Apache Spark
By Matei A. Zaharia, Reynold Xin, Patrick Wendell, Tathagata Das, Michael Armbrust, Ankur Dave, Xiangrui Meng, Josh Rosen, Shivaram Venkataraman, M. Franklin and 4 more. 2016
1,660 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Information Systems, Information Systems and Management
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Ion Stoica
- Alexey Tumanov
- Michael I. Jordan
- Bilal Mirza
- Sandeep Kaushik
- Jie Wang
- Wei Wang
- Howard Choi
In Information Systems
- Ion Stoica
- Alexey Tumanov
- Hendrik Boogaard
- Ioannis N. Athanasiadis
- Allard de Wit
- Wagner Meira Jr
- Michael I. Jordan
- Christos Pylianidis
In Information Systems and Management
Citations in the corpus
- Ray: A Distributed Framework for Emerging AI Applications (2017, background): “As with existing cluster computing frameworks, such as Spark [64], and Dryad [28], the object store is limited to immutable data.”
- Machine learning for large-scale crop yield forecasting (2020, methodology): “We used Apache Spark dataframes (Zaharia et al. 2016) for data preprocessing and feature design, and applied machine learning using the scikit-learn python package (Pedregosa et al. 2011).”
- Fairness and Transparency of Machine Learning for Trustworthy Cloud Services (2018, methodology): “By using high performance and scalable technologies, such as BSC COMPSs [12] and Apache Spark [13], Lemonade enables the processing of very large amount of data, hiding all back-end complexity from users and allowing them to focus mainly on the construction of solutions, models and services.”
- Machine Learning and Integrative Analysis of Biomedical Big Data (2019, methodology): “From the original MapReduce framework, various computational platforms have arisen which are suitable for large-scale ML, such as Apache Spark [213].”
- Big data in healthcare: management, analysis and future prospects (2019, background): “It is a unified engine for distributed data processing that includes higher-level libraries for supporting SQL queries ( Spark SQL ), streaming data ( Spark Streaming ), machine learning ( MLlib ) and graph processing ( GraphX ) [18].”
- Error Metrics and Performance Fitness Indicators for Artificial Intelligence and Machine Learning in Engineering and Sciences (2020, methodology): “An integral part of the wide spread of integrating ML into new research areas is due to the availability of user-friendly and easy-to-use software packages that simplifies the process of ML by utilizing pre-defined algorithms and training/validation procedure [26–30].”