get richer'' phenomenon. However, these models fail to explain several distributions arising from empirical results, due to the fact that the predicted exponent is not consistent with the data. To address this problem, we extend the evolutionary model of the Web graph by including a non-preferential component, and we view the stochastic process in terms of an urn transfer model. By making this extension, we can now explain a wider variety of empirically discovered power-law distributions provided the exponent is greater than two. These include: the distribution of incoming links, the distribution of outgoing links, the distribution of pages in a Web site and the distribution of visitors to a Web site. A by-product of our results is a formal proof of the convergence of the standard stochastic model (first proposed by Simon).

cond-mat.dis-nn↗