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ApproxHadoop: Bringing Approximations to MapReduce Frameworks
2017
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We conclude that our framework and system can make approximation easily accessible to many application domains using the MapReduce model. ...
In this paper, we We propose and evaluate a framework for creating and running approximation-enabled MapReduce programs. ...
Verma and his co-authors for the barrier-less extension to Hadoop [47] . We also thank David Carrera and the ASPLOS reviewers for comments that helped us improve this paper. ...
doi:10.7282/t3cj8j0p
fatcat:f4yflbwl5bhpvhen3wbjebvnou
Uncertainty Propagation in Data Processing Systems
2018
Proceedings of the ACM Symposium on Cloud Computing - SoCC '18
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We implement this framework in a system called UP-MapReduce, and use it to modify ten applications, including AI/ML, image processing and trend analysis applications to process uncertain data. ...
We present a framework for uncertainty propagation (UP) that allows developers to modify precise implementations of DAG nodes to process uncertain inputs with modest effort. ...
We implement the proposed framework in UP-MapReduce, an extension of the Hadoop MapReduce, to handle uncertainty propagation (UP). ...
doi:10.1145/3267809.3267833
dblp:conf/cloud/ManousakisGBRN18
fatcat:ng32pkxpyfdqldatji4u6ku3yi
Approximation with Error Bounds in Spark
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1812.01823v2 fatcat:3zs7uuuoxjdbjcdtzcf6pa2qlaOther Versions
We introduce a sampling framework to support approximate computing with estimated error bounds in Spark. ...
We have implemented a prototype of our framework called ApproxSpark, and used it to implement five approximate applications from different domains. ...
ApproxHadoop [25] introduces approximation to the MapReduce [26] paradigm. ...
arXiv:1812.01823v3
fatcat:4jhtqgkrq5fodlzdpgzqkujooe
Online Adaptive Approximate Stream Processing with Customized Error Control
2019
IEEE Access
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To address these problems, we present an online adaptive approximate processing framework with a delicate combination of data learning, sampling, and quality control. ...
In approximate processing on stream data, most works focus on how to approximate online arrival data. However, the efficiency of approximation needs to consider multiple aspects. ...
Based on the MapReduce framework, Goiri et al. [19] designed a prototype system, ApproxHadoop, to implement approximation-enabled applications through input data sampling and task dropping. ...
doi:10.1109/access.2019.2899825
fatcat:6dcw5efe55ctjo2huhm5ehhfvy
Constant- Time Approximate Sliding Window Framework with Error Control
2019
2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC)
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It is, to our knowledge, the first general purpose sliding window programable framework that combines constant-time aggregations with error bounded approximate computing techniques. ...
In this context, Stream Processing frameworks need to combine efficient algorithms with low computational complexity to manage sliding windows, with the ability to adjust resource demands for different ...
[20] proposed an approximate computing set of mechanisms for batch processing for Hadoop, called ApproxHadoop. ...
doi:10.1109/isorc.2019.00031
dblp:conf/isorc/VillalbaC19
fatcat:52lcxpoz6nh43lbrut4vgf2nb4
Input responsiveness: using canary inputs to dynamically steer approximation
2016
SIGPLAN notices
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Based on these runtime tests, the approximation that best fits the desired accuracy constraints is selected and applied to the full input to produce an approximate result. ...
to automatically control how program approximation is applied on an input-by-input basis. ...
Acknowledgements We would like to thank our anonymous reviewers for their comments and suggestions. We also thank our shepherd, Cindy Rubio González, for her valuable feedback. ...
doi:10.1145/2980983.2908087
fatcat:irsndunbwrfxtbnmq673qnmqxy
Input responsiveness: using canary inputs to dynamically steer approximation
2016
Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation - PLDI 2016
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Based on these runtime tests, the approximation that best fits the desired accuracy constraints is selected and applied to the full input to produce an approximate result. ...
to automatically control how program approximation is applied on an input-by-input basis. ...
Acknowledgements We would like to thank our anonymous reviewers for their comments and suggestions. We also thank our shepherd, Cindy Rubio González, for her valuable feedback. ...
doi:10.1145/2908080.2908087
dblp:conf/pldi/LaurenzanoHSMMT16
fatcat:vnqayw7manffhd67ujannbtyyi
Approximating distributed graph algorithms
[article]
2019
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This leads to the question: do we always need to know the exact answer for a large graph? ...
This has led to the development of many distributed graph processing systems. But these existing graph processing systems take several minutes or even hours to execute popular graph algorithms. ...
GRASS adopted them for approximation analytics. ApproxHadoop [GBNN15] enables approximation in MapReduce Systems. ...
doi:10.18419/opus-10383
fatcat:fxnkryebsrcv3gwdr5silz7idq
Wavelet-based Algorithms for Approximate Processing in the Big Data Era
[article]
2021
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functions of MapReduce programs. ...
not be affected by the overhead of the three MapReduce jobs. ...
MapReduce for L2-error Synopses
Index of Algorithms BUDGreedyAbs Distributed heuristic algorithm, that given a budget constraint, approximates the L ∞ -optimal wavelet synopsis. ...
doi:10.26240/heal.ntua.21878
fatcat:xlllphljkjdfhhgx7m4itgauye