Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists
Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists
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DOI：
10.1145/3502736
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发表时间：
2022-03
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影响因子：
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通讯作者：
A. Varde
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文献类型：
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作者：
A. Varde
在科学领域中，实验结果通常被绘制为二维图形图（aka graphs），描绘因变量与自变量，以帮助对过程进行可视化分析。重复进行实验室实验消耗大量的时间和资源，从而激发了对计算估计的需求。目标是估计在给定输入条件的实验中获得的图，并估计将导致期望的图的条件。现有的估计方法通常不能满足目标应用的准确性和效率需求。我们开发了一种称为AutoDomainMine的计算估计方法，该方法将复杂科学数据的聚类和分类集成在一个框架中，以便自动化科学家的经典学习方法。由此从现有实验的数据库中发现的知识用作估计的基础。面临的挑战包括保持领域语义聚类，找到匹配策略的分类，达到一个良好的平衡之间的阐述和简洁性，同时显示估计结果的基础上，目标用户的需求，并得出客观的措施，以捕捉主观用户的兴趣。这些和其他挑战都在这项工作中得到解决。AutoDomainMine方法用于构建计算估计系统，并使用材料科学中的真实的数据进行严格评估。我们的评估证实了AutoDomainMine在计算估计中提供了所需的准确性和效率。它可以扩展到其他科学和工程领域，如生物信息学和纳米技术等领域的子过程的适应。
Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.