Abstract Multi-model DBMSs, which support different data models with a fully integrated backend, have been shown to be beneficial to data warehouses and OLAP systems. Indeed, they can store data according to the multidimensional model and, at the same time, let each of its elements be represented through the most appropriate model. An open challenge in this context is the lack of methods for logical design. Indeed, in a multi-model context, several alternatives emerge for the logical representation of dimensions and facts. The goal of this paper is to devise a set of guidelines for the logical design of multi-model data warehouses so that the designer can achieve the best trade-off between features such as querying, storage, and ETL. To this end, for each model considered (relational, document-based, and graph-based) and for each type of multidimensional element (e.g., non-strict hierarchy) we propose some solutions and carry out a set of intra-model and inter-model comparisons. The resulting guidelines are then tested on a case study that shows all types of multidimensional elements.

Spatial OLAP (SOLAP) systems allow to explore and analyze huge volume of georeferenced data by means of pivot tables, graphical displays and maps. In the last years, SOLAP and Spatial Data Warehouses has received a lot of attention from the academic and industrial communities, who mainly focus on logical and physical data models to improve SOLAP queries performance. Although, geovisualization is essential for effective SOLAP analysis, few works propose advanced methods for the visualization of SOLAP queries results using cartographic displays. In this work, we present some geovisualization methods to reach an effective visualization of pivot table data over maps. We implement these proposals in a web-based SOLAP system, and we use a real case study concerning the analysis of the agrobiodiversity data for the demo scenario.

A decision support system is used by decision makers for a long time. But, in some cases, the originally designed multidimensional schema does not cover the entire needs of decision makers, which can change over time. One such unfulfilled need, is using facts to describe dimension members. In this article, we propose a methodology to transform the constellation schema of a data warehouse by integrating factual data into a dimension. The proposed methodology and algorithms enrich a constellation multidimensional schema with new analytical possibilities for decision makers. This enrichment has repercussions for the entire multidimensional schema that are managed by multidimensional modeling, hierarchy calculation and the hierarchy version. In this article, we present a theoretical view of the proposed methodology supported by a case study, an implemented prototype and a complete evaluation based on a standard benchmark.

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]MOTIVE

The approaches to data warehouse design are based on the assumption that source data are known in advance and available. While this assumption is true in common project situations, in some peculiar contexts it is not. This is the case of the French national project for analysis of energetic agricultural farms, that is the case study of this paper. Here, the above-mentioned methods can hardly be applied because source data can only be identified and collected once user requirements indicate a need. Besides, the users involved in this project found it very hard to express their analysis needs in abstract terms, i.e., without visualizing sample results of queries, which in turn would require availability of source data. To solve this deadlock we propose ProtOLAP, a tool-assisted fast prototyping methodology that enables quick and reliable test and validation of data warehouse schemata in situations where data supply is collected on users' demand and users' ICT skills are minimal. To this end, users manually feed sample realistic data into a prototype created by designers, then they access and explore these sample data using pivot tables to validate the prototype.

Spatial Data Warehouses (SDWs) and Spatial OLAP systems are an effective solution to perform multidimensional spatial analysis on geographical data. However, the quality of such analysis heavily depends on the quality of stored data. Due to this, some works have been attempted to address the issues of data quality of SDWs using Integrity Constraints (ICs). In this paper, we propose a conceptual framework for SDW ICs based on two new classifications. Moreover, we describe the Model-Driven Architecture (MDA)-based specification and implementation of some IC classes using the Object Constraint Language (OCL) and an MDA-based code generator, called Spatial OCL2SQL

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.