The Traditional data warehouse did not contain data as today. Hence, it is difficult to retrieve these data and treat them. Furthermore, its content is not updated, which may lead to bad decisions. Data are typically loaded from conventional operational systems. Given that today’s decisions, in the business world, are becoming more and more in real time. Thus, the system supporting these decisi...

Data warehouses collect data from multiple remote sources and integrate the information as materialized views in a local database. The materialized views are used to answer queries that analyze the collected data for patterns, anomalies, and trends. This type of query processing is often called on-line analytical processing (OLAP). So that OLAP queries can be posed and answered easily, the data...

Intelligent and comprehensive data warehouse systems are a powerful instrument for organizations to analyze their business. The implementation of such decision systems for an enterprise-wide management and decision support can be very different from traditional software implementations. Because data warehouse systems are strongly data-driven, the development process is highly dependent on its u...

Original scientific paper Warehouse management is one of the most important legs in supply chain operations and production management. To support that warehouse execution, a warehouse management system is required. Many warehouses have used various methods to handle this difficult and time consuming evaluation process. The purpose of this paper is to provide a decision support model to make a c...

Poor quality of data in a warehouse adversely impacts the usability of the warehouse and managing data quality in a warehouse is very important. In this article we describe a framework for managing data quality in a data warehouse. This framework is of interest to both academics and practitioners as it offers an intuitive approach for not just managing data quality in a data warehouse but also ...

Data warehouse projects bring together different communities of practice, with the primary objective of producing one body of information which is capable of comparative advantages in business analysis. Due to the number of involved communities and the complexity of their collaboration, data warehouse projects are costly. In this paper we give a closer look at communication problems on boundari...

Data warehouse systems are used by decision makers for performance measurement and decision support. Measures such as the number of transactions per customer or the increase of sales during a promotion are used to recognize warning signs and to decide on future investments with regard to the strategic goals of the organization. Currently, the main focus of the data warehouse research field is o...

Traditional enterprise data warehouse, an integral part of decision support systems (DSS), provides a variety of granularity data to satisfy requirements of different users. It is estimated that about 80% of the information stored in data warehouse is geo-spatial related. However, traditional data warehouse cannot efficiently process spatial data. With the increasing amount of spatial data stor...

abstractin this study, a vendor-managed inventory model is developed for a single-vendor multiple-retailer single-warehouse (sv-mr-sv) supply chain problem based on the economic order quantity in which demands are stochastic and follow a uniform probability distribution. in order to reduce holding costs and to help balanced on-hand inventory cost between the vendor and the retailers, it is assu...