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arXiv 0911.0505astro-ph.IMcs.DBcs.IRphysics.data-an
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天文科学数据挖掘

Scientific Data Mining in Astronomy

Kirk Borne

摘要:

我们描述了数据挖掘算法在天文学研究问题中的应用。我们认为,数据挖掘一直是天文学研究的基础,因为数据挖掘是基于证据的发现,包括分类,聚类和新颖性发现的基础。这些算法代表了在大型数据库中发现的一套主要计算工具,这在数据密集型天文学时代将变得越来越重要。回顾了天文学数据挖掘的历史实例,然后讨论了预计在未来十年中最大的数据生产项目之一:大型综合巡天望远镜(LSST)。为了促进天文学中数据驱动的发现,我们设想了一种新的天文学和天体物理学面向数据的研究范式-天体信息学。天文信息学被描述为现代数据密集型天文学的一种研究方法和教育必要性。大型时域巡天(如LSST)的一个重要应用领域是实时天空事件(包括移动物体、光度可变物体和瞬变现象)的快速识别、表征和分类。我们描述了一个可能的实现的分类代理这样的事件,它结合了几个天体信息学技术:用户注释,语义标记,元数据标记,异构数据集成,分布式数据挖掘。这些类型的合作分类和发现的方法在其他科学学科的例子。

英文摘要:

We describe the application of data mining algorithms to research problems in astronomy. We posit that data mining has always been fundamental to astronomical research, since data mining is the basis of evidence-based discovery, including classification, clustering, and novelty discovery. These algorithms represent a major set of computational tools for discovery in large databases, which will be increasingly essential in the era of data-intensive astronomy. Historical examples of data mining in astronomy are reviewed, followed by a discussion of one of the largest data-producing projects anticipated for the coming decade: the Large Synoptic Survey Telescope (LSST). To facilitate data-driven discoveries in astronomy, we envision a new data-oriented research paradigm for astronomy and astrophysics -- astroinformatics. Astroinformatics is described as both a research approach and an educational imperative for modern data-intensive astronomy. An important application area for large time-domain sky surveys (such as LSST) is the rapid identification, characterization, and classification of real-time sky events (including moving objects, photometrically variable objects, and the appearance of transients). We describe one possible implementation of a classification broker for such events, which incorporates several astroinformatics techniques: user annotation, semantic tagging, metadata markup, heterogeneous data integration, and distributed data mining. Examples of these types of collaborative classification and discovery approaches within other science disciplines are presented.

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