ASAP-DM: a framework for automatic selection of analytic platforms for data mining

IF 2.4 Q1 Computer Science SICS Software-Intensive Cyber-Physical Systems Pub Date : 2019-08-17 DOI:10.1007/s00450-019-00408-7
Manuel Fritz, Osama Muazzen, Michael Behringer, Holger Schwarz
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Abstract

The plethora of analytic platforms escalates the difficulty of selecting the most appropriate analytic platform that fits the needed data mining task, the dataset as well as additional user-defined criteria. Especially analysts, who are rather focused on the analytics domain, experience difficulties to keep up with the latest developments. In this work, we introduce the ASAP-DM framework, which enables analysts to seamlessly use several platforms, whereas programmers can easily add several platforms to the framework. Furthermore, we investigate how to predict a platform based on specific criteria, such as lowest runtime or resource consumption during the execution of a data mining task. We formulate this task as an optimization problem, which can be solved by today’s classification algorithms. We evaluate the proposed framework on several analytic platforms such as Spark, Mahout, and WEKA along with several data mining algorithms for classification, clustering, and association rule discovery. Our experiments unveil that the automatic selection process can save up to 99.71% of the execution time due to automatically choosing a faster platform.
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asp - dm:用于自动选择数据挖掘分析平台的框架
过多的分析平台增加了选择适合所需数据挖掘任务、数据集以及其他用户定义标准的最合适的分析平台的难度。特别是那些专注于分析领域的分析人员,在跟上最新发展方面遇到了困难。在这项工作中,我们介绍了asp - dm框架,它使分析人员能够无缝地使用多个平台,而程序员可以轻松地将多个平台添加到框架中。此外,我们还研究了如何基于特定的标准来预测平台,例如在执行数据挖掘任务期间最低的运行时间或资源消耗。我们将这个任务表述为一个优化问题,它可以通过今天的分类算法来解决。我们在几个分析平台(如Spark、Mahout和WEKA)上评估了所提出的框架,以及用于分类、聚类和关联规则发现的几种数据挖掘算法。我们的实验表明,由于自动选择更快的平台,自动选择过程可以节省高达99.71%的执行时间。
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SICS Software-Intensive Cyber-Physical Systems
SICS Software-Intensive Cyber-Physical Systems COMPUTER SCIENCE, HARDWARE & ARCHITECTURE-
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