论文标题
ODSearch：快速和资源效率的机上自然语言搜索健身追踪器的数据
ODSearch: Fast and Resource Efficient On-device Natural Language Search for Fitness Trackers' Data
论文作者
论文摘要
移动和可穿戴技术已承诺对医疗保健行业进行重大变化。尽管最先进的沟通和基于云的技术允许进行这些升级，但它们在低收入国家的实施和普及一直具有挑战性。我们提出了“ ODSearch”，这是一个设备搜索框架，配备了用于移动和可穿戴设备的自然语言接口。为了实现搜索，“ ODSearch”采用压缩和开花过滤器，它提供了近乎实时的搜索查询响应，而无需网络依赖。特别是，Bloom过滤器降低了搜索和压缩的时间范围可降低要搜索的数据的大小。我们的实验是在手机和智能手表上进行的。我们将“ ODSearch”与当前的最新搜索机制进行了比较，并且在执行时间平均比它们的表现要优于53次，能源使用26倍，而内存利用率为2.3％。
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.
