# PACO: A System-Level Abstraction for On-Loading Contextual Data to Mobile Devices

## TL;DR

PACO introduces a system-level abstraction enabling mobile devices to efficiently store and process dense spatiotemporal context data locally, enhancing privacy, responsiveness, and multi-application reasoning.

## Contribution

The paper presents PACO, a novel architecture and API for on-loading rich spatiotemporal context data on resource-constrained mobile devices, improving privacy and query capabilities.

## Key findings

- Supports expressive on-device queries with dense context data
- Demonstrates energy efficiency and accuracy in real-world tests
- Enables unified contextual reasoning across applications

## Abstract

Spatiotemporal context is crucial in modern mobile applications that utilize increasing amounts of context to better predict events and user behaviors, requiring rich records of users' or devices' spatiotemporal histories. Maintaining these rich histories requires frequent sampling and indexed storage of spatiotemporal data that pushes the limits of resource-constrained mobile devices. Today's apps offload processing and storing contextual information, but this increases response time, often relies on the user's data connection, and runs the very real risk of revealing sensitive information. In this paper we motivate the feasibility of on-loading large amounts of context and introduce PACO (Programming Abstraction for Contextual On-loading), an architecture for on-loading data that optimizes for location and time while allowing flexibility in storing additional context. The PACO API's innovations enable on-loading very dense traces of information, even given devices' resource constraints. Using real-world traces and our implementation for Android, we demonstrate that PACO can support expressive application queries entirely on-device. Our quantitative evaluation assesses PACO's energy consumption, execution time, and spatiotemporal query accuracy. Further, PACO facilitates unified contextual reasoning across multiple applications and also supports user-controlled release of contextual data to other devices or the cloud; we demonstrate these assets through a proof-of-concept case study.