Ernest: efficient performance prediction for large-scale advanced analytics
Impact in
Classified as
- Journal
- Networked Systems Design and Implementation
In The Last Decade
doi.org/w9049190 →Countries where authors are citing Ernest: efficient performance prediction for large-scale advanced analytics
This map shows the geographic impact of Ernest: efficient performance prediction for large-scale advanced analytics. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ernest: efficient performance prediction for large-scale advanced analytics with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ernest: efficient performance prediction for large-scale advanced analytics more than expected).
Fields of papers citing Ernest: efficient performance prediction for large-scale advanced analytics
This network shows the impact of Ernest: efficient performance prediction for large-scale advanced analytics. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Ernest: efficient performance prediction for large-scale advanced analytics.
About Ernest: efficient performance prediction for large-scale advanced analytics
This paper, published in 2016, received 230 indexed citations . Written by Shivaram Venkataraman, Zongheng Yang, Michael J. Franklin, Benjamin Recht and Ion Stoica covering the research area of Computer Networks and Communications and Information Systems. It is primarily cited by scholars working on Computer Networks and Communications (192 citations), Information Systems (188 citations), Artificial Intelligence (76 citations), Hardware and Architecture (32 citations) and Computer Vision and Pattern Recognition (30 citations). Published in Networked Systems Design and Implementation.
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This paper is also available at doi.org/w9049190.