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Ivan de Prado

dblp:124/7218 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

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

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

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 87% Cloud and datacenter computing · 13%

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

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › data-parallel programming
mapreduce
0.112012
Tuple MapReduce: Beyond Classic MapReduce · ICDM 2012
Parallel and multicore computing › data-parallel programming
mapreduce programming model
0.112012
Tuple MapReduce: Beyond Classic MapReduce · ICDM 2012
Cloud and datacenter computing › cluster computing framework
mapreduce framework
0.012012
Tuple MapReduce: Beyond Classic MapReduce · ICDM 2012
YearPublicationVenuePosition
2014 Tuple MapReduce and Pangool: an associated implementation
Pedro Ferrera, Ivan de Prado, Eric Palacios, Jose Luis Fernandez-Marquez, Giovanna Di Marzo Serugendo
Knowl. Inf. Syst.2
2012 Tuple MapReduce: Beyond Classic MapReduce
abstract
This paper proposes Tuple Map Reduce, a new foundational model extending Map Reduce with the notion of tuples. Tuple Map Reduce allows to bridge the gap between the low-level constructs provided by Map Reduce and higher-level needs required by programmers, such as compound records, sorting or joins. This paper presents as well Pangool, an open-source framework implementing Tuple Map Reduce. Pangool eases the design and implementation of applications based on Map Reduce and increases their flexibility, still maintaining Hadoop's performance.
Pedro Ferrera, Ivan de Prado, Eric Palacios, Jose Luis Fernandez-Marquez, Giovanna Di Marzo Serugendo
ICDM2