Geographical Asynchronous Information Access for Distributed Systems

TL;DR

GAIA is a new model for efficient retrieval of geo-localized data in cloud systems, outperforming existing models with a logarithmic efficiency due to its innovative combination of projection and cell decomposition.

Contribution

The paper introduces GAIA, a novel model that improves data access efficiency in cloud-based GIS applications through a unique combination of projection and cell decomposition techniques.

Findings

GAIA achieves O(log(n)) efficiency, outperforming linear models.

Experimental results confirm GAIA's superior performance in cloud environments.

Benchmark comparisons demonstrate GAIA's effectiveness over existing models.

Abstract

Non-relational databases are the common means of data storage in the Cloud, and optimizing the data access is of paramount importance into determining the overall Cloud system performance. In this paper, we present GAIA, a novel model for retrieving and managing correlated geo-localized data in the cloud environment. We survey and compare the existing models used mostly in Geographical Information Systems (GIS), mainly the Grid model and the Coordinates Projection model. Besides, we present a benchmark comparing the efficiency of the models. Using extensive experimentation, we show that GAIA outperforms the existing models by its high efficiency which is of O(log(n)), and this mainly thanks to its combination of projection with cell decomposition. The other models have a linear efficiency of O(n). The presented model is designed from the ground up to support GIS and is designed to suit both cloud and parallel computing.