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Chris Carnes

dblp:67/3374 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 2Databases, data management, data science and information retrieval · 1

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
Database system architecture and tuning · 44% Indexing and storage engines · 44% Transaction processing and concurrency control · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Indexing and storage engines › query indexing
boolean expression indexing
0.011999
Scalable Trigger Processing · ICDE 1999
Database system architecture and tuning › active database
trigger processing
0.011999
Scalable Trigger Processing · ICDE 1999
Transaction processing and concurrency control
concurrency control
0.011999
Scalable Trigger Processing · ICDE 1999

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

trigger caching · 0.0expression signatures · 0.0
YearPublicationVenuePosition
2005 "iCuro"™ (Intensive Care Unit Real-time Observer) : A Real-Time ICU Patient Data Integration and Presentation System
Willa H. Drummond, Chris Carnes, Samuel W. Coons, Kevin Birkett, Jingqi Xian, Rebecca Roys
AMIA2
2002 CentraView: A Real-time ICU Patient Data Integration and Presentation System
Willa H. Drummond, Chris Carnes, Samuel W. Coons, Kevin Birkett
AMIA2
1999 Scalable Trigger Processing
abstract
Current database trigger systems have extremely limited scalability. This paper proposes a way to develop a truly scalable trigger system. Scalability to large numbers of triggers is achieved with a trigger cache to use the main memory effectively, and a memory-conserving selection predicate index based on the use of unique expression formats called expression signatures. A key observation is that if a very large number of triggers are created, many will have the same structure, except for the appearance of different constant values. When a trigger is created, tuples are added to special relations created for expression signatures to hold the trigger's constants. These tables can be augmented with a database index or main-memory index structure to serve as a predicate index. The design presented also uses a number of types of concurrency to achieve scalability, including token (tuple)-level, condition-level, rule action-level and data-level concurrency.
Eric N. Hanson, Chris Carnes, Mohan Konyala, Lloyd Noronha, Sashi Parthasarathy, J. B. Park, Albert Vernon
ICDE2