MISCELA: Discovering Correlated Attribute Patterns in Time Series Sensor Data

The urban condition is monitored by a wide variety of sensors with several attributes such as temperature and traffic volume. It is expected to discover the correlated attributes to accurately analyze and understand the urban condition. Several mining techniques for spatio-temporal data have been proposed for discovering the sets of sensors that are spatially close to each other and temporally correlated in their measurements. However, they cannot discover correlated attributes efficiently because their targets are correlated sensors with a single attribute. In this paper, we introduce a problem of discovering correlations among multiple attributes, which we call correlated attribute pattern (CAP) mining. Although the existing spatio-temporal data mining methods can be extended to discover CAPs, they are inefficient because they extract unnecessary correlated sensors that do not have CAPs. Therefore, we propose a CAP mining method MISCELA to efficiently discover CAPs. In MISCELA, we develop a new tree structure called CAP search tree, by which we can effectively prune the unnecessary patterns for the CAP mining. Our experiments using real sensor datasets show that the response time of MISCELA is up to 79% faster compared to the state-of-the-art.