MapReduce for Data Intensive Scientific Analyses

By Jaliya Ekanayake, S. Pallickara, Geoffrey C. Fox. 2008 IEEE Fourth International Conference on eScience, 2008

457 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/cc7f20f2ad4db10ea58facc10c265236a2811755

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. Ion Stoica
2. Scott Shenker
3. Mosharaf Chowdhury
4. Matei A. Zaharia
5. Ravi Kumar
6. Andrew Tomkins
7. Flavio Chierichetti
8. Chandan K. Reddy

In Information Systems

1. Ion Stoica
2. Mosharaf Chowdhury
3. Matei A. Zaharia
4. Scott Shenker
5. Andrew Tomkins
6. Ravi Kumar
7. Flavio Chierichetti
8. Derek Gordon Murray

In Information Systems and Management

1. Andrew Tomkins
2. Ravi Kumar
3. Flavio Chierichetti
4. Matei A. Zaharia
5. Scott Shenker
6. Ion Stoica

Citations in the corpus

• Twister: a runtime for iterative MapReduce (2010, background): “From the years of experience in applying MapReduce to various scientific applications we identified a set of extensions to the programming model and improvements to its architecture that will expand the applicability of MapReduce to more classes of applications.”
• A survey on platforms for big data analytics (2014, background): “CGL MapReduce [16],[17] is another work that focuses on improving the performance of MapReduce iterative tasks.”
• NDA: Near-DRAM acceleration architecture leveraging commodity DRAM devices and standard memory modules (2015, background): “Data processing in many data-intensive applications in domains such as high-energy physics, biology, scientific computing, and geology can be split this way [47].”
• Spark: Cluster Computing with Working Sets (2010, background): “The need to extendMapReduce to support iterative jobs was also recognized by Twister [6, 12], a MapReduce framework that allows long-lived map tasks to keep static data in memory between jobs.”
• CIEL: A Universal Execution Engine for Distributed Data-Flow Computing (2011, methodology): “multiple jobs are chained together and the job latency is multiplied [12, 21, 25, 28, 34].”
• Max-cover in map-reduce (2010, methodology): “Map-Reduce-based realizations of existing algorithms have been extensively developed in machine learning, including algorithms for regression, naive Bayes, k-means clustering, principal and independent component analysis, EM, and SVM [6]; see also [22, 10].”
• HaLoop (2010, background): “Twister [6] is a stream-based MapReduce framework that supports iterative programs, in which mappers and reducers are long running with distributed memory caches.”