A filter-based protocol for continuous queries over imprecise location data

Abstract

In typical location-based services (LBS), moving objects (e.g., GPS-enabled mobile phones) report their locations through a wireless network. An LBS server can use the location information to answer various types of continuous queries. Due to hardware limitations, location data reported by the moving objects are often uncertain. In this paper, we study efficient methods for the execution of Continuous Possible Nearest Neighbor Query (CPoNNQ) that accesses imprecise location data. A CPoNNQ is a standing query (which is active during a period of time) such that, at any time point, all moving objects that have non-zero probabilities of being the nearest neighbor of a given query point are reported. To handle the continuous nature of a CPoNNQ, a simple solution is to require moving objects to continuously report their locations to the LBS server, which evaluates the query at every time step. To save communication bandwidth and mobile devices' batteries, we develop two filter-based protocols for CPoNNQ evaluation. Our protocols install "filter bounds" on moving objects, which suppress unnecessary location reporting and communication between the server and the moving objects. Through extensive experiments, we show that our protocols can effectively reduce communication costs while maintaining a high query quality.

Moving range k nearest neighbor queries with quality guarantee over uncertain moving objects

“…The management of uncertain data has been studied extensively in the context of moving object databases [2,5,6,11,15,20,21,30,31]. Because location uncertainty is inherent in such databases, the development of efficient and effective solutions providing probabilistic query results is in high demand.…”

“…However, this simple method is unacceptable because the wireless bandwidth available is typically limited in a mobile environment. In our study, we make two assumptions regarding the location of an object, which have been widely used in many previous studies [4,11,15,24]: (1) The uncertainty region of object o x , denoted by U x , is represented by a circle with center c x and radius r x , within which o x is located, and (2) the location of o x is considered to be uniformly distributed within U x . However, as we will formally demonstrate in Section 4.2, our results can be applied to non-uniform distributions (e.g., normal and Zipf distributions) of object locations in the uncertainty region.…”

Distributed execution of continuous queries

“…In this tutorial we present various method for querying uncertain data (Bullets C, H, and K) . Authors of [13] describe the optimal way of pulling data based on their PDFs, [14] explains the range queries with probability threshold, whereas [15] explains a method of answering nearest neighbor queries over uncertain data.…”

Scalable Evaluation of Trajectory Queries over Imprecise Location Data

“…Other types of nearest neighbor queries, like group nearest neighbor query [29], continuous nearest neighbor query [30], expected nearest neighbor query [31] have also been proposed. In these works, the query input is limited to precise points.…”