Benchmarking Spatial Data Warehouses

Abstract

Abstract. Spatial data warehouses (SDW) enable analytical multidimensional queries together with spatial analysis. Mainly, three operations are related to SDW query processing performance: (i) joining large fact tables and large spatial and non-spatial dimension tables; (ii) computing one or more costly spatial predicates based on spatial ad hoc query windows; and (iii) aggregating data according to different spatial granularity levels. Several techniques to improve the query processing performance over SDW have been proposed in the literature. However, we identified the lack of a benchmark to carry out a controlled experimental evaluation of such techniques and, principally, to effectively measure the costs of the aforementioned three complex operations. In this paper, we propose a novel spatial data warehouse benchmark, called Spadawan, to provide performance evaluation environments for SDW and enable a further investigation on spatial data redundancy. The Spadawan benchmark is available at http://gbd.dc.ufscar.br/spadawan.

Spatial data warehouse: an analysis in tourism sector of west java province

“…Operational systems (illustrated in Figure 4) designed for specific purposes are unsuitable for offering stakeholders or management staff an impactful consolidation and multi-dimensional perspective of the data, as high-level executives require the capability to slice and dice the same data rather than drill down to examine the data detail. Comprehensive information analysis and selection tools Data warehousing is a repository for vast quantities of historical data [7]. It is also a group of decision support tools designed to assist (executives, managers, and analysts) in making quicker and more accurate decisions.…”

“…Geographic Information Systems (GIS) have been used in urban administration and planning for decades, but only recently, because of developments in graphics, distributed processing, and network connections, has software progressed to the point where it can be used consistently and efficiently, sometimes called smart cities or smart urban which include tourism sector [1] [2]. Geographic or Geospatial Information Systems are crucial to several methods of collecting data in the tourism industry [3][4] [5] and has had a significant impact on the tourism industry, and as a result, there has been renewed interest in the use of spatial data warehouse in the tourism sector to manage large, diverse geographical databases for urban applications [6] [7]. Many people in the tourism industry use dan will collect data, including those who use social media and the government use the data to make an impact to improve the tourist attraction or to make a benefit as an income or invest for the government [8].…”

An Integrated Framework for Predicting Long-Term Productivity of Pastures in the Kingdom of Saudi Arabia

“…Mainly, three operations related to SDW query processing are considered [40]: (i) joining large fact tables with large spatial/non-spatial dimension tables; (ii) computing one or more costly spatial predicates based on spatial ad hoc query windows; and (iii) aggregating data according to different levels of spatial granularity. For example, "Find the total area suitable for cultivation of a certain pasture grass variety inside a 'rectangular' window."…”