E. Fox

Publications
Influence

E. Fox, J. A. Shaw
Computer Science
TREC
1993

The TREC-2 project at Virginai Tech focused on methods for combining the evidence from multiple retrieval runs to improve performance over any single retrieval method. Expand

1,071
146
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Extended Boolean information retrieval

G. Salton, E. Fox, Harry Wu
Computer Science
CACM
1 November 1983

A new, extended Boolean information retrieval system is introduced which is intermediate between the Boolean system of query processing and the vector processing model. Expand

1,048
63

Removal policies in network caches for World-Wide Web documents

M. Abrams, C. Standridge, G. Abdulla, E. Fox, S. Williams
Computer Science
SIGCOMM
1 October 1996

We determine the maximum possible hit rate and weighted hit rate that a cache could ever achieve, and the removal policy that maximizes hit rate in each of the studied workloads. Expand

Caching Proxies: Limitations and Potentials

M. Abrams, C. Standridge, G. Abdulla, S. Williams, E. Fox
Computer Science
World Wide Web J.
18 July 1995

We assess the potential of proxy servers to cache documents retrieved with the HTTP protocol, and show that a proxy server has a 30-50% maximum possible hit rate no matter how it is designed. Expand

Social media use by government: from the routine to the critical

Andrea L. Kavanaugh, E. Fox, +6 authors Lexing Xie
Business, Computer Science
dg.o '11
12 June 2011

Social media (i.e., Twitter, Facebook, Flickr, YouTube) and other services with user-generated content have made a staggering amount of information (and misinformation) available. Expand

Streams, structures, spaces, scenarios, societies (5s): A formal model for digital libraries

Marcos André Gonçalves, E. Fox, L. Watson, Neill A. Kipp
Computer Science
TOIS
1 April 2004

We propose the fundamental abstractions of Streams, Structures, Spaces, Scenarios, and Societies (5S), which allow us to define digital libraries rigorously and usefully. Expand

Automatic document metadata extraction using support vector machines

Hui Han, C. Lee Giles, Eren Manavoglu, H. Zha, Zhenyue Zhang, E. Fox
Computer Science
Joint Conference on Digital Libraries…
27 May 2003

We describe a support vector machine classification based method for metadata extraction from header part of research papers and show that it outperforms other machine learning methods on the same task. Expand

Combining the Evidence of Multiple Query Representations for Information Retrieval

N. Belkin, P. Kantor, E. Fox, J. A. Shaw
Computer Science
Inf. Process. Manag.
1 May 1995

We report on two studies in the TREC-2 program that investigated the effect on retrieval performance of combination of multiple representations of TREC topics. Expand

Social media use by government: From the routine to the critical

Andrea L. Kavanaugh, E. Fox, +5 authors Lexing Xie
Sociology, Computer Science
Gov. Inf. Q.
1 October 2012

This paper presents findings from a exploratory study we conducted between June and December 2010 with government officials in Arlington, VA (and the greater National Capitol Region around Washington, D.C.), with the broad goal of understanding social media use by government officials as well as community organizations, businesses, and the public at large. Expand

Extending the boolean and vector space models of information retrieval with p-norm queries and multiple concept types

E. Fox
Computer Science, Mathematics
1983

Information retrieval systems accept user queries and respond by identifying documents presumed to be relevant to those queries. Expand

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