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Renu Kurien Bostwick

dblp:03/819 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

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

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 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.

Artificial intelligence
1 paper
Planning, search and constraint satisfaction · 77% Multi-agent systems · 23%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 77% Distributed systems · 23%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › plan execution
plan execution monitoring		 0.112007
Expectation Failure as a Basis for Agent-Based Model Diagnosis and Mixed Initiative Model Adaptation during Anomalous Plan Execution · IJCAI 2007
High-performance computing
scientific computing systems		 0.012004
Efficient Execution of Computation Modules in a Model with Massive Data · ICDE 2004
Knowledge, reasoning and agents › Multi-agent systems
agent-based simulation		 0.012007
Expectation Failure as a Basis for Agent-Based Model Diagnosis and Mixed Initiative Model Adaptation during Anomalous Plan Execution · IJCAI 2007

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

mixed-initiative adaptation · 0.1agent-based model diagnosis · 0.1
YearPublicationVenuePosition
2007 Expectation Failure as a Basis for Agent-Based Model Diagnosis and Mixed Initiative Model Adaptation during Anomalous Plan Execution
Alice M. Mulvehill, Brett Benyo, Michael T. Cox, Renu Kurien Bostwick
 IJCAI4
2004 Efficient Execution of Computation Modules in a Model with Massive Data
abstract
Models and simulations for analyzing difficult real-world problems often deal with massive amounts of data. The data problem is compounded when the analysis must be repeatedly run to perform what-if analyses. One such model is the Integrated Consumable Item Support (ICIS) Model developed for the U.S. Defense Logistics Agency (DLA). It models DLA's ability to satisfy future wartime requirements for parts, fuel, and food. ICIS uses a number of computation modules to project demands, model sourcing, and identify potential problem items for various commodities and military services. These modules are written in a variety of computer languages and must work together to generate an ICIS analysis.
Gary Kratkiewicz, Renu Kurien Bostwick, Geoffrey S. Knauth
 ICDE2