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Anthony Tomasic

Researcher at Carnegie Mellon University

Publications -  101
Citations -  4544

Anthony Tomasic is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Query optimization & Distributed database. The author has an hindex of 34, co-authored 100 publications receiving 4297 citations. Previous affiliations of Anthony Tomasic include University of California & Stanford University.

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Book ChapterDOI

Partial Answers for Unavailable Data Sources

TL;DR: This paper proposes a novel approach where, in presence of unavailable data sources, the answer to a query is a partial answer, and presents a framework for partial answers and proposes three algorithms for the evaluation of queries in Presence of unavailable Sources, the construction of incremental queries and the Evaluation of parachute queries.

Citizen motivation on the go : the role of psychological empowerment

TL;DR: Evaluating the impact of three principles of psychological empowerment, namely perceived self-efficacy, sense of community and causal importance, on public transport passengers’ motivation to report issues and complaints while on the move revealed that self- efficacy and causal important increased participation in short bursts and increased perceptions of service quality over longer periods.
Journal ArticleDOI

Citizen Motivation on the Go: The Role of Psychological Empowerment

TL;DR: In this article, the authors evaluated the impact of three principles of psychological empowerment, namely perceived self-efficacy, sense of community and causal importance, on public transport passengers' motivation to report issues and complaints while on the move.

The Efficacy of GlOSS for the Text Database Discovery Problem

TL;DR: A practical method for attacking the text database discovery problem based on estimating the result size of a query and a database is presented and GlOSS--Glossary of Servers Server is evaluated using four different semantics to answer a user''s queries.
Proceedings ArticleDOI

NER Systems that Suit User's Preferences: Adjusting the Recall-Precision Trade-off for Entity Extraction

TL;DR: This method based on "tweaking" an existing learned sequential classifier to change the recall-precision tradeoff, guided by a user-provided performance criterion, is described and proves to be both simple and effective.