ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data

Xumeng Wang; Wei Chen; Jiazhi Xia; Zexian Chen; Dongshi Xu; Xiangyang Wu; Mingliang Xu; Tobias Schreck

doi:10.1109/VAST50239.2020.00006

ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data

Xumeng Wang, Wei Chen, Jiazhi Xia, Zexian Chen, Dongshi Xu, Xiangyang Wu, Mingliang Xu, Tobias Schreck

Institute of Computer Graphics and Knowledge Visualisation (7110)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.

Original language	English
Title of host publication	Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020
Pages	1-11
Number of pages	11
ISBN (Electronic)	9781728180090
DOIs	https://doi.org/10.1109/VAST50239.2020.00006
Publication status	Published - Oct 2020
Event	IEEE VIS 2020 - Virtuell, United States Duration: 25 Oct 2020 → 30 Oct 2020 http://ieeevis.org/year/2020/welcome

Publication series

Name	Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020

Conference

Conference	IEEE VIS 2020
Abbreviated title	VIS 2020
Country/Territory	United States
City	Virtuell
Period	25/10/20 → 30/10/20
Internet address	http://ieeevis.org/year/2020/welcome

Keywords

and decision making; machine learning techniques
data analysis
data analysis, reasoning, problem solving, and decision making
machine learning techniques
problem solving
reasoning
Temporal data

ASJC Scopus subject areas

Media Technology
Modelling and Simulation

Fields of Expertise

Information, Communication & Computing

Access to Document

10.1109/VAST50239.2020.00006

https://arxiv.org/abs/2007.15272

Cite this

Wang, X., Chen, W., Xia, J., Chen, Z., Xu, D., Wu, X., Xu, M., & Schreck, T. (2020). ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data. In Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020 (pp. 1-11). Article 9308627 (Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020). https://doi.org/10.1109/VAST50239.2020.00006

ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data. / Wang, Xumeng; Chen, Wei; Xia, Jiazhi et al.
Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020. 2020. p. 1-11 9308627 (Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Wang, X, Chen, W, Xia, J, Chen, Z, Xu, D, Wu, X, Xu, M & Schreck, T 2020, ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data. in Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020., 9308627, Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020, pp. 1-11, IEEE VIS 2020, Virtuell, United States, 25/10/20. https://doi.org/10.1109/VAST50239.2020.00006

Wang X, Chen W, Xia J, Chen Z, Xu D, Wu X et al. ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data. In Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020. 2020. p. 1-11. 9308627. (Proceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020). doi: 10.1109/VAST50239.2020.00006

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title = "ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data",

abstract = "Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.",

keywords = "and decision making; machine learning techniques, data analysis, data analysis, reasoning, problem solving, and decision making, machine learning techniques, problem solving, reasoning, Temporal data",

author = "Xumeng Wang and Wei Chen and Jiazhi Xia and Zexian Chen and Dongshi Xu and Xiangyang Wu and Mingliang Xu and Tobias Schreck",

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AU - Wu, Xiangyang

AU - Xu, Mingliang

AU - Schreck, Tobias

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N2 - Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.

AB - Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.

KW - and decision making; machine learning techniques

KW - data analysis

KW - data analysis, reasoning, problem solving, and decision making

KW - machine learning techniques

KW - problem solving

KW - reasoning

KW - Temporal data

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ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Fields of Expertise

Access to Document

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