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Jeffrey Hoyt

dblp:99/2440 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Databases, data management, data science and information retrieval · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data models and query languages · 50% Data integration and cleaning · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data integration and cleaning › data integration system
multidatabase query
0.112009
Galaxy: Encouraging Data Sharing among Sources with Schema Variants · ICDE 2009
Data models and query languages
schema management
0.112009
Galaxy: Encouraging Data Sharing among Sources with Schema Variants · ICDE 2009

Methods — techniques the papers use, named apart from their topics

schema versioning · 0.1
YearPublicationVenuePosition
2009 Galaxy: Encouraging Data Sharing among Sources with Schema Variants
abstract
This demonstration presents Galaxy, a schema manager that facilitates easy and correct data sharing among autonomous but related, evolving data sources. Galaxy reduces heterogeneity by helping database developers identify, reuse, customize, and advertise related schema components. The central idea is that as schemata are customized, Galaxy maintains a derivation graph, and exploits it for data exchange, discovery, and multi-database query over the "galaxy" of related data sources. Using a set of schemata from the biomedical domain, we demonstrate how Galaxy facilitates schema and data sharing.
Kris Mork, Leonard J. Seligman, Arnon Rosenthal, Michael Morse, Chris Wolf, Jeffrey Hoyt, Kenneth P. Smith
ICDE6
2008 An Exchange Format for Multimodal Annotations
Thomas Schmidt 0005, Susan Duncan, Oliver Ehmer, Jeffrey Hoyt, Michael Kipp, Dan Loehr, Magnus Magnusson, R. Travis Rose, Han Sloetjes
LREC4
2003 Securely sharing neuroimagery
abstract
Shared scientific data, such as neuroimagery, offers great benefits to science. However, data owners must exercise custodial responsibilities which can conflict with the unhindered sharing of their data. Given simple choices of sharing widely or not at all, the result will frequently be no sharing. We hypothesize that neuroimagery sharing will be enhanced if data owners are provided with well-defined intermediate levels of data visibility. In this paper, we describe a broadly applicable data sharing model, Structured Sharing Communities (SSC), in which data becomes incrementally visible to communities structured as a complete partial-order; the associated properties of Privacy and Fairness regulate access to shared data. Within SSC, a customized policy space is defined capturing the sharing relationships among specific collaborators.
Kenneth P. Smith, Vipin Swarup, Sushil Jajodia, Donald B. Faatz, Todd Cornett, Jeffrey Hoyt
CIKM6