Spatial OLAP and Map Generalization

Boussaïd, Omar; Bertolotto, Michela; Bimonte, Sandro; Gensel, Jérôme

doi:10.4018/jdwm.2012010102

Cited by 17 publications

(5 citation statements)

References 26 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the aim to increase DB computational performance and to allow a more interactive and intuitive exploration of data, the integration of the new SOLAP (Spatial On-Line Analytical Processing) technologies within AGUA would be very effective, since they, as shown in different previous studies (e.g. Bimonte, Bertolotto, Gensel, & Boussaid, 2012;Boulil, Ber, Bimonte, Grac, & Cernesson, 2014), supports rapid and straightforward spatio-temporal exploration and analysis of data by means of a multidimensional approach and different aggregation strategies available in cartographic, tabular and diagram displays (Rivest, Bédard, & Marchand, 2001).…”

Section: Discussionmentioning

confidence: 99%

Potential Nitrogen Load from Crop-Livestock Systems

Vizzari

Antognelli

Pauselli

et al. 2016

International Journal of Agricultural and Environmental Information Systems

View full text Add to dashboard Cite

The EU “Water” Directive establishes a common European framework for the environmental protection of inland, coastal and marine waters. One of the major environmental concerns about water quality is certainly the N loads from agro-livestock systems. In this study, carried out in Umbria region, Italy, a novel spatial database for a multi-scale and multi-level analysis was designed and implemented integrating different agricultural and livestock farming datasets related to agro-livestock system. This database allows the calculation of different descriptive indicators about agricultural and livestock farming systems at different scales of investigation (NVZ, sub-basins, bodies of ground water, cadastral sheets, municipalities, provinces, entire region). Moreover, three relevant spatial indicators (potential nitrogen crop supply, potential nitrogen availability from livestock manure, and total potential nitrogen loads) were calculated applying an assessment model developed in the study. All this information appears very significant to support decision making at the various administrative levels and to pursue the environmental objectives established by EU and national regulations.

show abstract

Section: Discussionmentioning

confidence: 99%

Potential Nitrogen Load from Crop-Livestock Systems

Vizzari

Antognelli

Pauselli

et al. 2016

International Journal of Agricultural and Environmental Information Systems

View full text Add to dashboard Cite

show abstract

“…The notion of spatial OLAP (SOLAP) was introduced in [14], and it is reviewed in [15]. Other relevant work on SOLAP can be found in [16][17][18].…”

Section: Related Workmentioning

confidence: 99%

Mobility Data Warehouses

Vaisman

Zimányi

2019

IJGI

View full text Add to dashboard Cite

The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like "List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp" in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB.

show abstract

“…On-the-fly generalization process is therefore well suited to this context, because it can interactively adapt the visualized geographic information to decision-makers needs [7]. However it only addresses the cartographic aspect, at the expense of the decisional one, which is important in multidimensional analysis.…”

Section: Introductionmentioning

confidence: 99%