ODSearch: Fast and Resource Efficient On-device Natural Language Search for Fitness Trackers' Data

TL;DR

ODSearch is an on-device natural language search framework for fitness tracker data that uses compression and Bloom filters to enable fast, resource-efficient searches on mobile and wearable devices without network reliance.

Contribution

It introduces a novel on-device search framework combining compression and Bloom filters for efficient natural language search on mobile and wearable devices.

Findings

Outperforms existing methods by 53x in execution time

Uses 26x less energy than current solutions

Reduces memory utilization by 2.3%

Abstract

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.