MISCELA: discovering simultaneous and time-delayed correlated attribute patterns
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MISCELA: discovering simultaneous and time‑delayed correlated attribute patterns Kei Harada1 · Yuya Sasaki1 · Makoto Onizuka1 Accepted: 16 September 2020 © The Author(s) 2020
Abstract This article addresses a new pattern mining problem in time series sensor data, which we call correlated attribute pattern mining. The correlated attribute patterns (CAPs for short) are the sets of attributes (e.g., temperature and traffic volume) on sensors that are spatially close to each other and temporally correlated in their measurements. Although the CAPs are useful to accurately analyze and understand spatio-temporal correlation between attributes, the existing mining methods are inefficient to discover CAPs because they extract unnecessary patterns. Therefore, we propose a mining method Miscela to efficiently discover CAPs. Miscela can discover not only simultaneous correlated patterns but also time delayed correlated patterns. Furthermore, we extend Miscela to automatically search for correlated patterns with any time delays. Through our experiments using three real sensor datasets, we show that the response time of Miscela is up to 20.84 times faster compared with the state-of-the-art method. We show that Miscela discovers meaningful patterns for urban managements and environmental studies. Keywords Spatio-temporal data mining · Smart city · Co-evolving patterns · Correlated attribute patterns
1 Introduction Many cities have installed a wide variety of sensors to continuously and cooperatively monitor urban conditions, such as the distribution of air pollution, the transition of traffic volume, and the change of temperature. Municipalities analyze the * Kei Harada [email protected]‑u.ac.jp Yuya Sasaki [email protected]‑u.ac.jp Makoto Onizuka [email protected]‑u.ac.jp 1
Graduate School of Information Science and Technology, Osaka University, Suita 565‑0871, Japan
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Distributed and Parallel Databases
Fig. 1 Examples of simultaneous and time-delayed correlations in China
urban conditions and make a decision for the urban planning by using such sensor data. For example, Santander, Spain monitors the traffic volume within the city and informs people of the real-time traffic information [12, 13]. The accumulated traffic data are used to several urban managements such as the traffic prediction, the road extension, and the traffic signal control. In these services, it is useful to discover sets of roads which are spatially close and whose traffic volume increases or decreases during the same periods (i.e., co-evolve). The problem is called the spatial co-evolving pattern mining (for short, SCP mining), which discovers sensors that are spatially close to each other and temporally co-evolving in their measurements. Since the SCP mining is useful for many applications such as the air pollution analysis in an urban area, several SCP mining methods have been proposed [2, 17]. The SCP mining discovers meaningful patterns for analyzing urban environments. 1.1 Motivation Many cities typically monitor m
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