Parallel data processing with MapReduce: a survey

By Kyong-Ha Lee, Yoon-Joon Lee, Hyunsik Choi, Yon Dohn Chung, Bongki Moon. SGMD, 2012

708 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/283f1674a4e6a6a5e2dd11fafaadcd3dff2d17fb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Amir Salman Avestimehr
2. Songze Li
3. Mohammad Ali Maddah-Ali
4. Rodrigo Neves Calheiros
5. Rajkumar Buyya
6. Yonggang Wen
7. Marcos Dias de Assunção
8. Sergio Ramírez-Gallego

In Computer Vision and Pattern Recognition

1. Salvador Garcia
2. Xuelong Li
3. Francisco Herrera
4. Tat-seng Chua
5. Sergio Ramírez-Gallego
6. Alberto Fernández
7. Min Chen
8. Chandan K. Reddy

In Information Systems

1. Rajkumar Buyya
2. Rodrigo Neves Calheiros
3. Yonggang Wen
4. Francisco Herrera
5. Amir Salman Avestimehr
6. Salvador Garcia
7. Songze Li
8. Alberto Fernández

Citations in the corpus

• A survey on platforms for big data analytics (2014, background): “A good survey about MapReduce for parallel data processing is available in [10].”
• Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce (2018, background): “MapReduce (MR) has established as a de-facto solution that comprises all the previous capabilities [18 – 20].”
• Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to? (2019, methodology): “To this end, MapReduce has established as a de­facto solution to simplify the implementation of these techniques [66].”
• Big Data computing and clouds: Trends and future directions (2013, background): “[67] present a survey about the features, benefits, and limitations of MapReduce for parallel data analytics.”
• Coded MapReduce (2015, background): “(A,10)[1], (B,25)[1], (C,15)[1], (D,6)[1] (A,10)[2], (B,25)[2], (C,15)[2], (D,6)[2] (A,10)[3], (B,25)[3], (C,15)[3], (D,6)[3] (A,10)[4], (B,25)[4], (C,15)[4], (D,6)[4] (A,10)[5], (B,25)[5], (C,15)[5], (D,6)[5] (A,10)[6], (B,25)[6], (C,15)[6], (D,6)[6] Server 2 Server 3 Server 4 Chapter 1 Chapter 2 C”
• Tupleware: Distributed Machine Learning on Small Clusters (2014, background): “Iterations: Many ML algorithms are most naturally expressed iteratively, but neither MapReduce nor SQL effectively supports iteration [26, 16].”
• Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “Please refer to [276] and [277] for more details on this topic.”