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To support complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused heavily on the design of systems to support training complex models on large datasets.
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MauveDB: Supporting model-based user views in database systems
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Bayesstore: Managing large, uncertain data repositories with probabilistic graphical models
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Learning deep architectures for AI
Y. Bengio · 2009
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Matrix factorization techniques for recommender systems
Y. Koren, R. Bell, and C. Volinsky · 2009
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Matrix completion from power-law distributed samples
R. Meka et al · 2009
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A contextual-bandit approach to personalized news article recommendation
L. Li, W. Chu, J. Langford, and R. E. Schapire · 2010
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The hadoop distributed file system
K. Shvachko et al · 2010
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The case for predictive database systems: Opportunities and challenges
M. Akdere et al · 2011
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Probabilistic Databases
D. Suciu, D. Olteanu, C. Ré, and C. Koch · 2011
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Materialized views
R. Chirkova and J. Yang · 2012
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Towards a unified architecture for in-rdbms analytics
X. Feng, A. Kumar, B. Recht, and C. Ré · 2012
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Powergraph: Distributed graph-parallel computation on natural graphs
Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing
M. Zaharia et al · 2012
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Massively parallel databases and mapreduce systems
S. Babu and H. Herodotou · 2013
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Mlbase: A distributed machine-learning system
T. Kraska, A. Talwalkar, J. C. Duchi, R. Griffith, M. J. Franklin, and M. I. Jordan · 2013
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Sparkler: Supporting large-scale matrix factorization
B. Li, S. Tata, and Y. Sismanis · 2013
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MLI: An API for distributed machine learning
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Tachyon: Reliable, memory speed storage for cluster computing frameworks
H. Li, A. Ghodsi, M. Zaharia, S. Shenker, and I. Stoica · 2014
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J. E. Gonzalez et al · 2012
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Materialization optimizations for feature selection workloads
C. Zhang, A. Kumar, and C. Ré · 2014
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