论文标题
Valmod：一个套件，用于简化数据系列中可变长度基序的精确检测
VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series
论文作者
论文摘要
数据系列图案发现代表了数据系列挖掘的最有用的原始方法之一，并应用于许多领域，例如机器人技术，昆虫学，地震学，医学和气候学等。最新的主题发现工具仍然要求用户提供主题长度。但是，在某些情况下，基序长度的选择对于它们的检测至关重要。不幸的是，在给定范围内测试所有长度的明显蛮力解决方案在计算上是站不住脚的，并且不能为在不同分辨率（即长度）下对基序进行排名。我们演示了Valmod，即我们的可扩展图案发现算法，该算法有效地在给定的长度范围内找到所有基序，并输出基序的长度不变排名。此外，我们通过新提出的元数据结构来支持分析过程，该结构可帮助用户选择最有希望的模式长度。该演示旨在详细说明所提出的方法的步骤，展示我们的算法和相应的图形见解如何使用户有效地识别正确的主题。 （发表在2018年ACM Sigmod会议上的论文。）
Data series motif discovery represents one of the most useful primitives for data series mining, with applications to many domains, such as robotics, entomology, seismology, medicine, and climatology, and others. The state-of-the-art motif discovery tools still require the user to provide the motif length. Yet, in several cases, the choice of motif length is critical for their detection. Unfortunately, the obvious brute-force solution, which tests all lengths within a given range, is computationally untenable, and does not provide any support for ranking motifs at different resolutions (i.e., lengths). We demonstrate VALMOD, our scalable motif discovery algorithm that efficiently finds all motifs in a given range of lengths, and outputs a length-invariant ranking of motifs. Furthermore, we support the analysis process by means of a newly proposed meta-data structure that helps the user to select the most promising pattern length. This demo aims at illustrating in detail the steps of the proposed approach, showcasing how our algorithm and corresponding graphical insights enable users to efficiently identify the correct motifs. (Paper published in ACM Sigmod Conference 2018.)
