An open source and web based framework for geographic and multidimensional processing

Silva, Joel da; Times, Valéria Cesário; Salgado, Ana Carolina

doi:10.1145/1141277.1141292

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…directed to the subject, integrated, nonvolatile and varying in time. Furthermore, a GDW must offer support to storage, indexing structures, aggregation functions and georeferenced analysis in maps or tables [13,44].…”

Section: Introductionmentioning

confidence: 99%

Modelling and querying geographical data warehouses

Silva

Oliveira

Fidalgo

et al. 2010

Information Systems

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Section: Introductionmentioning

confidence: 99%

Modelling and querying geographical data warehouses

Silva

Oliveira

Fidalgo

et al. 2010

Information Systems

Self Cite

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“…Malinowski and Zymani [22] extend this classification considering that a dimension level is spatial if it is represented as a spatial data type (point, region, etc.). Da Silva, Times, and Salgado [23] provide a framework, called GeoDWFrame, classifying dimensions as geographic data or geographic and non spatial data. A dimension is considered to be spatial if it contains at least one spatial dimension level [24].…”

Section: A Spatiotemporal Data Warehousesmentioning

confidence: 99%

Transforming multidimensional models into OWL-DL ontologies

Prat¹,

Akoka²,

Comyn-Wattiau³

2012

2012 Sixth International Conference on Research Challenges in Information Science (RCIS)

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Abstract-Business intelligence is based on data warehouses. Data warehouses use a multidimensional model, which represents relevant facts and their measures according to different dimensions. Based on this model, OLAP cubes may be defined, enabling decision makers to analyze and synthesize data. Ontologies (and, more specifically, OWL ontologies) are a key component of the semantic Web. This paper proposes an approach to represent multidimensional models as OWL-DL ontologies. To this end, it presents the multidimensional metamodel, the concepts of OWL-DL, and transformation rules for mapping a multidimensional model into and OWL-DL ontology. It then illustrates application to a case study with a simplified example of a spatiotemporal data warehouse. The transformation rules are refined to deal with spatiotemporal data warehouses, applied step by step, and the resulting ontology is implemented in the Protégé ontology tool. As illustrated by the case study, our approach enables better formalization and inferencing, thanks to OWL-DL. The ontology may also be used to represent OLAP cubes on the semantic Web (with RDF), by defining these cubes as instances of the OWL-DL multidimensional ontology.

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“…Further on, the same authors present a method to transform a conceptual schema to a logical one, expressed in the Object-Relational paradigm [31]. Fidalgo et al [11] and da Silva et al [46] introduced GeoDWFrame, a framework for spatial OLAP, which classifies dimensions as geographic and hybrid, if they represent only geographic data, or geographic and nonspatial data, respectively. Over this framework, da Silva et al [47] propose GeoMDQL, a query language based on MDX and OGC 6 simple features, for querying spatial data cubes.…”

Section: Article In Pressmentioning

confidence: 99%

Spatial aggregation: Data model and implementation

Gómez

Haesevoets²,

Kuijpers

et al. 2009

Information Systems

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a b s t r a c tData aggregation in Geographic Information Systems (GIS) is a desirable feature, only marginally present in commercial systems nowadays, mostly through ad hoc solutions. We address this problem introducing a formal model that integrates, in a natural way, geographic data and non-spatial information contained in a data warehouse external to the GIS. This approach allows both aggregation of geometric components and aggregation of measures associated to those components, defined in GIS fact tables. We define the notion of geometric aggregation, a general framework for aggregate queries in a GIS setting. Although general enough to express a wide range of (aggregate) queries, some of these queries can be hard to compute in a real-world GIS environment because they involve computing an integral over a certain area. Thus, we identify the class of summable queries, which can be efficiently evaluated replacing this integral with a sum of functions of geometric objects. Integration of GIS and OLAP (On Line Analytical Processing) is supported also through a language, GISOLAP-QL. We present an implementation, denoted Piet, which supports four kinds of queries: standard GIS, standard OLAP, geometric aggregation (like ''total population in states with more than three airports''), and integrated GIS-OLAP queries (''total sales by product in cities crossed by a river'', also allowing navigation of the results). Further, Piet implements a novel query processing technique: first, a process called subpolygonization decomposes each thematic layer in a GIS, into open convex polygons; then, another process (the overlay precomputation) computes and stores in a database the overlay of those layers for later use by a query processor. Experimental evaluation showed that for a wide class of geometric queries, overlay precomputation outperforms R-tree-based techniques, suggesting that it can be an alternative for GIS query processing.

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An open source and web based framework for geographic and multidimensional processing

Cited by 14 publications

References 10 publications

Modelling and querying geographical data warehouses

Modelling and querying geographical data warehouses

Transforming multidimensional models into OWL-DL ontologies

Spatial aggregation: Data model and implementation

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