Starfish: A Self-tuning System for Big Data Analytics.
- Authors
- Herodotos HerodotouHarold LimGang LuoNedyalko BorisovLiang Dong
- Journal
- Conference on Innovative Data Systems Research
In The Last Decade
doi.org/w5468504 →Countries where authors are citing Starfish: A Self-tuning System for Big Data Analytics.
This map shows the geographic impact of Starfish: A Self-tuning System for Big Data 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 Starfish: A Self-tuning System for Big Data Analytics. with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Starfish: A Self-tuning System for Big Data Analytics. more than expected).
Fields of papers citing Starfish: A Self-tuning System for Big Data Analytics.
This network shows the impact of Starfish: A Self-tuning System for Big Data Analytics.. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Starfish: A Self-tuning System for Big Data Analytics..
About Starfish: A Self-tuning System for Big Data Analytics.
This paper, published in 2011, received 404 indexed citations . Written by Herodotos Herodotou, Harold Lim, Gang Luo, Nedyalko Borisov, Liang Dong and Shivnath Babu covering the research area of Computer Networks and Communications and Information Systems. It is primarily cited by scholars working on Information Systems (335 citations), Computer Networks and Communications (310 citations) and Artificial Intelligence (97 citations). Published in Conference on Innovative Data Systems Research.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w5468504.