Space odyssey: efficient exploration of scientific data

Mirjana Pavlovic, Eleni Tzirita Zacharatou, Darius Sidlauskas, T. Heinis, A. Ailamaki
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引用次数: 7

Abstract

Advances in data acquisition---through more powerful supercomputers for simulation or sensors with better resolution---help scientists tremendously to understand natural phenomena. At the same time, however, it leaves them with a plethora of data and the challenge of analysing it. Ingesting all the data in a database or indexing it for an efficient analysis is unlikely to pay off because scientists rarely need to analyse all data. Not knowing a priori what parts of the datasets need to be analysed makes the problem challenging. Tools and methods to analyse only subsets of this data are rather rare. In this paper we therefore present Space Odyssey, a novel approach enabling scientists to efficiently explore multiple spatial datasets of massive size. Without any prior information, Space Odyssey incrementally indexes the datasets and optimizes the access to datasets frequently queried together. As our experiments show, through incrementally indexing and changing the data layout on disk, Space Odyssey accelerates exploratory analysis of spatial data by substantially reducing query-to-insight time compared to the state of the art.
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太空漫游:科学数据的有效探索
数据采集方面的进步——通过更强大的超级计算机进行模拟或分辨率更高的传感器——极大地帮助科学家理解自然现象。然而,与此同时,这给他们留下了过多的数据和分析这些数据的挑战。获取数据库中的所有数据或将其编入索引以进行有效分析不太可能取得成功,因为科学家很少需要分析所有数据。如果事先不知道数据集的哪些部分需要分析,就会使问题变得具有挑战性。仅分析这些数据子集的工具和方法相当罕见。因此,在本文中,我们提出了太空漫游,这是一种新颖的方法,使科学家能够有效地探索大规模的多个空间数据集。在没有任何先验信息的情况下,Space Odyssey会对数据集进行增量索引,并优化对经常一起查询的数据集的访问。正如我们的实验所示,通过增量索引和改变磁盘上的数据布局,Space Odyssey通过大大减少从查询到洞察的时间来加速空间数据的探索性分析。
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