Human activity recognition, which involves recognizing human activities from sensor data, has drawn a lot of interest from researchers and practitioners as a result of the advent of smart homes, smart cities, and smart systems. Existing studies on activity recognition mostly concentrate on coarse-grained activities like walking and jumping, while fine-grained activities like eating and drinking are understudied because it is more difficult to recognize fine-grained activities than coarse-grained ones. As such, food intake activity recognition in particular is under investigation in the literature despite its importance for human health and well-being, including telehealth and diet management. In order to determine sensors' practical recognition accuracy, preferably with the least amount of privacy intrusion, a dataset of food intake activities utilizing sensors with varying degrees of privacy sensitivity is required. In this study, we collected such a dataset by collecting fine-grained food intake activities using sensors of heterogeneous privacy sensitivity levels, namely a mmWave radar, an RGB camera, and a depth camera. Solutions to recognize food intake activities can be developed using this dataset, which may provide a more comprehensive picture of the accuracy and privacy trade-offs involved with heterogeneous sensors.

Data mining techniques have been widely used in various applications. However, the misuse of these techniques may lead to the disclosure of sensitive information. Researchers have recently made efforts at hiding sensitive association rules. Nevertheless, undesired side effects, e.g., nonsensitive rules falsely hidden and spurious rules falsely generated, may be produced in the rule hiding process. In this paper, we present a novel approach that strategically modifies a few transactions in the transaction database to decrease the supports or confidences of sensitive rules without producing the side effects. Since the correlation among rules can make it impossible to achieve this goal, in this paper, we propose heuristic methods for increasing the number of hidden sensitive rules and reducing the number of modified entries. The experimental results show the effectiveness of our approach, i.e., undesired side effects are avoided in the rule hiding process. The results also report that in most cases, all the sensitive rules are hidden without spurious rules falsely generated. Moreover, the good scalability of our approach in terms of database size and the influence of the correlation among rules on rule hiding are observed

Since tree-structured data such as XML files are widely used for data representation and exchange on the Internet, discovering frequent tree patterns over tree-structured data streams becomes an interesting issue. In this paper, we propose an online algorithm to continuously discover the current set of frequent tree patterns from the data stream. A novel and efficient technique is introduced to incrementally generate all candidate tree patterns without duplicates. Moreover, a framework for counting the approximate frequencies of the candidate tree patterns is presented. Combining these techniques, the proposed approach is able to compute frequent tree patterns with guarantees of completeness and accuracy.

Mining frequent itemsets has been widely studied over the last decade. Past research focuses on mining frequent itemsets from static databases. In many of the new applications, data flow through the Internet or sensor networks. It is challenging to extend the mining techniques to such a dynamic environment. The main challenges include a quick response to the continuous request, a compact summary of the data stream, and a mechanism that adapts to the limited resources. In this paper, we develop a novel approach for mining frequent itemsets from data streams based on a time-sensitive sliding window model. Our approach consists of a storage structure that captures all possible frequent itemsets and a table providing approximate counts of the expired data items, whose size can be adjusted by the available storage space. Experiment results show that in our approach both the execution time and the storage space remain small under various parameter settings. In addition, our approach guarantees no false alarm or no false dismissal to the results yielded.

Mining sequential patterns in large databases is an important research topic. The main challenge of mining sequential patterns is the high processing cost due to the large amount of data. We propose a new strategy called direct sequence comparison (abbreviated as DISC), which can find frequent sequences without having to compute the support counts of nonfrequent sequences. The main difference between the DISC strategy and the previous works is the way to prune nonfrequent sequences. The previous works are based on the antimonotone property, which prune the nonfrequent sequences according to the frequent sequences with shorter lengths. On the contrary, the DISC strategy prunes the nonfrequent sequences according to the other sequences with the same length. Moreover, we summarize three strategies used in the previous works and design an efficient algorithm called DISC-all to take advantages of all the four strategies. The experimental results show that the DISC-all algorithm outperforms the PrefixSpan algorithm on mining frequent sequences in large databases. In addition, we analyze these strategies to design the dynamic version of our algorithm, which achieves a much better performance.

Searching information from the WWW efficiently and effectively has become a very important issue. We apply the information filtering concept to finding good matches of Web pages to what the user needs. Furthermore, we propose four new methods for indexing user information needs and show their efficiency in comparison with two well-known approaches.