第一届、第二届跨学科时间序列分析研讨会报告

Themis Palpanas, V. Beckmann
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引用次数: 48

摘要

与现代工业操作和科学实验相关的时间序列数据分析正在将计算能力和资源推向极限。为了分析现有的和(更重要的是)未来的非常大的时间序列集合,需要新技术和更高效、更智能的算法的发展。跨学科时间序列分析研讨会的两个版本汇集了来自计算机科学、天体物理学、神经科学、工程、电力网络和音乐领域的数据分析师。这些讲习班的重点是不同领域的不同应用的需要,以及学术界和工业界在时间序列管理和分析领域的进展。在本文中,我们总结了两场研讨会的经验和成果,突出了相关的技术现状和开放的研究问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Report on the First and Second Interdisciplinary Time Series Analysis Workshop (ITISA)
The analysis of time-series data associated with modernday industrial operations and scientific experiments is now pushing both computational power and resources to their limits. In order to analyze the existing and (more importantly) future very large time series collections, new technologies and the development of more efficient and smarter algorithms are required. The two editions of the Interdisciplinary Time Series Analysis Workshop brought together data analysts from the fields of computer science, astrophysics, neuroscience, engineering, electricity networks, and music. The focus of these workshops was on the requirements of different applications in the various domains, and also on the advances in both academia and industry, in the areas of time-series management and analysis. In this paper, we summarize the experiences presented in and the results obtained from the two workshops, highlighting the relevant state-ofthe- art-techniques and open research problems.
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