The coming age of pervasive data processing

Jan S. Rellermeyer, Sobhan Omranian Khorasani, Dan Graur, Apourva Parthasarathy

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Emerging Big Data analytics and machine learning applications require a significant amount of computational power. While there exists a plethora of large-scale data processing frameworks which thrive in handling the various complexities of data-intensive workloads, the ever-increasing demand of applications have made us reconsider the traditional ways of scaling (e.g., scale-out) and seek new opportunities for improving the performance. In order to prepare for an era where data collection and processing occur on a wide range of devices, from powerful HPC machines to small embedded devices, it is crucial to investigate and eliminate the potential sources of inefficiency in the current state of the art platforms. In this paper, we address the current and upcoming challenges of pervasive data processing and present directions for designing the next generation of large-scale data processing systems.

Original languageEnglish
Title of host publication2019 18th International Symposium on Parallel and Distributed Computing (ISPDC)
Subtitle of host publicationProceedings
EditorsAlexandru Iosup, Florin Pop, Radu Prodan, Alexandru Uta
Number of pages8
ISBN (Electronic)978-1-7281-3801-5
ISBN (Print)978-1-7281-3802-2
Publication statusPublished - 2019
Event18th International Symposium on Parallel and Distributed Computing, ISPDC 2019 - Amsterdam, Netherlands
Duration: 5 Jun 20197 Jun 2019


Conference18th International Symposium on Parallel and Distributed Computing, ISPDC 2019

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Big Data
  • Machine Learning
  • Systems
  • Performance
  • Efficiency


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