Parameter-Free Search of Time-Series Discord

Luo, Wei; Gallagher, Marcus; Wiles, Janet

doi:10.1007/s11390-013-1330-8

Cited by 15 publications

(2 citation statements)

References 22 publications

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“…Another popular and well-studied data series primitive, the discord (Yankov et al, 2008;Keogh et al, 2005;Yeh et al, 2016;Senin et al, 2015;Luo et al, 2013), is proposed to discover subsequences that represent outliers. Surprisingly, the solutions to this problem that have been proposed in the literature are not as effective and scalable as practice requires.…”

Section: Introductionmentioning

confidence: 99%

Matrix profile goes MAD: variable-length motif and discord discovery in data series

Linardi

Zhu

Palpanas

et al. 2020

Data Min Knowl Disc

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In the last fifteen years, data series motif and discord discovery have emerged as two useful and well-used primitives for data series mining, with applications to many domains, including robotics, entomology, seismology, medicine, and climatology. Nevertheless, the state-of-the-art motif and discord discovery tools still require the user to provide the relative length. Yet, in several cases, the choice of length is critical and unforgiving. Unfortunately, the obvious brute-force solution, which tests all lengths within a given range, is computationally untenable. In this work, we introduce a new framework, which provides an exact and scalable motif and discord discovery algorithm that efficiently finds all motifs and discords in a given range of lengths. We evaluate our approach with five diverse real datasets, and demonstrate that it is up to 20 times faster than the state-of-the-art. Our results also show that removing the unrealistic assumption that the user knows the correct length, can often produce more intuitive and actionable results, which could have otherwise been missed.

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Section: Introductionmentioning

confidence: 99%

Matrix profile goes MAD: variable-length motif and discord discovery in data series

Linardi

Zhu

Palpanas

et al. 2020

Data Min Knowl Disc

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“…As in [74,27], we assume the presence of n synchronized, equal length time series stored in a flat relational table R : Definition 4.1. Q is a range selection query on the timestamp attribute:…”

Section: Problem Definitionmentioning

confidence: 99%

Similarity-aware query refinement for data exploration

Albarrak¹

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Database users are easily overwhelmed by the sheer size of data found in large-scale scientific and financial databases. Exploring these databases to make sense of the explored data and to discover interesting insights (i.e., data exploration) has been, and still is, a hideous and labour-intensive task, especially for non-expert users with no solid background of the underlying data. Some three decades ago, the database research community noticed the limitation of traditional DBMS in supporting users for data exploration tasks. Since then, the research community has proposed and designed various effective and efficient data exploration techniques to assist users in extracting interesting insights from their data. An instance of these techniques is the Query Refinement technique. In query refinement techniques, users' queries are assumed to be imprecise, i.e., the returned result does not meet some user-defined constraints. Accordingly, the goal of query refinement techniques is to automatically refine these imprecise queries to maximize users' satisfaction with the results. In particular, the predicates of the queries are carefully modified so that the returned results satisfy the user-defined constraints. Since users' constraints on the queries results are diverse and miscellaneous, this thesis focuses on two specific forms of constraints in exploring relational and sequential data, namely, 1) user-defined aggregate constraints on the result, and 2) user-defined correlation constraints of time series data. These constraints are common in real world applications because they represent an upper level view of the result that is easier to understand and digest than the raw result itself. This thesis addresses the limitations of current query refinement techniques that are oblivious to the similarity of the refined queries to the users' initial queries. Specifically, users' initial (and imprecise) queries are defined as anchor points for which the similarity of its corresponding refined queries are computed over the whole refinement space. Consequently, the similarity-aware query refinement problem is formulated as a search problem, which aims to balance the trade-off between minimizing the deviation from satisfying a constraint on the query result, and maximizing the similarity of the refined query to the initial one. Searching for a trade-off between satisfying a constraint on the result of a query and maximizing the similarity introduces various challenges. A common challenge shared by many query refinement problems is that finding an optimal trade-off i Lastly, I would like to formally thank my sponsor, Al-Imam Muhammad Ibn Saud Islamic University, for providing the financial support which made this journey possible.

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The Framework and Applications of Anomalous Subsequence Detection in Streaming Data Analysis and Process Monitoring in Intelligent Manufacturing

Sutrisno,

Yang

2023

Automation, Collaboration, &Amp; E-Services

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Parameter-Free Search of Time-Series Discord

Cited by 15 publications

References 22 publications

Matrix profile goes MAD: variable-length motif and discord discovery in data series

Matrix profile goes MAD: variable-length motif and discord discovery in data series

Similarity-aware query refinement for data exploration

The Framework and Applications of Anomalous Subsequence Detection in Streaming Data Analysis and Process Monitoring in Intelligent Manufacturing

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