S
Sushovan De
Researcher at Arizona State University
Publications - 10
Citations - 570
Sushovan De is an academic researcher from Arizona State University. The author has contributed to research in topics: Tuple & Database design. The author has an hindex of 6, co-authored 10 publications receiving 424 citations.
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Proceedings Article
Mental Health Discourse on reddit: Self-Disclosure, Social Support, and Anonymity
Munmun De Choudhury,Sushovan De +1 more
TL;DR: These findings reveal, for the first time, the kind of unique information needs that a social media like reddit might be fulfilling when it comes to a stigmatic illness, and expand the understanding of the role of the social web in behavioral therapy.
Proceedings Article
AI-MIX: using automated planning to steer human workers towards better crowdsourced plans
TL;DR: The implementation of AI-MIX, a tour plan generation system that uses automated checks and alerts to improve the quality of plans created by human workers; and a preliminary evaluation of the effectiveness of steering provided by automated planning.
Journal ArticleDOI
BayesWipe: A Scalable Probabilistic Framework for Improving Data Quality
TL;DR: This article provides a method for correcting individual attribute values in a structured database using a Bayesian generative model and a statistical error model learned from the noisy database directly, to avoid the necessity for a domain expert or clean master data.
Proceedings ArticleDOI
BayesWipe: A multimodal system for data cleaning and consistent query answering on structured bigdata
TL;DR: This paper provides a method for correcting individual attribute values in a structured database using a Bayesian generative model and a statistical error model learned from the noisy database directly, to avoid the necessity for a domain expert or clean master data.
Posted Content
Bayes Networks for Supporting Query Processing Over Incomplete Autonomous Databases
TL;DR: Empirical studies are presented to demonstrate that at higher levels of incompleteness, when multiple attribute values are missing, Bayesian networks do provide a significantly higher classification accuracy and the relevant possible answers retrieved by the queries reformulated using Bayesian Networks provide higher precision and recall than AFDs while keeping query processing costs manageable.