Aggregate Analytic Window Query over Spatial Data

TL;DR

This paper introduces aggregate analytic window queries for spatial data, extending traditional relational queries to support spatial aggregations, and proposes efficient algorithms with complexity analysis.

Contribution

It defines spatial aggregate analytic window queries and develops algorithms with proven efficiency for grid and tree-index structures.

Findings

Algorithms are efficient and practical.

Complexity analysis confirms their effectiveness.

Supports spatial data aggregation in new ways.

Abstract

Analytic window query is a commonly used query in the relational databases. It answers the aggregations of data over a sliding window. For example, to get the average prices of a stock for each day. However, it is not supported in the spatial databases. Because the spatial data are not in a one-dimension space, there is no straightforward way to extend the original analytic window query to spatial databases. But these queries are useful and meaningful. For example, to find the average number of visits for all the POIs in the circle with a fixed radius for each POI as the centre. In this paper, we define the aggregate analytic window query over spatial data and propose algorithms for grid index and tree-index. We also analyze the complexity of the algorithms to prove they are efficient and practical.