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Eduardo J. Ruiz
Researcher at University of California, Riverside
Publications - 6
Citations - 337
Eduardo J. Ruiz is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Approximation algorithm & Automatic summarization. The author has an hindex of 5, co-authored 6 publications receiving 324 citations. Previous affiliations of Eduardo J. Ruiz include Florida International University & University of California.
Papers
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Proceedings ArticleDOI
Correlating financial time series with micro-blogging activity
TL;DR: The problem of correlating micro-blogging activity with stock-market events, defined as changes in the price and traded volume of stocks, is studied and it is shown that even relatively small correlations between price and micro- bloggers features can be exploited to drive a stock trading strategy that outperforms other baseline strategies.
Journal ArticleDOI
Facilitating Document Annotation Using Content and Querying Value
TL;DR: This paper presents algorithms that identify structured attributes that are likely to appear within the document, by jointly utilizing the content of the text and the query workload, to identify attributes of interest.
Proceedings ArticleDOI
Patentssearcher: a novel portal to search and explore patents
Vagelis Hristidis,Eduardo J. Ruiz,Alejandro Eduardo Gutiérrez Hernández,Fernando Farfán,Ramakrishna Varadarajan +4 more
TL;DR: The PatentsSearcher system is presented, whose key contribution is to leverage the domain semantics to improve the quality of discovery and ranking, and which offers other novel functionalities to help users locate and navigate relevant and important patents or applications.
Proceedings Article
Efficient Filtering on Hidden Document Streams
TL;DR: This paper studies how to best utilize a constrained streaming access interface to maximize the number of retrieved relevant items, with respect to a classifier, expressed as a set of rules, and proposes exact and bounded approximation algorithms.
CADS: A Collaborative Adaptive Data Sharing Platform 1
TL;DR: This paper proposes CADS, a Collaborative Adaptive Data Sharing platform, where the information demand of the community-e.g., query workload-is exploited to annotate the data at insertion-time and uses the appl ication of CADS on the Business Continuity Information Network (BCIN) of South Florida as a motivating example.