Set-based Similarity Search for Time Series

Peng, Jinglin; Wang, Hongzhi; Li, Jianzhong; Gao, Hong

doi:10.1145/2882903.2882963

Cited by 25 publications

(10 citation statements)

References 37 publications

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“…There is a lot of potential information in EM tasks and the study of time series is becoming more and more important. There are studies of data entity matching with hidden temporal information [22] and time series [23]. Data cleaning is also particularly important in EM.…”

Section: Traditional Methodsmentioning

confidence: 99%

Relation-Aware Entity Matching Using Sentence-BERT

Duhayyim¹,

Alshahrani²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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A key aspect of Knowledge fusion is Entity Matching. The objective of this study was to investigate how to identify heterogeneous expressions of the same real-world entity. In recent years, some representative works have used deep learning methods for entity matching, and these methods have achieved good results. However, the common limitation of these methods is that they assume that different attribute columns of the same entity are independent, and inputting the model in the form of paired entity records will cause repeated calculations. In fact, there are often potential relations between different attribute columns of different entities. These relations can help us improve the effect of entity matching, and can perform feature extraction on a single entity record to avoid repeated calculations. To use attribute relations to assist entity matching, this paper proposes the Relation-aware Entity Matching method, which embeds attribute relations into the original entity description to form sentences, so that entity matching is transformed into a sentence-level similarity determination task, based on Sentence-BERT completes sentence similarity calculation. We have conducted experiments on structured, dirty, and textual data, and compared them with baselines in recent years. Experimental results show that the use of relational embedding is helpful for entity matching on structured and dirty data. Our method has good results on most data sets for entity matching and reduces repeated calculations.

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Section: Traditional Methodsmentioning

confidence: 99%

Relation-Aware Entity Matching Using Sentence-BERT

Duhayyim¹,

Alshahrani²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In 2019, the VLDB paper proposed a variable-length standardized subsequence similarity query algorithm [12]. In 2016, the Harbin Institute Technology team proposed a set-based approximate query algorithm for time series at the SIGMOD conference [13]. The team from the University of Chinese Academy of Sciences and the State Grid Electric Power Research Institute proposed a multidimensional query system DGFIndex for smart grid data [14], and the Beihang team proposed an approximate representation and query algorithm for trajectory time series [15].…”

Section: Related Researchmentioning

confidence: 99%

Design of Management Platform Architecture and Key Algorithm for Massive Monitoring Big Data

Liu

Pan

et al. 2021

Wireless Communications and Mobile Computing

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With the construction and development of industrial informatization, industrial big data has become a trend within the smart industry. To obtain valuable information on massive data, achieving the acquisition, storage, analysis, and mining is becoming an important area of research. Focusing on the application requirements for industrial fields, we propose a data acquisition and analysis system based on the NB-IoT for industrial applications. The system is an integrated system that includes sensor data acquisition, data transmission, data storage, and analysis mining. In this study, we mainly focused on the use of the NB-IoT network to collect and transmit real-time data for sensors. First, for the long time series (e.g., if we collect the data streams for one year for the sensor with a frequency of 1 Hz, the length of the series will reach 107). Then, we propose DSCS-LTS, a distributed storage and calculation model, and CCCA-LTS, an algorithm for the correlation coefficient of long time series in a distributed environment. Third, we propose a granularity selection algorithm and query process logic for visualization. We tested the platform in our laboratory and an automated production line for one year, and the experimental results using real data sets show that our approach is effective and scalable, can achieve efficient data management, and provide the basis for intelligent enterprise decision-making.

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“…In addition, it is unsensitive to the abnormal data points and can deal with the similarity between two time series with different lengths. Due to the merits of DTW [35], [38], [39], it is often applied to the field of time series data mining such as gesture recognition, speech processing, image recognition and medicine expert systems.…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

Similarity Measure Based on Incremental Warping Window for Time Series Data Mining

Wang

2019

IEEE Access

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A similarity measure is one of the most important tasks in the fields of time series data mining. Its quality often affects the efficiency and effectiveness of the related algorithms that need to measure the similarity between two time series in advance. Dynamic time warping is one of the most robust methods to compare one time series with another based on warping alignments. In this paper, the design of an incremental warping window is used to improve the performance of dynamic time warping. The incremental warping window is changeable for various time series with different lengths. Moreover, the improved dynamic time warping based on the novel window considers the recent alignments as much as possible, which indicates that the proposed method concentrates on more information of the recent data points than that of the previous data points. In addition, it is suitable for online similarity measure between data stream. The experimental evaluation shows that the proposed method is effective and efficient for time series mining. INDEX TERMS Dynamic time warping, similarity measure, time series data mining, incremental warping window, classification.

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Set-based Similarity Search for Time Series

Cited by 25 publications

References 37 publications

Relation-Aware Entity Matching Using Sentence-BERT

Relation-Aware Entity Matching Using Sentence-BERT

Design of Management Platform Architecture and Key Algorithm for Massive Monitoring Big Data

Similarity Measure Based on Incremental Warping Window for Time Series Data Mining

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