Logical Representation of a Conceptual Model for Spatial Data Warehouses

Malinowski, Elzbieta; Zimányi, Esteban

doi:10.1007/s10707-007-0022-3

Cited by 28 publications

(28 citation statements)

References 26 publications

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“…Malinowski and Zimányi [19][20] define an extension of ER model to represent dimensions, hierarchies and spatial measures/levels. The extension makes use of classes and relationships, both stereotyped with spatial pictograms, in order to model the geometry of spatial levels and the topological relationships between these levels.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Coverage and Expressive Power of Spatial Data Warehousing Modeling: The SDWM Approach

Cuzzocrea

Fidalgo

2012

Data Warehousing and Knowledge Discovery

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Abstract. This paper proposes a novel perspective of research on the challenging issue of modeling Spatial Data Warehouses (SDW) that nicely contributes to improve state-of-the-art proposals. This conveys in the so-called Spatial Data Warehouse Metamodel (SDWM) that allow us to enhance both coverage and expressive power of SDW modeling by means of the following amenities: (i) separating the conceptual SDW modeling from the conceptual (spatial) OLAP modeling; (ii) supporting the modeling of complex constructs in SDW; and (iii) stereotyping attributes and measures as spatial objects directly. All these contributions finally depict a novel perspective of research in the investigated scientific field, which breaks the actual trend of state-of-the-art initiatives, by pinpointing their limitations. We complete our analytical contribution by means of a real-life application implemented via SDWM, which highlights the benefits deriving from applying SDWM in contrast with traditional SDW modeling methodologies.

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Section: Related Workmentioning

confidence: 99%

Enhancing Coverage and Expressive Power of Spatial Data Warehousing Modeling: The SDWM Approach

Cuzzocrea

Fidalgo

2012

Data Warehousing and Knowledge Discovery

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“…In particular, in [30], the authors define measures as attributes of an n-ary fact relationship between dimensions. 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.…”

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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“…A spatial data warehouse (SDW) is a multidimensional database that inherits and combines capabilities and characteristics from the data warehouse (DW), the geographic information system (GIS) and the on-line analytical processing (OLAP) to explore the decision-making process [1][2][3][4]. Like a conventional DW, a SDW is a subject-oriented, integrated, timevariant, voluminous and non-volatile database.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, in a SDW, hierarchies may be defined also over spatial attributes of one or more spatial dimension tables. A predefined spatial hierarchy is a 1:N association among higher and lower granularity spatial attributes that is determined by a spatial relationship, such as (region_geo) (nation_geo) (city_geo) (s_address_geo), where the spatial relationship is containment [3,12]. Regarding this example of predefined spatial hierarchy, it states that a given address is inside of only a specific city, a given city is inside of only a specific nation, and a given nation is inside of only a specific region.…”

Section: Introductionmentioning

confidence: 99%

The SB-index and the HSB-index: efficient indices for spatial data warehouses

et al. 2011

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Spatial data warehouses (SDWs) allow for spatial analysis together with analytical multidimensional queries over huge volumes of data. The challenge is to retrieve data related to ad hoc spatial query windows according to spatial predicates, avoiding the high cost of joining large tables. Therefore, mechanisms to provide efficient query processing over SDWs are essential. In this paper, we propose two efficient indices for SDW: the SB-index and the HSB-index. The proposed indices share the following characteristics. They enable multidimensional queries with spatial predicate for SDW and also support predefined spatial hierarchies. Furthermore, they compute the spatial predicate and transform it into a conventional one, which can be evaluated together with other conventional predicates by accessing a star-join Bitmap index. While the SB-index has a sequential data structure, the HSB-index uses a hierarchical data structure to enable spatial Geoinformatica (2012) 16:165-205 objects clustering and a specialized buffer-pool to decrease the number of disk accesses. The advantages of the SB-index and the HSB-index over the DBMS resources for SDW indexing (i.e. star-join computation and materialized views) were investigated through performance tests, which issued roll-up operations extended with containment and intersection range queries. The performance results showed that improvements ranged from 68% up to 99% over both the star-join computation and the materialized view. Furthermore, the proposed indices proved to be very compact, adding only less than 1% to the storage requirements. Therefore, both the SB-index and the HSB-index are excellent choices for SDW indexing. Choosing between the SB-index and the HSBindex mainly depends on the query selectivity of spatial predicates. While low query selectivity benefits the HSB-index, the SB-index provides better performance for higher query selectivity.

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Logical Representation of a Conceptual Model for Spatial Data Warehouses

Cited by 28 publications

References 26 publications

Enhancing Coverage and Expressive Power of Spatial Data Warehousing Modeling: The SDWM Approach

Enhancing Coverage and Expressive Power of Spatial Data Warehousing Modeling: The SDWM Approach

Spatial aggregation: Data model and implementation

The SB-index and the HSB-index: efficient indices for spatial data warehouses

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