Sandro Bimonte

Towards a spatial multidimensional model

Data warehouses and OLAP systems help to interactively analyze huge volume of data. This data, extracted from transactional databases, frequently contains spatial information which is useful for decision-making process. Integration of spatial data in multidimensional models leads to the concept of SOLAP (Spatial OLAP). Using a spatial measure as a geographical object, i.e. with geometric and descriptive attributes, raises problems regarding the aggregation operation in its semantic and implementation aspects. This paper shows the requirements for a multidimensional spatial data model and presents a multidimensional data model which is able to support complex objects as measures, inter-dependent attributes for measures and aggregation functions, use of ad-hoc aggregation functions and n to n relations between fact and dimension, in order to handle geographical data, according to its particular nature in an OLAP context.

When Spatial Analysis Meets OLAP: Multidimensional Model and Operators

Introducing spatial data into multidimensional models leads to the concept of Spatial OLAP SOLAP. Existing SOLAP models do not completely integrate the semantic component of geographic information alphanumeric attributes and relationships or the flexibility of spatial analysis into multidimensional analysis. In this paper, the authors propose the GeoCube model and its associated operators to overcome these limitations. GeoCube enriches the SOLAP concepts of spatial measure and spatial dimension and take into account the semantic component of geographic information. The authors define geographic measures and dimensions as geographic and/or complex objects belonging to hierarchy schemas. GeoCubes algebra extends SOLAP operators with five new operators, i.e., Classify, Specialize, Permute, OLAP-Buffer and OLAP-Overlay. In addition to classical drill-and-slice OLAP operators, GeoCube provides two operators for navigating the hierarchy of the measures, and two spatial analysis operators that dynamically modify the structure of the geographic hypercube. Finally, to exploit the symmetrical representation of dimensions and measures, GeoCube provides an operator capable of permuting dimension and measure. In this paper, GeoCube is presented using environmental data on the pollution of the Venetian Lagoon.

GeoCube, a multidimensional model and navigation operators handling complex measures: application in spatial OLAP

Data warehouses and OLAP systems help to interactively analyze huge volume of data. Frequently this data contains spatial information which is useful for decision-making process. Spatial OLAP (SOLAP) refers to the integration of spatial data in multidimensional applications at physical, logical and conceptual level. Using spatial measure as a geographical object, i.e. taking in account its geometric and descriptive attributes, raises problems regarding the aggregation operation and the cube navigation in their semantic and implementation aspects. This paper defines an extended multidimensional data model which is able to support complex objects as measures, in order to handle geographical data according with its particular nature in an OLAP context. The model allows the multidimensional navigation process. OLAP operators are described which include this new concept of measure. A prototype of a SOLAP tool that handles geographical object as measures is presented.

Integrating Google Earth within OLAP tools for Multidimensional Exploration and Analysis of Spatial Data

Spatial OnLine Analytical Processing solutions are a type of Business Information Tool meant to support a Decision Maker in extracting hidden knowledge from data warehouses containing spatial data. To date, very few SOLAP tools are available, each presenting some drawbacks reducing their flexibility. To overcome these limitations, we have developed a web-based SOLAP tool, obtained by suitably integrating into an ad-hoc architecture the Geobrowser Google Earth with a freely available OLAP engine, namely Mondrian. As a consequence, a Decision Maker can perform exploration and analysis of spatial data both through the Geobrowser and a Pivot Table in a seamlessly fashion. In this paper, we illustrate the main features of the system we have developed, together with the underlying architecture, using a simulated case study.

GeWOlap: a web based spatial OLAP proposal

Data warehouses and OLAP systems help to interactively analyze huge volumes of data Spatial OLAP refers to the integration of spatial data in multidimensional applications at the physical, logical and conceptual level In order to include spatial information as a result of the decision-making process, we propose to define spatial measures as geographical objects in the multidimensional data model This raises problems regarding aggregation operations and cube navigation in both semantic and implementation aspects This paper presents a GeWOlap, a web based, integrated and extensible GIS-OLAP prototype, able to support geographical measures Our approach is illustrated by its application in a project for the CORILA consortium (Consortium for Coordination of Research Activities concerning the Venice Lagoon System).

Spatial OLAP and Map Generalization: Model and Algebra

Map generalization can be used as a central component of Spatial Decision Support Systems to provide a simplified and more readable cartographic visualization of geographic information. Indeed, it supports the user mental process for discovering important and unknown geospatial relations, trends and patterns. Spatial OLAP SOLAP integrates spatial data into OLAP and data warehouse systems. SOLAP models and tools are based on the concepts of spatial dimensions and measures that represent the axes and the subjects of the spatio-multidimensional analysis. Although powerful under some respect, current SOLAP models cannot support map generalization capabilities. This paper provides the first effort to integrate Map Generalization and OLAP. Firstly the authors define all modeling and querying requirements to do this integration, and then present a SOLAP model and algebra that support map generalization concepts. The approach extends SOLAP spatial hierarchies introducing multi-association relationships, supports imprecise measures, and it takes into account spatial dimensions constraints generated by multiple map generalization hierarchies.

A Web-Based Tool for Spatio-Multidimensional Analysis of Geographic and Complex Data

Spatial OLAP (SOLAP) integrates spatial data into OLAP systems, and SOLAP models define spatial dimensions while measuring spatio-multidimensional operators. In this paper, the author presents the concepts of geographic and complex measures that allow integrating geographic and complex information as subjects of analysis in spatial data warehouses. The concept of geographic measure extends the concept of spatial measure to the semantic component of geographic information. The concept of complex measure allows introducing complex data as subjects of multidimensional analysis. To reduce the gap in flexibility between spatial and multidimensional analysis, this paper proposes a symmetrical representation of measures and dimensions. Additionally, the author presents a Web-based SOLAP prototype, GeWOlap, that enriches existing SOLAP tools by effectively and easily supporting symmetrical geographic/complex measures and dimensions for modeling and visualization. To validate this approach, the simulated environmental data concerning the pollution of the Venice lagoon is used.

A Multidimensional Model for Data Warehouses of Simulation Results

This paper examines the multidimensional modeling of a data warehouse for simulation results. Environmental dynamics modeling is used to study complex scenarios like urbanization, climate change and deforestation while allowing decision makers to understand and predict the evolution of the environment in response to potential value changes in a large number of influence variables. In this context, exploring simulation models produces a huge volume of data, which must often be studied extensively at different levels of aggregation due to there being a great need to define tools and methodologies specifically adapted for the storage and analysis of such complex data. Data warehousing systems provide technologies for managing simulation results from different sources. Moreover, OLAP technologies allow one to analyze and compare these results and their corresponding models. In this paper, the authors propose a generic multidimensional schema to analyze the results of a simulation model, which can guide modelers in designing specific data warehouses, and an adaptation of an OLAP client tool to provide an adequate visualization of data. As an example, a data warehouse for the analysis of results produced from a savanna simulation model is implemented using a Relational OLAP architecture.

Multidimensional arrays for warehousing data on clouds

Data warehouses and OLAP systems are business intelligence technologies. They allow decision-makers to analyze on the fly huge volumes of data represented according to the multidimensional model. Cloud computing on the impulse of ICT majors like Google, Microsoft and Amazon, has recently focused the attention. OLAP querying and data warehousing in such a context consists in a major issue. Indeed, problems to be tackled are basic ones for large scale distributed OLAP systems (large amount of data querying, semantic and structural heterogeneity) from a new point of view, considering specificities from these architectures (pay-as-you-go rule, elasticity, and user-friendliness). In this paper we address the pay-as-you-go rules for warehousing data storage. We propose to use the multidimensional arrays storage techniques for clouds. First experiments validate our proposal.

Un modèle UML et des contraintes OCL pour les entrepôts de données spatiales: De la représentation conceptuelle à l'implémentation

Spatial Data Warehouses (SDW) and Spatial OLAP (SOLAP) systems represent an effective solution to perform spatial analysis on geographical phenomena. However, the quality of such analysis heavily depends on the quality of stored data and how these data are explored: how the different indicators are computed (What aggregate functions are applied to summarize the measures and in what order these functions are applied?). In this context, a number of studies have been attempted to address the issues of data quality in SDW by using Integrity Constraints (IC). In this paper, motivated by the lack of Model Driven Architecture (MDA)-based implementations, we propose a conceptual framework based on two new classifications to ease identification and implementation of SDW IC. Moreover, following an MDA approach, we propose the MDA-based modeling of most IC categories using the UML (Unified Modeling Language) and OCL (Object Constraint Language) standard languages; and show the automatic implementation of some IC classes using an MDA-based code generator, called Spatial OCL2SQL.