论文标题
ConceptExplorer：概念漂移的视觉分析多源时间序列数据
ConceptExplorer: Visual Analysis of Concept Driftsin Multi-source Time-series Data
论文作者
论文摘要
时间序列数据在各种情况下进行了广泛研究，例如天气预报，股票市场，客户行为分析。要全面了解动态环境，有必要理解多个数据源的功能。本文提出了一种新型的视觉分析方法，用于检测和分析从多源时间序列中的概念漂移。我们提出了一种视觉检测方案，以根据预测模型发现从多个来源的时间序列中发现概念。我们设计了一个漂移级别索引来描述动力学，以及一个一致性判断模型，以证明概念从各种来源的漂移是否一致。我们集成的视觉界面，概念分解器，促进了视觉探索，提取，理解，理解和比较概念和概念从多源时间序列数据中漂移。我们进行了三项案例研究和专家访谈，以验证我们方法的有效性。
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.
