article · 2014
Scaling data mining in massively parallel dataflow systems
Cited by 0Abstract and details only
Abstract
The demand for mining large datasets using shared-nothing clusters is steadily on the rise. Despite the availability of parallel processing paradigms such as MapReduce, scalable data mining is still a tough problem. Naïve ports of existing algorithms to platforms like Hadoop exhibit various scalability bottlenecks, which prevent their application to large real-world datasets. These bottlenecks arise from various pitfalls that have to be overcome, including the scalability of the mathematical operations of the algorithm, the performance of the system when executing iterative computations, as well as its ability to efficiently execute meta learning techniques such as cross-validation and ensemble learning.
Cite this paper
Schelter, S. (2014). Scaling data mining in massively parallel dataflow systems. https://doi.org/10.1145/2602622.2602631
Schelter, Sebastian. “Scaling data mining in massively parallel dataflow systems.” 2014, pp. 11–15, https://doi.org/10.1145/2602622.2602631.
Schelter, Sebastian. 2014. “Scaling data mining in massively parallel dataflow systems.” https://doi.org/10.1145/2602622.2602631.
Schelter, S. (2014) ‘Scaling data mining in massively parallel dataflow systems’. Available at: https://doi.org/10.1145/2602622.2602631.
@article{schelter2014scaling,
title = {{Scaling data mining in massively parallel dataflow systems}},
author = {Schelter, Sebastian},
pages = {11--15},
year = {2014},
doi = {10.1145/2602622.2602631}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.