Report on the First and Second Interdisciplinary Time Series Analysis Workshop (ITISA)

Palpanas, Themis; Beckmann, Volker

doi:10.1145/3377391.3377400

Cited by 42 publications

(28 citation statements)

References 32 publications

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“…Data series 1 have gathered the attention of the data management community for more than two decades (Agrawal et al, 1993;Jagadish et al, 1995;Rafiei and Mendelzon, 1998;Chakrabarti et al, 2002;Papadimitriou and Yu, 2006;Camerra et al, 2010;Kashyap and Karras, 2011;Wang et al, 2013b;Camerra et al, 2014;Dallachiesa et al, 2014;Zoumpatianos et al, 2016;Yagoubi et al, 2017;Jensen et al, 2017;Palpanas, 2017;Kondylakis et al, 2018;Peng et al, 2018;Gogolou et al, 2019;Echihabi et al, 2018Echihabi et al, , 2019Yagoubi et al, 2020;Kondylakis et al, 2019;Peng et al, 2020a;Peng et al, 2020b;Palpanas, 2020;Gogolou et al, 2020). They are now one of the most common types of data, present in virtually every scientific and social domain (Palpanas, 2015;Raza et al, 2015;Mirylenka et al, 2016;Keogh, 2011;Palpanas and Beckmann, 2019;Bagnall et al, 2019).…”

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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“…An increasing number of applications across many diverse domains continuously produce very large amounts of data series 1 (such as in finance, environmental sciences, astrophysics, neuroscience, engineering, and others [1]- [3]), which makes them one of the most common types of data. When these sequence collections are generated (often times composed of a large number of short series [3], [4]), users need to query and analyze them (e.g., detect anomalies [5], [6]). This process is heavily dependent on data series similarity search (which apart from being a useful query in itself, also lies at the core of several machine learning methods, such as, clustering, classification, motif and outlier detection, etc.)…”

Section: Introductionmentioning

confidence: 99%

Data Series Indexing Gone Parallel

Peng

2020

2020 IEEE 36th International Conference on Data Engineering (ICDE)

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Data series similarity search is a core operation for several data series analysis applications across many different domains. However, the state-of-the-art techniques fail to deliver the time performance required for interactive exploration, or analysis of large data series collections. In this Ph.D. work, we present the first data series indexing solutions, for both on-disk and in-memory data, that are designed to inherently take advantage of multi-core architectures, in order to accelerate similarity search processing times. Our experiments on a variety of synthetic and real data demonstrate that our approaches are up to orders of magnitude faster than the alternatives. More specifically, our on-disk solution can answer exact similarity search queries on 100GB datasets in a few seconds, and our inmemory solution in a few milliseconds, which enables real-time, interactive data exploration on very large data series collections.

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“…of data series 1 (such as in finance, environmental sciences, astrophysics, neuroscience, engineering, multimedia, etc. [7,34,37,55]), which makes them one of the most common types of data. When these sequence collections are generated (often times composed of a large number of short series [37]), users may need to query and analyze them as soon as they become available.…”

mentioning

confidence: 99%

“…[7,34,37,55]), which makes them one of the most common types of data. When these sequence collections are generated (often times composed of a large number of short series [37]), users may need to query and analyze them as soon as they become available. This process is heavily dependent on data series similarity search (which apart from being a useful query in itself, also lies at the core of several machine learning methods, such as, clustering, classification, motif and outlier detection, etc.)…”

mentioning

confidence: 99%

“…For example, the current state-of-the-art index, ADS+ [19,53], requires more than 4min to answer any single exact query on a moderately sized 250GB sequence collection. Moreover, index construction time also becomes a significant bottleneck in the analysis process [53], especially in cases where new data arrive frequently and need to be indexed [37]. Thus, tradi-tional solutions and systems are inefficient at, or incapable of managing and processing the voluminous sequence collections that already exist in several domains.…”

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confidence: 99%

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ParIS+: Data Series Indexing on Multi-core Architectures

Peng

Fatourou

Palpanas

2020

IEEE Trans. Knowl. Data Eng.

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Data series similarity search is a core operation for several data series analysis applications across many different domains. Nevertheless, even state-of-the-art techniques cannot provide the time performance required for large data series collections. We propose ParIS and ParIS+, the first disk-based data series indices carefully designed to inherently take advantage of multi-core architectures, in order to accelerate similarity search processing times. Our experiments demonstrate that ParIS+ completely removes the CPU latency during index construction for disk-resident data, and for exact query answering is up to 1 order of magnitude faster than the current state of the art index scan method, and up to 3 orders of magnitude faster than the optimized serial scan method. ParIS+ (which is an evolution of the ADS+ index) owes its efficiency to the effective use of multi-core and multi-socket architectures, in order to distribute and execute in parallel both index construction and query answering, and to the exploitation of the Single Instruction Multiple Data (SIMD) capabilities of modern CPUs, in order to further parallelize the execution of instructions inside each core. 1 introduction [Motivation] An increasing number of applications across many diverse domains continuously produce very large amounts

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Report on the First and Second Interdisciplinary Time Series Analysis Workshop (ITISA)

Cited by 42 publications

References 32 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

Data Series Indexing Gone Parallel

ParIS+: Data Series Indexing on Multi-core Architectures

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