Use of eCommons for rapid dissemination of COVID-19 research
In order to maximize the discoverability of COVID-19 research, and to conform with repository best practices and the requirements of publishers and research funders, we provide special guidance for COVID-19 submissions.
Predicting Indexer Performance in a Distributed Digital Library
|dc.contributor.author||French, James C.||en_US|
|dc.description.abstract||Resource discovery in a distributed digital library poses many challenges, one of which is how to choose search engines for query distribution, given a query and a set of search engines. This paper focuses on search engine performance as a criterion for search engine selection and defines two measurements of search engine performance: availability - will the search engine respond within a time limit and response time - how quickly will the search engine respond, given that it responds at all. We predicted both of these performance characteristics with a variety of algorithms, all of which required little computation time and combined past performance data for each search engine into a succinct record. We used operational data from the NCSTRL distributed digital library to make and evaluate predictions, and we found that simple prediction methods performed as well as more complex methods and that prediction accuracy was closely related to data consistency.||en_US|
|dc.title||Predicting Indexer Performance in a Distributed Digital Library||en_US|