SpatioTemporal Traveler

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 [2, 3, 12]. The increasing concerns about contextual data privacy, and specifically location privacy [9] motivate onloading [8], or moving storage and processing of data onto the device, to prevent revealing potentially sensitive user information. This demo showcases the PACO (Programming Abstraction for Contextual Onloading) middleware, which is designed to support onloading large amounts of contextual data to the mobile devices that generate data; the onloading is motivated both by a need to preserve user privacy and by a desire to reduce a constant data connection to continuously store spatiotemporal data at some third-party central service. The PACO middleware maintains a database on-device and exposes an application-facing API that provides flexible query operations that can be performed over a user’s historical spatiotemporal data. Through access profiles, users can control the lossiness of the queries that are used by other applications and for possible cloud offload. The PACO system model is depicted in Figure 1. In PACO a data point is stored as timestamped location data and represents some ”observation” (captured as a linked piece of context data) of a given space at a given time. PACO models a data point as having a region of influence which can best be visualized as a heat map with intensity decaying as spatial and temporal distance increases from the point of observation. To realize this view of spatiotemporal data, PACO leverages previous work in spatiotemporal data storage [6, 11]; specifically, PACO uses both a 3-dimensional R-Tree [7] and a k-d Tree [1] to efficiently index its data points. In this demo, the PACO data points represent a tourist’s observations of predefined points of interest. PACO supports queries across ranges of space, time, or the combination of the two. The basic PACO query computes the aggregate influence of all points, called the probability of knowledge (PoK), for the spatiotemporal region in