Fast database engines have become an essential building block in many systems and applications. Yet most of them are designed based on on-premise solutions and do not directly work in the cloud. Existing cloud-native database systems are mostly disk resident databases that follow a storage-centric design and exploit the potential of modern cloud infrastructure, such as manycore processors, large main memory and persistent memory. However, in-memory databases are infrequent and untapped.

This paper presents VeinDeep, a system for using vein patterns to secure smartphones from opportunistic access, e.g. a device left unattended. VeinDeep takes advantage of infrared depth sensors, which at the time of writing have recently started to appear in smartphones for 3D indoor mapping and localisation. We find these sensors can be re-purposed to capture images of the unique vein patterns on the back of each person's hand. We simulate a depth sensor equipped smartphone by developing VeinDeep on a low power Compute Stick. We use Kinect V2 depth sensor to collect 240 recordings from 40 hands belonging to 20 test subjects. Then use this data to compare VeinDeep to one older but popular vein pattern recognition algorithm which uses Hausdorff distance and one recently developed algorithm which uses Kernel distance. We achieve a precision of 0.98, compared to 0.9 for Kernel distance and 0.5 for Hausdorff distance when recall is approximately at 0.83. In addition VeinDeep does all this while taking an average of 6 MiB of memory and 466 milliseconds per comparison. This is an average of 1/6 run time, 2/3 the memory of Hausdorff distance and 1/3 the run time, 1/2 the memory of Kernel distance.

Personnel working in health services and food preparation industries maintain hand hygiene by washing their hands. Often this follows the WHO hand hygiene guidelines. Systems exist to monitor and detect compliance with all stages of the guidelines. However, the critical step of verifying whether the subject has correctly lathered soap on the hands can only be monitored using wrist worn sensors. This comes with several disadvantages: the wearable sensors may become contaminated, require battery, the user forgets to wear or misplaces the device. We address these problems by proposing WashInDepth, a system which uses a fixed contactless depth sensor mounted above the wash basin and is activated using a wireless trigger. As the system is fixed in position and contactless, there is reduced chance of contamination, the option to not require a battery and no possibility of forgetting or misplacement of the device. This system provides the potential ability to warn a person, after they have washed their hand, if improper application of soap was detected based on their hand gestures. We evaluate the gesture detection accuracy with 15 subjects and achieve 94% gesture detection accuracy. We deploy the system on a low power Compute Stick and demonstrate that it can keep track of hand gestures in real-time when video is recorded at 20 FPS.