期刊
出版社
IEEE COMPUTER SOC
DOI: 10.1109/VAST50239.2020.00006
关键词
Temporal data; data analysis; reasoning; problem solving; and decision making; machine learning techniques
类别
资金
- National Natural Science Foundation of China [61772456, 61761136020, 61972122, 61872389]
- Open Project Program of State Key Lab of CADCG [A1903]
- FFG [854184]
- Austrian COMET Program Competence Centers for Excellent Technologies under Austrian Federal Ministry of Transport, Innovation and Technology
- Austrian Federal Ministry for Digital and Economic Affairs
- Province of Upper Austria
- Province of Styria
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.
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