A data cube model for analysis of high volumes of ambient data

Ambient systems generate large volumes of data for many of their application areas with XML often the format for data exchange. As a result, large scale ambient systems such as smart cities require some form of optimization before different components can merge their data streams. In data warehousing, the cube structure is often used for optimizing the analytics process with more recent structures such as dwarf, providing new orders of magnitude in terms of optimizing data extraction. However, these systems were developed for relational data and as a result, we now present the development of an XML dwarf to manage ambient systems generating XML data

Diamond multidimensional model and aggregation operators for document OLAP

International audienceOn-Line Analytical Processing (OLAP) has generated methodologies for the analysis of structured data. However, they are not appropriate to handle document content analysis. Because of the fast growing of this type of data, there is a need for new approaches abling to manage textual content of data. Generally, these data exist in XML format. In this context, we propose an approach of construction of our Diamond multidimensional model, which includes semantic dimension to better consider the semantics of textual data In addition, we propose new aggregation operators for textual data in OLAP environment

E‐ARK Dissemination Information Package (DIP) Final Specification

The primary aim of this report is to present the final version of the E-ARK Dissemination Information Package (DIP) formats. The secondary aim is to describe the access scenarios in which these DIP formats will be rendered for use

Data warehouse structuring methodologies for efficient mining of Western Australian petroleum data sources

Representing the knowledge domain of a petroleum system is a complex problem. In the present study, logical modelling of shared attributes of resources industry entities (dimensions or objects) has been used for construction of a dynamic and time-variant metadata model. This work demonstrates effectiveness of multidimensional data modelling for petroleum industry, which will be further investigated for fine-grain data presentation and interpretation for quality knowledge discovery