摘要翻译：
现代科学数据主要由大量技术收集的大量数据集组成，存储在非常多样化且往往不兼容的数据仓库中。一般而言，在e-science环境中，集成由单个企业内不同资源组成的分布、异构、动态的“虚拟组织”的服务被认为是一个关键而紧迫的需求。在过去的十年里，由于探测器（平板到数字到马赛克）、望远镜和空间仪器的发展，天文学已经成为一个数据丰富的领域。虚拟观测站方法包括全球现有所有天文档案的共同标准下的联合会，以及数据分析、数据挖掘和数据勘探应用。这种努力背后的主要驱动力是，一旦基础设施完成，它将允许一种新的多波长、多时代的科学，这是几乎无法想象的。数据挖掘，即数据库中的知识发现，虽然是提取海量数据集所包含的科学信息的主要方法，但由于它必须协调对不同计算环境的透明访问、算法的可伸缩性、资源的可重用性等复杂问题，因此提出了关键问题。本文概述了虚拟天文台MDS的现状，以及在DAME（Data Mining&Exploration）项目中引入先进数据挖掘方法的进展和计划。
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英文标题：
《Mining Knowledge in Astrophysical Massive Data Sets》
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作者：
M. Brescia, G. Longo, F. Pasian
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最新提交年份：
2010
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分类信息：

一级分类：Physics        物理学
二级分类：Instrumentation and Methods for Astrophysics        天体物理学仪器和方法
分类描述：Detector and telescope design, experiment proposals. Laboratory Astrophysics. Methods for data analysis, statistical methods. Software, database design
探测器和望远镜设计，实验建议。实验室天体物理学。资料分析方法，统计方法。软件，数据库设计
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Modern scientific data mainly consist of huge datasets gathered by a very large number of techniques and stored in very diversified and often incompatible data repositories. More in general, in the e-science environment, it is considered as a critical and urgent requirement to integrate services across distributed, heterogeneous, dynamic "virtual organizations" formed by different resources within a single enterprise. In the last decade, Astronomy has become an immensely data rich field due to the evolution of detectors (plates to digital to mosaics), telescopes and space instruments. The Virtual Observatory approach consists into the federation under common standards of all astronomical archives available worldwide, as well as data analysis, data mining and data exploration applications. The main drive behind such effort being that once the infrastructure will be completed, it will allow a new type of multi-wavelength, multi-epoch science which can only be barely imagined. Data Mining, or Knowledge Discovery in Databases, while being the main methodology to extract the scientific information contained in such MDS (Massive Data Sets), poses crucial problems since it has to orchestrate complex problems posed by transparent access to different computing environments, scalability of algorithms, reusability of resources, etc. In the present paper we summarize the present status of the MDS in the Virtual Observatory and what is currently done and planned to bring advanced Data Mining methodologies in the case of the DAME (DAta Mining & Exploration) project.
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PDF链接：
https://arxiv.org/pdf/1010.3796