ODSearch: Fast and Resource Efficient On-device Natural Language Search for Fitness Trackers' Data
This addresses the problem of enabling healthcare data search in low-income countries by providing a resource-efficient on-device solution, though it is incremental as it builds on existing compression and Bloom filter techniques.
The paper tackles the challenge of implementing efficient natural language search on mobile and wearable devices in low-resource settings by proposing ODSearch, which uses compression and Bloom filters to achieve near real-time responses without network dependency, outperforming state-of-the-art methods by 53 times in execution time, 26 times in energy usage, and 2.3% in memory utilization.
Mobile and wearable technologies have promised significant changes to the healthcare industry. Although cutting-edge communication and cloud-based technologies have allowed for these upgrades, their implementation and popularization in low-income countries have been challenging. We propose "ODSearch", an On-device Search framework equipped with a natural language interface for mobile and wearable devices. To implement search, "ODSearch" employs compression and Bloom filter, it provides near real-time search query responses without network dependency. In particular, the Bloom filter reduces the temporal scope of the search and compression reduces the size of the data to be searched. Our experiments were conducted on a mobile phone and smartwatch. We compared "ODSearch" with current state-of-the-art search mechanisms, and it outperformed them on average by 53 times in execution time, 26 times in energy usage, and 2.3% in memory utilization.