OverCite: a distributed, cooperative citeseer

  • Authors:
  • Jeremy Stribling;Jinyang Li;Isaac G. Councill;M. Frans Kaashoek;Robert Morris

  • Affiliations:
  • MIT Computer Science and Artificial Intelligence Laboratory;New York University and MIT Computer Science and Artificial Intelligence Laboratory via University of California, Berkeley;Pennsylvania State University;MIT Computer Science and Artificial Intelligence Laboratory;MIT Computer Science and Artificial Intelligence Laboratory

  • Venue:
  • NSDI'06 Proceedings of the 3rd conference on Networked Systems Design & Implementation - Volume 3
  • Year:
  • 2006

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Abstract

CiteSeer is a popular online resource for the computer science research community, allowing users to search and browse a large archive of research papers. CiteSeer is expensive: it generates 35 GB of network traffic per day, requires nearly one terabyte of disk storage, and needs significant human maintenance. OverCite is a new digital research library system that aggregates donated resources at multiple sites to provide CiteSeer-like document search and retrieval. OverCite enables members of the community to share the costs of running CiteSeer. The challenge facing OverCite is how to provide scalable and load-balanced storage and query processing with automatic data management. OverCite uses a three-tier design: presentation servers provide an identical user interface to CiteSeer's; application servers partition and replicate a search index to spread the work of answering each query among several nodes; and a distributed hash table stores documents and metadata, and coordinates the activities of the servers. Evaluation of a prototype shows that OverCite increases its query throughput by a factor of seven with a nine-fold increase in the number of servers. OverCite requires more total storage and network bandwidth than centralized CiteSeer, but spreads these costs over all the sites. OverCite can exploit the resources of these sites to support new features such as document alerts and to scale to larger data sets.