Hyracks: A flexible and extensible foundation for data-intensive computing

By Vinayak R. Borkar, Michael J. Carey, Raman Grover, Nicola Onose, Rares Vernica. IEEE International Conference on Data Engineering, 2011

294 citations recorded by the source corpus.

How researchers see this paper

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MLbase: A Distributed Machine-learning System (2013, background): “[14] X. Feng et al. Towards a unified architecture for in-RDBMS analytics.”
Probabilistic Demand Forecasting at Scale (2017, background): “Dataflows have proven themselves as a useful abstraction for general distributed, dataintensive computing [8, 1, 6].”
Shark: fast data analysis using coarse-grained distributed memory (2012, background): “Hyracks and Asterix [12, 11] are exploring more flexible building blocks than just map and reduce to build massively parallel databases.”
One Trillion Edges: Graph Processing at Facebook-Scale (2015, methodology): “DAG-based execution systems that generalize the MapReduce model to broader computation models, such as Hyracks [11], Spark [45], and Dryad [28], can also do graph and iterative computation.”
The Stratosphere platform for big data analytics (2014, methodology): “A different approach is followed in the Asterix project, where the Hyracks engine [15] executes physical operators (e.”
Automating model search for large scale machine learning (2015, background): “Weka [5], MLlib [36], Vowpal Wabbit [3], Hyracks [15] and Mahout [2] are notable open-source ML libraries.”
TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries (2015, background): “Weka [6], MLlib [25], Vowpal Wabbit [3], Hyracks [15] and Mahout [2] are notable open-source ML libraries.”