IEEE Transactions on Parallel and Distributed Systems
TPDS Seeks Editor-in-Chief for 2018-2020 Term
The IEEE Computer Society seeks applicants for the position of editor-in-chief of the IEEE Transactions on Parallel and Distributed Systems, serving a three-year term starting 1 January 2018. Prospective candidates are asked to provide a complete curriculum vitae, a brief plan for the publication's future, and a letter of support from their institution or employer (as PDF files) by 1 March 2017. For more information on the search process and to submit application materials, click here or please contact: Kimberly Sperka, email@example.com.
IEEE Transactions on Parallel and Distributed Systems (TPDS) is a scholarly archival journal published monthly. Parallelism and distributed computing are foundational research and technology to rapidly advance computer systems and their applications. Read the full scope of TPDS
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From the April 2017 Issue
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment
By Jianguo Chen, Kenli Li, Zhuo Tang, Kashif Bilal, Shui Yu, Chuliang Weng, and Keqin Li
With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability. With the expansion of the scale of the random forest model and the Spark cluster, the advantage of the PRF algorithm is more obvious.
Editorials and Announcements
- According to Thomson Reuters' 2013 Journal Citation Report, TPDS has an impact factor of 2.173.
- TPDS celebrates its 25th Anniversary. Editor-in-Chief David A. Bader says, "Congratulations to TPDS on its Silver Jubilee! For 25 years, TPDS has been the parallel and distributed computing community's flagship journal for research breakthroughs!"
- Get Your Journals as eBooks for Free
- Editor's Note (January 2017)
- Editor's Note (January 2016)
- Editor's Note (January 2015)
- State of the Journal (January 2014)
- Editor's Note: EIC Farewell and New EIC Introduction (Dec 2013)
- Editor's Note (November 2013)
- Editor's Note (January 2013)
- Editor's Note (April 2012)
- Editor's Note (January 2012)
- Editorial: Media Center (November 2011)
- Editor's Note: How to Write Research Articles in Computing and Engineering Disciplines by Ivan Stojmenovic
- Full Supplemental PDF of Editor's Note: How to Write Research Articles in Computing and Engineering Disciplines by Ivan Stojmenovic and Veljko Milutinovic (PDF)
- Special Issue on Trust, Security, and Privacy in Parallel and Distributed Systems (Feb 2014)
- Special Issue on Cloud Computing (June 2013)
- Special Issue on Cyber-Physical Systems (CPS) (Sept 2012)
- Special Section on Many-Task Computing (June 2011)
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