Abstract Multi-model DBMSs, which support different data models with a fully integrated backend, have been shown to be beneficial warehouses and OLAP systems. Indeed, they can store according the multidimensional model and, at same time, let each of its elements represented through most appropriate model. An open challenge in this context is lack methods for logical design. multi-model context,...

Data warehouses have been developed to answer the increasing demands of quality information required by the top managers and economic analysts of organizations. Their importance in now a day business area is unanimous recognized, being the foundation for developing business intelligence systems. Data warehouses offer support for decision-making process, allowing complex analyses which cannot be...

The popularity of the Semantic Web (SW) encourages organizations to organize and publish semantic data using RDF model. This growth poses new requirements Business Intelligence technologies enable On-Line Analytical Processing (OLAP)-like analysis over data. incorporation into a Data Warehouse (DW) is not supported by traditional Extract-Transform-Load (ETL) tools because they do consider issue...

During the last decade, data warehouse and OLAP techniques have helped companies to gather, organize and analyze the structured data they produce. Simultaneously, digital libraries have applied Information Retrieval mechanisms to query their repositories of unstructured documents. In this context, the emergence of XML means the convergence of these two approaches, making possible the developmen...

The increasing need for Decision Support Systems for business enterprises has lead to tremendous growth of Data Warehouses. Within large enterprises number of data sources is increasing; due to which Data Warehouses are getting more and more complex. The ETL componentone of the basic components of data warehousecan be made simpler with the use of Distributed Database technology in the developme...