Source Integration is one of the core problems in Data Warehousing. Two critical factors for the design and maintenance of applications requiring Source Integration, and in particular Data Warehouse applications, are conceptual modeling of the domain, and reasoning support over the conceptual representation. We present a novel approach to conceptual modeling for Source Integration, which allows...

Warehousing data is not a trivial task, particularly when dealing with huge amounts of distributed and heterogeneous data. Moreover, traditional decision support systems do not feature intelligent capabilities for integrating such complex data. Therefore, we propose an approach for intelligent decision support based on active XML warehousing. We exploit XML as a pivot language in order to unify...

Big data is a ubiquitous feature of large modern enterprises. Many organizations generate huge amounts of on-line streaming data – examples include network monitoring, Twitter feeds, financial data, and industrial application monitoring. Making effective use of these data streams can be challenging. While Data Stream Management Systems can provide support for realtime alerting and data reductio...

Traditional data warehouse projects follow a waterfall development model in which the project goes through distinct phases such as requirements gathering, design, development, testing, deployment, and stabilization. However, both business requirements and technology are complex in nature and the waterfall model can take six to nine months to fully implement a solution; by then business as well ...

The Data Warehousing field has been fundamentally changed by the Big Data revolution. Computational and storage methodologies such as Hadoop provide an alternate way of managing and analyzing the torrent of data that is flooding in from all manner of instrumentation. This review article will elucidate the relationship between traditional enterprise data warehousing and one of the primary analyt...

This paper demonstrates how the soft systems methodology can be used to improve the delivery of a module in data warehousing for fourth year information technology students. Graduates in information technology needs to have academic skills but also needs to have good practical skills to meet the skills requirements of the information technology industry. In developing and improving current data...

A data warehousing system collects data from multiple distributed sources and stores the integrated information as materialized views in a local data warehouse. Users then perform data analysis and mining on the warehouse views. Figure 1 shows the basic architecture of a data warehousing system. In many cases, the warehouse view contents alone are not su cient for in-depth analysis. It is often...

from long time ago the shortage of finance in business practices was one of the striking problems for traders and thus there have always been endeavors to find a solution to it. one of the ways put forward was using the stocks stored in warehouses as security for loans. taking advantage of a descriptive method, this paper describes field-warehousing as a means of raising cash funds and the regu...

“Outcomes Research” in molecular immunology is driven by faster, cheaper and increasingly sophisticated methods such as miniaturisation, automation, and data integration. The latter is a prerequisite for efficient information analysis, knowledge discovery, and eventually research planning. The data warehousing approach has been successfully applied for managing clinical data [1], but rarely in ...