Aggregate Estimations over Location Based Services

TL;DR

This paper introduces algorithms for estimating SUM and COUNT aggregates over location-based services' databases using only their public query interfaces, leveraging Voronoi cell computations, with extensive real-world testing.

Contribution

It presents novel algorithms for aggregate estimation over LBS databases via restrictive interfaces, including techniques for Voronoi cell approximation, and validates them through real-world experiments.

Findings

Algorithms accurately estimate aggregates with limited queries.

Effective Voronoi cell approximation improves estimation precision.

Validated on Google Maps, WeChat, and Sina Weibo.

Abstract

Location based services (LBS) have become very popular in recent years. They range from map services (e.g., Google Maps) that store geographic locations of points of interests, to online social networks (e.g., WeChat, Sina Weibo, FourSquare) that leverage user geographic locations to enable various recommendation functions. The public query interfaces of these services may be abstractly modeled as a kNN interface over a database of two dimensional points on a plane: given an arbitrary query point, the system returns the k points in the database that are nearest to the query point. In this paper we consider the problem of obtaining approximate estimates of SUM and COUNT aggregates by only querying such databases via their restrictive public interfaces. We distinguish between interfaces that return location information of the returned tuples (e.g., Google Maps), and interfaces that do not return location information (e.g., Sina Weibo). For both types of interfaces, we develop aggregate estimation algorithms that are based on novel techniques for precisely computing or approximately estimating the Voronoi cell of tuples. We discuss a comprehensive set of real-world experiments for testing our algorithms, including experiments on Google Maps, WeChat, and Sina Weibo.