A Data Model for Moving Objects Supporting Aggregation

Kuijpers, Bart; Vaisman, Alejandro

doi:10.1109/icdew.2007.4401040

Cited by 15 publications

(19 citation statements)

References 13 publications

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“…That means, spatial objects in thematic layers can be added, removed, split, merged, or their shape may change. In [17,32] the authors show that the Piet framework supports continuous motion, following the classification above. However, Piet works under the assumption that all objects in a layer remain unchanged across time, i.e., neither does Piet support objects with discrete changes nor objects combining continuous motion and changing shapes.…”

Section: Gis-olap Decision Supportmentioning

confidence: 94%

A data model and query language for spatio-temporal decision support

2010

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In recent years, applications aimed at exploring and analyzing spatial data have emerged, powered by the increasing need of software that integrates Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP). These applications have been called SOLAP (Spatial OLAP). In previous work, the authors have introduced Piet, a system based on a formal data model that integrates in a single framework GIS, OLAP (On-Line Analytical Processing), and Moving Object data. Real-world problems are inherently spatio-temporal. Thus, in this paper we present a data model that extends Piet, allowing tracking the history of spatial data in the GIS layers. We present a formal study of the two typical ways of introducing time into Piet: timestamping the thematic layers in the GIS, and timestamping the spatial objects in each layer. We denote these strategies snapshot-based and timestamp-based representations, respectively, following well-known terminology borrowed from temporal databases. We present and discuss the formal model for both alternatives. Based on the timestamp-based representation, we introduce a formal First-Order spatio-temporal query language, which we denote L t , able to express spatio-temporal queries over GIS, OLAP, and trajectory data. Finally, we discuss implementation issues, the update operators that must be supported by the 456 Geoinformatica (2011) 15:455-496 model, and sketch a temporal extension to Piet-QL, the SQL-like query language that supports Piet.

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Section: Gis-olap Decision Supportmentioning

confidence: 94%

A data model and query language for spatio-temporal decision support

2010

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“…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. The GooLAP [17] proposes a three-tier architecture to build a system for spatial OLAP operations; but the authors split data in two different databases, a geographic boundaries database and a spatial data warehouse, while our approach unifies shapes in a centralized data repository.…”

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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“…The implementation we present in this work is based in the data model introduced in [2,9]. There, the authors proposed a model where spatial and non-spatial data are integrated in a single framework.…”

Section: Data Model Overviewmentioning

confidence: 99%

“…Our work is aimed at integrating these two different worlds in a single framework [2,9]. In this way, we will be able to evaluate a query like "total income in provinces crossed by at least one river", and navigate the results in the usual OLAP way.…”

Section: Introductionmentioning

confidence: 99%

Piet

Escribano

Gómez

Kuijpers

et al. 2007

Proceedings of the ACM Tenth International Workshop on Data Warehousing and OLAP

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Data aggregation in Geographic Information Systems (GIS)is a desirable feature, although only marginally present in commercial systems, which also fail to provide integration between GIS and OLAP (On Line Analytical Processing). With this in mind, we have developed Piet, a system that makes use of a novel query processing technique: first, a process called sub-polygonization decomposes each thematic layer in a GIS, into open convex polygons; then, another process computes and stores in a database the overlay of those layers for later use by a query processor. We describe the implementation of Piet, and provide experimental evidence that overlay precomputation can outperform GIS systems that employ indexing schemes based on R-trees.

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A Data Model for Moving Objects Supporting Aggregation

Abstract: Moving objects databases (MOD)

Cited by 15 publications

References 13 publications

A data model and query language for spatio-temporal decision support

A data model and query language for spatio-temporal decision support

Multi-representation Lens for Visual Analytics

Piet

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