In-Memory Distributed Matrix Computation Processing and Optimization

The use of large-scale machine learning and data mining methods is becoming ubiquitous in many application domains ranging from business intelligence and bioinformatics to self-driving cars. These methods heavily rely on matrix computations, and it is hence critical to make these computations scalab...

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Veröffentlicht in:	2017 IEEE 33rd International Conference on Data Engineering (ICDE) S. 1047 - 1058
Hauptverfasser:	Yongyang Yu, Mingjie Tang, Aref, Walid G., Malluhi, Qutaibah M., Abbas, Mostafa M., Ouzzani, Mourad
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.04.2017
Schlagworte:	Computational modeling Data models distributed computing Distributed databases Generators Matrix computation Optimization query optimization Sparks Sparse matrices
ISSN:	2375-026X
Online-Zugang:	Volltext
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