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Algorithms
Special Issues
Data Mining in Multi-Core, Many-Core and Cloud Era
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Data Mining in Multi-Core, Many-Core and Cloud Era

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 May 2010) | Viewed by 7983

Special Issue Editor

Prof. Dr. Gagan Agrawal

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA

Special Issue Information

Dear Colleagues,

In recent years, multi-core and many-core architectures have become the only means for scaling performance. This development, however,
has lead to new challenges for application development. Another independent development has been related to cloud computing, where computing hardware and services can be accessed and paid for like utilities. Again, this is creating new challenges in developing scalable applications.

This special issue will focus on data mining in the multi-core, many-core and cloud era. Specific topics of interest include:

1. Data Mining algorithms for multi-core and many-core architectures, including GPUs.
2. Data Mining case studies on new emerging architectures.
3. Data Mining algorithms and implementation development with existing cloud infrastructures (including, but not limited to map-reduce and its variants).
4. Data Mining for optimizing and tuning applications on multi-cores and GPGPUs
5. Data Mining for resource provisioning and other performance optimizations in cloud environments.

Prof. Dr. Gagan Agrawal,
Guest Editor

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Published Papers (1 paper)

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Research

244 KiB  
Article
Base Oils Biodegradability Prediction with Data Mining Techniques
by Sihem Ben Abdelmelek, Saloua Saidane and Malika Trabelsi
Algorithms 2010, 3(1), 92-99; https://doi.org/10.3390/algor3010092 - 23 Feb 2010
Cited by 1 | Viewed by 7481
Abstract
In this paper, we apply various data mining techniques including continuous numeric and discrete classification prediction models of base oils biodegradability, with emphasis on improving prediction accuracy. The results show that highly biodegradable oils can be better predicted through numeric models. In contrast, [...] Read more.
In this paper, we apply various data mining techniques including continuous numeric and discrete classification prediction models of base oils biodegradability, with emphasis on improving prediction accuracy. The results show that highly biodegradable oils can be better predicted through numeric models. In contrast, classification models did not uncover a similar dichotomy. With the exception of Memory Based Reasoning and Decision Trees, tested classification techniques achieved high classification prediction. However, the technique of Decision Trees helped uncover the most significant predictors. A simple classification rule derived based on this predictor resulted in good classification accuracy. The application of this rule enables efficient classification of base oils into either low or high biodegradability classes with high accuracy. For the latter, a higher precision biodegradability prediction can be obtained using continuous modeling techniques. Full article
(This article belongs to the Special Issue Data Mining in Multi-Core, Many-Core and Cloud Era)
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