Paul Hodor

dblp:174/6621 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2016
0000-0001-7770-8347ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author

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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

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

TopicWeightPapersLastEvidence papers
High-performance computing › cluster computing
cluster deployment
0.212016
cl-dash: rapid configuration and deployment of Hadoop clusters for bioinformatics research in the cloud · Bioinform. 2016
Bioinformatics and computational biology › genomics
genomic data analysis
0.112016
cl-dash: rapid configuration and deployment of Hadoop clusters for bioinformatics research in the cloud · Bioinform. 2016

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

mapreduce · 0.5cloud provisioning · 0.5
YearPublicationVenuePosition
2016 RexDB & GSM Provide Iterative Cohort Selection for GWAS: A Case Study of Gains through Interoperability of Two Open-Source Technologies
Owen McGettrick, Ezekiel Maier, Natasha Sefcovic, Leon Rozenblit, Paul Hodor
AMIA5
2016 cl-dash: rapid configuration and deployment of Hadoop clusters for bioinformatics research in the cloud
abstract
UNLABELLED: : One of the solutions proposed for addressing the challenge of the overwhelming abundance of genomic sequence and other biological data is the use of the Hadoop computing framework. Appropriate tools are needed to set up computational environments that facilitate research of novel bioinformatics methodology using Hadoop. Here, we present cl-dash, a complete starter kit for setting up such an environment. Configuring and deploying new Hadoop clusters can be done in minutes. Use of Amazon Web Services ensures no initial investment and minimal operation costs. Two sample bioinformatics applications help the researcher understand and learn the principles of implementing an algorithm using the MapReduce programming pattern. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://bitbucket.org/booz-allen-sci-comp-team/cl-dash.git. CONTACT: [email protected].
Paul Hodor, Amandeep Chawla, Lauren Neal
Bioinform.1