VLDB 2026 Research / reviewers in the wild / expert
Nena M. Marin
dblp:47/7123
· DBLP profile ↗
1ranked-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 · 1Databases, 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 |
Data mining · 87% Recommender systems · 13% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining
clustering |
0.1 | 1 | 2009 | Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data · KDD 2009 |
Data mining › clustering
co-clustering |
0.1 | 1 | 2009 | Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data · KDD 2009 |
Parallel and multicore computing › parallel programming models
dataflow programming |
0.1 | 1 | 2009 | Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data · KDD 2009 |
Parallel and multicore computing
parallel data mining |
0.1 | 1 | 2009 | Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data · KDD 2009 |
Recommender systems
collaborative filtering |
0.0 | 1 | 2009 | Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data · KDD 2009 |
Methods — techniques the papers use, named apart from their topics
weighted co-clustering · 0.2dataflow programming · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix dataabstractAll Netflix Prize algorithms proposed so far are prohibitively costly for large-scale production systems. In this paper, we describe an efficient dataflow implementation of a collaborative filtering (CF) solution to the Netflix Prize problem [1] based on weighted coclustering [5]. The dataflow library we use facilitates the development of sophisticated parallel programs designed to fully utilize commodity multicore hardware, while hiding traditional difficulties such as queuing, threading, memory management, and deadlocks. Srivatsava Daruru, Nena M. Marin, Matt Walker, Joydeep Ghosh |
KDD | 2 |