Multiaspect Mining of Complex Sensor Sequences In recent years, a massive amount of timestamped sensor data has been generated and collected by many Internet of Things (IoT) applications, such as advanced automobiles and health care devices. Given such a large collection of complex sensor sequences, which consists of multiple attributes (e.g., sensor, user, timestamp), how can we automatically find important dynamic timeseries patterns and the points of variation? How can we summarize all the complex sensor sequences, and achieve a meaningful segmentation? Also, can we see any hidden userspecific differences and outliers? In this paper we present CUBEMARKER, an efficient and effective method for capturing such multiaspect features in sensor sequences. CUBEMARKER performs multiway summarization for all attributes, namely, sensors, users, and time, and specifically it extracts multiaspect features, such as important timeseries patterns (i.e., timeaspect features) and hidden groups of users (i.e., useraspect features), in complex sensor sequences. Our proposed method has the following advantages: (a) It is effective: it extracts multiaspect features from complex sensor sequences and enables the efficient and effective analysis of complicated datasets; (b) It is automatic: it requires no prior training and no parameter tuning; (c) It is scalable: our method is carefully designed to be linear as regards dataset size and applicable to a large number of sensor sequences. Extensive experiments on real datasets show that CUBEMARKER is effective in that it can capture meaningful patterns for various realworld datasets, such as those obtained from smart factories, human activities, and automobiles. CUBEMARKER consistently outperforms the best stateoftheart methods in terms of both accuracy and execution speed.

A Multiresident Activity Recognition Approach based on Frequent Itemset Mining Features Activity recognition is one of the most important supporting technologies for smarthome applications, and most existing works conducted on this topic assume that there is only one resident in the smart home. However, there are often more than one resident at home in reality, which makes it ever more challenging to recognize their activities. In this work, a knowledgedriven approach for multiresident activity recognition based on the features constructed by frequent itemset is proposed. Unlike traditional singlelabel based methods, we address this problem as learning multiple activity labels from the sensordata sequence. Based on the binary ambient sensor data, our approach utilizes both features generated from single sensor and frequent itemset features of each class, i.e. activity. After that, five multilabel approaches are applied to recognize different activities in a short period of time. Finally, we conduct several experiments on a multiresident activity dataset, and the experimental result demonstrates that the proposed approach with frequent itemset features is better than those without frequent itemset features added.