Special Issue "Data Mining in Multi-Core, Many-Core and Cloud Era"

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

Deadline for manuscript submissions: closed (31 May 2010)

Special Issue Editor

Guest Editor
Prof. Dr. Gagan Agrawal (Website)

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

Published Papers (1 paper)

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Research

Open AccessArticle Base Oils Biodegradability Prediction with Data Mining Techniques
Algorithms 2010, 3(1), 92-99; doi:10.3390/algor3010092
Received: 30 December 2009 / Revised: 24 January 2010 / Accepted: 28 January 2010 / Published: 23 February 2010
PDF Full-text (244 KB) | HTML Full-text | XML Full-text
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 [...] 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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