ParIS: The Next Destination for Fast Data Series Indexing and Query Answering

Peng, Botao; Fatourou, Panagiota; Palpanas, Themis

doi:10.1109/bigdata.2018.8622293

Cited by 35 publications

(50 citation statements)

References 24 publications

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“…Efficiently supporting similarity search [3,15,21,25] is still challenging for large (out of memory) data series collections [6]. Only very recently attention has been given to solutions that can support variable-length queries [17,18], and there are still a lot to be done in terms of supporting uncertain series [5].…”

Section: Discussion Sessionsmentioning

confidence: 99%

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

Palpanas

Beckmann

2019

SIGMOD Rec.

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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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Section: Discussion Sessionsmentioning

confidence: 99%

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

Palpanas

Beckmann

2019

SIGMOD Rec.

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“…Each time series t is inserted to the index by the worker (i.e., the processor) that is responsible for the partition to which t belongs. If the subtree of the partition does not exist, it will be created (lines [23][24][25]. Then, the time series t is inserted to its corresponding leaf node in the subtree (lines [14][15].…”

Section: Algorithm 5: Dpisax Partitioning Functionmentioning

confidence: 99%

“…Thus, they cannot efficiently support similarity search queries, which is the focus of our study. In order to speed up similarity search, different works have studied the problem of indexing time series datasets, such as Indexable Symbolic Aggregate approXimation (iSAX) [30], [31], iSAX 2.0 [3], [4], iSAX2+ [4], Adaptive Data Series Index (ADS Index) [37], Dynamic Splitting Tree (DSTree) [32], Compact and Contiguous Sequence Infrastructure (Coconut) [14], Parallel Index for Sequences (ParIS) [24], and Ultra Compact Index for Variable-Length Similarity Search (ULISSE) [18]. A recent study is comparing the performance of several different time series indexes [6].…”

Section: Related Workmentioning

confidence: 99%

“…The ParIS index [24] was recently proposed for taking advantage of the modern hardware parallelization opportunities within a single compute node. ParIS describes techniques that use the Single Instruction Multiple Data (SIMD) instructions, as well as the multi-core and multi-socket architectures, for parallel index creation and query answering.…”

Section: Related Workmentioning

confidence: 99%

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Massively Distributed Time Series Indexing and Querying

Yagoubi

Akbarinia

Masséglia

et al. 2020

IEEE Trans. Knowl. Data Eng.

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“…Nevertheless, despite the significance of data series similarity search, and the abundance of relevant methods that have been proposed in the past two decades [3,22,73,63,23,13,42,66,71,81,14,89,58,85,52,51,62], no study has ever attempted to compare these methods under the same conditions. We also point out that we focus on the efficiency of similarity search methods, whereas previous works studied the accuracy of dimensionality reduction techniques and similarity measures, focusing on classification [44,27,9].…”

Section: Introductionmentioning

confidence: 99%

The lernaean hydra of data series similarity search

et al. 2018

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Increasingly large data series collections are becoming commonplace across many different domains and applications.A key operation in the analysis of data series collections is similarity search, which has attracted lots of attention and effort over the past two decades. Even though several relevant approaches have been proposed in the literature, none of the existing studies provides a detailed evaluation against the available alternatives. The lack of comparative results is further exacerbated by the non-standard use of terminology, which has led to confusion and misconceptions. In this paper, we provide definitions for the different flavors of similarity search that have been studied in the past, and present the first systematic experimental evaluation of the efficiency of data series similarity search techniques. Based on the experimental results, we describe the strengths and weaknesses of each approach and give recommendations for the best approach to use under typical use cases. Finally, by identifying the shortcomings of each method, our findings lay the ground for solid further developments in the field. PVLDB Reference Format:the whole (not a sub-) sequence. This problem represents a common use case across many domains [1, 2,38,29]. This work is the most extensive experimental comparison of the efficiency of similarity search methods ever conducted. Contributions. We make the following contributions:1. We present a thorough discussion of the data series similarity search problem, formally defining its different variations that have been studied in the literature under diverse and conflicting names. Thus, establishing a common language that will facilitate further work in this area.2. We include a brief survey of data series similarity search approaches, bringing together studies presented in different communities that have been treated in isolation from each other. These approaches range from smart serial scan methods to the use of indexing, and are based on a variety of classic and specialized data summarization techniques.3. We make sure that all approaches are evaluated under the same conditions, so as to guard against implementation bias. To this effect, we used implementations in C/C++ for all approaches, and reimplemented in C the ones that were only available in other programming languages. Moreover, we conducted a careful inspection of the code bases, and applied to all of them the same set of optimizations (e.g., with respect to memory management, Euclidean distance calculation, etc.), leading to considerably faster performance.4. We conduct the first comprehensive experimental evaluation of the efficiency of data series similarity search approaches, using several synthetic and 4 real datasets from diverse domains. In addition, we report the first large scale experiments with carefully crafted query workloads that include queries of varying difficulty, which can effectively stress-test all the approaches. Our results reveal characteristics that have not been reported in the literature, and lead...

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ParIS: The Next Destination for Fast Data Series Indexing and Query Answering

Cited by 35 publications

References 24 publications

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

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

Massively Distributed Time Series Indexing and Querying

The lernaean hydra of data series similarity search

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