D
Daniel Barbará
Researcher at George Mason University
Publications - 140
Citations - 8926
Daniel Barbará is an academic researcher from George Mason University. The author has contributed to research in topics: Cluster analysis & Anomaly detection. The author has an hindex of 42, co-authored 140 publications receiving 8730 citations. Previous affiliations of Daniel Barbará include Princeton University & University of Ulm.
Papers
More filters
Journal ArticleDOI
How to assign votes in a distributed system
TL;DR: In this article, the authors studied both of these strategies in detail and showed that they are not equivalent in general (although they are in some cases) and proved several interesting properties.
Proceedings Article
Sleepers and Workaholics: Caching Strategies in Mobile Environments
Daniel Barbará,Tomasz Imielinski +1 more
TL;DR: In this paper, a taxonomy of different cache invalidation strategies and study the impact of client's disconnection times on their performance is presented, and the authors determine that for the units which are often disconnected (sleepers) the best cache invalidization strategy is based on signatures previously used for efficient file comparison.
Journal ArticleDOI
The management of probabilistic data
TL;DR: A data model that includes probabilities associated with the values of the attributes, and the notion of missing probabilities is introduced for partially specified probability distributions, offers a richer descriptive language allowing the database to more accurately reflect the uncertain real world.
Proceedings ArticleDOI
Sleepers and workaholics: caching strategies in mobile environments
Daniel Barbará,Tomasz Imielinski +1 more
TL;DR: A taxonomy of different cache invalidation strategies is proposed and it is determined that for the units which are often disconnected (sleepers) the best cache invalidations strategy is based on signatures previously used for efficient file comparison, and for units which is connected most of the time (workaholics), the best Cache invalidation strategy isbased on the periodic broadcast of changed data items.
Proceedings ArticleDOI
On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking
TL;DR: A topic model that automatically captures the thematic patterns and identifies emerging topics of text streams and their changes over time and is comparable to, and sometimes better than, the original LDA in predicting the likelihood of unseen documents.