Extending the E/R Model for the Multidimensional Paradigm

Sapia, Carsten; Blaschka, Markus; Höfling, Gabriele; Dinter, Barbara

doi:10.1007/978-3-540-49121-7_9

Cited by 105 publications

(80 citation statements)

References 8 publications

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“…There are three methods of conceptual modeling of DWs; the first one is the top down approach [16] that is based on the needs of the users, the second is the bottom-up approach [5] that begins with the operational data sources and finally the mixed approach [17] that combines the two previous approaches. We used the top down approach in our modeling www.ijacsa.thesai.org phase because we were interested in user's needs.…”

Section: Adopted Methodologymentioning

confidence: 99%

Extending UML for trajectory data warehouses conceptual modelling

Oueslati¹,

Akaichi²

2012

IJACSA

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ABSTRACT-The new positioning and information capture technologies are able to treat data related to moving objects taking place in targeted phenomena. This gave birth to a new data source type called trajectory data (TD) which handle information related to moving objects. Trajectory Data must be integrated in a new data warehouse type called trajectory data warehouse (TDW) that is essential to model and to implement in order to analyze and understand the nature and the behavior of movements of objects in various contexts. However, classical conceptual modeling does not incorporate the specificity of trajectory data due to the complexity of their components that are spatial, temporal and thematic (semantic). For this reason, we focus in this paper on presenting the conceptual modeling of the trajectory data warehouse by defining a new profile using the StarUML extensibility mechanism.

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Section: Adopted Methodologymentioning

confidence: 99%

Extending UML for trajectory data warehouses conceptual modelling

Oueslati¹,

Akaichi²

2012

IJACSA

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“…Now, we present a study of the best available conceptual and logical models for data warehousing. Existing conceptual modeling approaches can be broadly classified into Extensions of Entity Relationship (E/R) models ( [6][7][8][9][10]), Extensions of Unified Modeling Language (UML) ( [11][12][13]) and Ad-hoc ( [14][15][16][17]) design models. Several different logical models have also been proposed to model multidimensional data in the past few years.…”

Section: Related Workmentioning

confidence: 99%

On the Requirements for User-Centric Spatial Data Warehousing and SOLAP

Viswanathan

Schneider

2011

Lecture Notes in Computer Science

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Abstract. Data warehouses and OLAP systems help to analyze complex multidimensional data and provide decision support. With the availability of large amounts of spatial data in recent years, several new models have been proposed to enable the integration of spatial data in data warehouses and to help analyze such data. This is often achieved by a combination of GIS and spatial analysis tools with OLAP and database systems, with the primary goal of supporting spatial analysis dimensions, spatial measures and spatial aggregation operations. However, this poses several new challenges related to spatial data modeling in a multidimensional context, such as the need for new spatial aggregation operations and ensuring consistent and valid results. In this paper, we review the existing modeling strategies for spatial data warehouses and SOLAP in all three levels: conceptual, logical and implementation. While studying these models, we gather the most essential requirements for handling spatial data in data warehouses and use insights from spatial databases to provide a "meta-framework" for modeling spatial data warehouses. This strategy keeps the user as the focal point and achieves a clear abstraction of the data for all stakeholders in the system. Our goal is to make analysis more user-friendly and pave the way for a clear conceptual model that defines new multidimensional abstract data types (ADTs) and operations to support spatial data in data warehouses.

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“…Early approaches [9,10,24] are only concerned about static aspects and lack of mechanisms to properly model multidimensional query behavior. At most, these approaches suggest a limited set of predefined aggregation functions but without providing a formal definition.…”

Section: Related Workmentioning

confidence: 99%

Specifying Aggregation Functions in Multidimensional Models with OCL

Cabot

Mazón

Pardillo

et al. 2010

Conceptual Modeling – ER 2010

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Abstract. Multidimensional models are at the core of data warehouse systems, since they allow decision makers to early define the relevant information and queries that are required to satisfy their information needs. The use of aggregation functions is a cornerstone in the definition of these multidimensional queries. However, current proposals for multidimensional modeling lack the mechanisms to define aggregation functions at the conceptual level: multidimensional queries can only be defined once the rest of the system has already been implemented, which requires much effort and expertise. In this sense, the goal of this paper is to extend the Object Constraint Language (OCL) with a predefined set of aggregation functions. Our extension facilitates the definition of platform-independent queries as part of the specification of the conceptual multidimensional model of the data warehouse. These queries are automatically implemented with the rest of the data warehouse during the code-generation phase. The OCL extensions proposed in this paper have been validated by using the USE tool.

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Extending the E/R Model for the Multidimensional Paradigm

Cited by 105 publications

References 8 publications

Extending UML for trajectory data warehouses conceptual modelling

Extending UML for trajectory data warehouses conceptual modelling

On the Requirements for User-Centric Spatial Data Warehousing and SOLAP

Specifying Aggregation Functions in Multidimensional Models with OCL

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