设计和挖掘多TB天文学档案：斯隆数字巡天
Designing and Mining Multi-Terabyte Astronomy Archives: The Sloan Digital Sky Survey
摘要：
下一代天文学数字档案将以高分辨率覆盖宇宙的大部分地区，涵盖从X射线到紫外线、光学和红外线等多种波长。档案将存放在不同的地理位置。斯隆数字巡天计划是首批项目之一，它将创建一个覆盖10 000平方度天空的5波长目录（见http：//www.sdss.org/）。这个数TB的数据库中的2亿个对象将主要具有数字属性，定义了一个100多个维度的空间。这个空间中的点具有高度相关的分布。该档案将使天文学家能够交互式地探索数据。数据访问将得到多维空间索引和其他索引的帮助。数据将以多种方式进行分区。由最流行的属性组成的小标记对象可以加快频繁搜索的速度。在多个服务器之间拆分数据可以实现并行、可扩展的I/O，并对数据应用并行处理。散列技术允许高效的聚类和成对比较算法，这些算法可以很好地并行化。随机采样的子集允许在桌面上调试其他大型查询。中央服务器将运行一个数据泵，支持触及大部分数据的扫描搜索。预期的查询需要与角距离相关的特殊运算符和对象属性（如形状、颜色、速度向量或时间行为）的复杂相似性测试。这些问题提出了有趣的数据管理挑战。
英文摘要：
The next-generation astronomy digital archives will cover most of the universe at fine resolution in many wave-lengths, from X-rays to ultraviolet, optical, and infrared. The archives will be stored at diverse geographical locations. One of the first of these projects, the Sloan Digital Sky Survey (SDSS) will create a 5-wavelength catalog over 10,000 square degrees of the sky (see http://www.sdss.org/). The 200 million objects in the multi-terabyte database will have mostly numerical attributes, defining a space of 100+ dimensions. Points in this space have highly correlated distributions. The archive will enable astronomers to explore the data interactively. Data access will be aided by a multidimensional spatial index and other indices. The data will be partitioned in many ways. Small tag objects consisting of the most popular attributes speed up frequent searches. Splitting the data among multiple servers enables parallel, scalable I/O and applies parallel processing to the data. Hashing techniques allow efficient clustering and pair-wise comparison algorithms that parallelize nicely. Randomly sampled subsets allow debugging otherwise large queries at the desktop. Central servers will operate a data pump that supports sweeping searches that touch most of the data. The anticipated queries require special operators related to angular distances and complex similarity tests of object properties, like shapes, colors, velocity vectors, or temporal behaviors. These issues pose interesting data management challenges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
