Wearable devices are becoming more and more prevalent in our daily lives as people become more curious about how well they are doing in monitoring, improving, or maintaining their health and fitness. Fitness trackers and smartwatches have become almost ubiquitous, so these devices have begun to play a critical role in forensic investigations. In this paper, the authors conducted a forensic analysis of the controlling applications for three popular fitness bands and smartwatches (i.e., Amazon Halo, Garmin Connect, and Mobvoi) on an Android smartphone device to (1) provide forensic investigators with a road-map of forensically relevant data that are stored within these applications and (2) highlight any privacy concerns that the stored data within these applications may present to the applications’ users. Our findings indicate that the three fitness applications store a wealth of user data. In particular, the Amazon Halo app stores daily, weekly, and monthly activity-related data for at least the last 13 days. The user’s Tone Analysis results were also recovered. The Garmin Connect application also records detailed user activity information, as it was possible to recover the last 15 days worth of user activity data. The Garmin Connect user’s general location was also determined via the application’s weather notification feature. Lastly, the Mobvoi application records all data points from the time the device is first used until the last time the device is used. These data points may include heart rates taken every 5 min and step counts. Our findings highlight the possibility of collecting personally identifiable information about users of these devices and apps, including their profile information, habits, location, and state of mind. These findings would be pertinent to forensic investigators in the event that these or similar applications are part of an investigation.

We present GridSet, a novel set visualization for exploring elements, their attributes, intersections, as well as entire sets. In this set visualization, each set representation is composed of glyphs, which represent individual elements and their attributes utilizing different visual encodings. In each set, elements are organized within a grid treemap layout that can provide space-efficient overviews of the elements structured by set intersections across multiple sets. These intersecting elements can be connected among sets through visual links. These visual representations for the individual set, elements, and intersection in GridSet facilitate novel interaction approaches for undertaking analysis tasks by utilizing both macroscopic views of sets, as well as microscopic views of elements and attribute details. In order to perform multiple set operations, GridSet supports a simple and straightforward process for set operations through dragging and dropping set objects. Our use cases involving two large set-typed datasets demonstrate that GridSet facilitates the exploration and identification of meaningful patterns and distributions of elements with respect to attributes and set intersections for solving complex analysis problems in set-typed data.

In this article, the authors study bitmap indexing for temporal querying of faces that appear in videos. Since the bitmap index is originally designed to select a set of records that satisfy a value in the domain of the attribute, there is no clear strategy for how to apply it for temporal querying. Accordingly, the authors introduce a multi-level bitmap index that the authors call “FaceTimeMap” for temporal querying of faces in videos. The first level of the FaceTimeMap index is used for determining whether a person appears in a video or not, whereas the second level of the index is used for determining intervals when a person appears. First, the authors analyze the co-appearance query where two or more people appear simultaneously in a video, and then examine next-appearance query where a person appears right after another person. In addition, to consider the gap between the appearance of people, the authors study eventual- and prior-appearance queries. Queries are satisfied by applying bitwise operations on the FaceTimeMap index. The authors provide some performance studies associated with this index.