Merging Hypermedia GIS with Spatial On-Line Analytical Processing: Towards Hypermedia SOLAP

Bédard, Yvan; Proulx, Marie-Josée; Rivest, Sonia; Badard, Thierry

doi:10.1007/978-3-540-34238-0_10

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…[14] present many possibilities to explore spatial data in OLAP cubes. The work [15] combines hypermedia documents and spatial data in OLAP hypercubes (hypermedia hypercubes). Piet is an implementation of a GIS-OLAP integration [16] presenting a method for precomputation of spatial overlay aggregations.…”

Section: Related Workmentioning

confidence: 99%

Multi-representation Lens for Visual Analytics

Gatto

Santanchè

2012

2012 IEEE 28th International Conference on Data Engineering Workshops

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Modern data analysis deeply relies on computational visualization tools, specially when spatial data is involved. Important efforts in governmental and private agencies are looking for patterns and insights buried in dispersive, massive amounts of data (conventional, spatiotemporal, etc.). In Visual Analytics users must be empowered to analyze data from different perspectives, integrating, transforming, aggregating and deriving new representations of conventional as well as spatial data. However, a challenge for visual analysis tools is how to articulate such wide variety of data models and formats, specially when multiple representations of geographic elements are involved. A usual approach is to convert data to a database -e.g., a multirepresentation database -which centralizes and homogenizes them. This approach has restrictions when facing the dynamic and distributed model of the Web. In this paper we propose an on the fly and on demand multi-representation data integration and homogenization approach, named Lens, as an alternative that fits better with the Web. It combines a metamodel driven approach to transform data to a unifying multidimensional and multirepresentation model, with a middleware-based architecture for seamless and on-the-fly data access, tailored to Visual Analytics.

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Section: Related Workmentioning

confidence: 99%

Multi-representation Lens for Visual Analytics

Gatto

Santanchè

2012

2012 IEEE 28th International Conference on Data Engineering Workshops

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“…Such approaches are not novel, but we do not yet know how best to design coordinated geovisual environments in the context of geospatial big data. Recent work to define new types of data structures around spatial dimensions (Bédard, Proulx, Rivest, & Badard, 2006) and trajectories (Leonardi et al, 2013) could help support rapid interaction with new cartographic interfaces to big data.…”

Section: Varietymentioning

confidence: 99%

Geospatial big data and cartography: research challenges and opportunities for making maps that matter

Robinson

Demšar

Moore

et al. 2017

International Journal of Cartography

113

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Geospatial big data present a new set of challenges and opportunities for cartographic researchers in technical, methodological and artistic realms. New computational and technical paradigms for cartography are accompanying the rise of geospatial big data. Additionally, the art and science of cartography needs to focus its contemporary efforts on work that connects to outside disciplines and is grounded in problems that are important to humankind and its sustainability. Following the development of position papers and a collaborative workshop to craft consensus around key topics, this article presents a new cartographic research agenda focused on making maps that matter using geospatial big data. This agenda provides both longterm challenges that require significant attention and short-term opportunities that we believe could be addressed in more concentrated studies.

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“…Newell, 1990). More recently, SOLAP has been enriched with hypermedia and annotation (red-lining) functionalities (Bé dard et al, 2006).…”

Section: Objectives and Proposed Solutionmentioning

confidence: 99%