The Dynameomics project is our effort to characterize the native-state dynamics and folding/unfolding pathways of representatives of all known protein folds by way of molecular dynamics simulations, as described by Beck et al. (in Protein Eng. Des. Select., the first paper in this series). The data produced by these simulations are highly multidimensional in structure and multi-terabytes in siz...

Decision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which use specific views or cubes from the corporate DW or Data Marts, based on the multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized accesses. In ...

Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube, where each cell contains one or more measures can be aggregated along dimensions. Despite the extensive corpus of work in the field, a standard language for OLAP is still needed, since there is no well-def...

RÉSUMÉ. Les bases de données orientées colonnes offrent au domaine décisionnel le modèle le plus approprié au stockage des entrepôts de données. Cependant, en l’absence d’opérateurs d’analyse en ligne, le seul moyen, très coûteux, qui existe pour construire des cubes OLAP consiste à utiliser l’opérateur UNION sur des requêtes de regroupement afin d’obtenir l’ensemble des Group By nécessaires au...

In the last ten years, database vendors have invested heavily in order to extend their products with new features for decision support. Examples of functionality that has been added are top N [2], ranking [13, 7], spreadsheet computations [19], grouping sets [14], data cube [9], and moving sums [15] in order to name just a few. Unfortunately, many modern OLAP systems do not use that functionali...

The rapidly changing data requirements of today’s dynamic business environments are not handled well by current On-Line Analytical Processing (OLAP) systems. Physically integrating data from new sources into OLAP systems is a long and time-consuming process, making logical integration the better choice in many situations. The increasing use of Extended Markup Language (XML), e.g. in business-to...

On-line analytical processing (OLAP) is an example of a new breed of tools for decision support that give decision makers the flexibility to customize the selection, aggregation, and presentation of data. To understand the impact of this type of tool, we study an implementation of an OLAP interface on the CATCH data warehouse used by knowledge workers at a regional health planning agency in the...

Due to the widespread adoption of locationbased services and other spatial applications, data warehouses that store spatial information are becoming increasingly prevalent. Consequently, it is becoming important to extend the standard OLAP paradigm with features that support spatial analysis and aggregation. While traditional OLAP systems are limited to data characterized by strictly categorica...

On-line analytical processing (OLAP) and data warehousing technologies has been accepted in business community as indispensable tools of effective data usage. Contemporary industrial process are as complex as the ones in the business field. OLAP techniques may be utilized in management of industrial process once there are implementations satisfying strident timing requirements of industrial pro...

Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube, where each cell contains one or more measures can be aggregated along dimensions. Despite the extensive corpus of work in the field, a standard language for OLAP is still needed, since there is no well-def...