Title: A cloud-based spatiotemporal data warehouse approach

Authors: Georgia Garani; Nunziato Cassavia; Ilias K. Savvas

Addresses: University of Thessaly, Gaiopolis, Larissa, 41500, Greece ' University of Calabria, Via Pietro Bucci, Arcavacata di Rende, 87036, Italy ' University of Thessaly, Gaiopolis, Larissa, 41500, Greece

Abstract: The arrival of the big data era introduces new necessities for accommodating data access and analysis by organisations. The evolution of data is three-fold, increase in volume, variety, and complexity. The majority of data nowadays is generated in the cloud. Cloud data warehouses profit from the benefits of the cloud by facilitating the integration of data in the cloud. A data warehouse is developed in this paper, which supports both spatial and temporal dimensions. The research focuses on proposing a general design for spatiobitemporal objects implemented by nested dimension tables using the starnest schema approach. Experimental results reflect that the parallel processing of such data on the cloud can process OLAP queries efficiently. Furthermore, by increasing the number of computational nodes results in a significant reduction of queries' time execution. The feasibility, scalability, and utility of the proposed technique for querying spatiotemporal data are demonstrated.

Keywords: cloud computing; big data; hive; business intelligence; data warehouses; cloud based data warehouses; spatiotemporal data; spatiotemporal objects; starnest schema; OLAP; online analytical processing.

DOI: 10.1504/IJGUC.2025.146278

International Journal of Grid and Utility Computing, 2025 Vol.16 No.3, pp.202 - 210

Received: 05 Jun 2020
Accepted: 12 Sep 2020
Published online: 15 May 2025 *

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