Traditionally, data warehouses have been used to analyze historical data. Recently, there has been a growing trend to use data warehouses to support real-time decision-making about an enterprise's day-to-day operations. The needs for improved query and update performance are two challenges that arise from this new application of a data warehouse. To address these needs, new data warehouse funct...

Often in a competitive market, corporations need tools to increase the accuracy and efficiency of the decisions. Facing this, several companies that have no resources to buy the commercial systems available because of the high costs, have no chance to buy a Decision Support Systems (DSS) platform. This paper is dedicated to a benchmark of PostgreSQL DBMS for data warehouse as part of a low cost...

A data warehouse consists of a set of materialized views defined over a number of data source, collects copies of data from remote, distributed, autonomous and heterogeneous data sources into a central repository to enable analysis and mining of the integrated information. Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingl...

The topic of data warehousing encompasses architec-tures, algorithms, and tools for bringing together selected data from multiple databases or other information sources into a single repository, called a data warehouse , suitable for direct querying or analysis. In recent years data warehousing has become a prominent buz-zword in the database industry, but attention from the database research c...

The topic of data warehousing encompasses architectures, algorithms, and tools for bringing together selected data from multiple databases or other information sources into a single repository, called a data warehouse, suitable for direct querying or analysis. In recent years data warehousing has become a prominent buzzword in the database industry, but attention from the database research comm...

Building a data management model for scientific applications poses challenges not normally encountered in commercial database development. Complex relationships and data types, evolving schema, and large data volumes (in terabyte) are some commonly cited challenges with scientific data. In this thesis, we propose a data warehouse model to manage and analyze scientific behavioral data. Data ware...

The explosive growth in the development of Traditional Chinese Medicine (TCM) has resulted in the continued increase in clinical and research data. The lack of standardised terminology, flaws in data quality planning and management of TCM informatics are preventing clinical decision-making, drug discovery and education. This paper argues that the introduction of data warehousing technologies to...

A data warehousing system collects data from multiple distributed sources and stores the inte grated information as materialized views in a local data warehouse Users then perform data analysis and mining on the warehouse views Figure 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 usefu...

The usage of location aware devices, such as mobile phones and GPS-enabled devices, is widely spread nowadays, allowing access to large spatiotemporal datasets. The space-time nature of this kind of data results in the generation of huge amounts of mobility data and imposes new challenges regarding the analytical tools that can be used for transforming raw data to knowledge. In our research, we...

Data warehousing is a booming industry with many inter esting research problems The database research community has concen trated on only a few aspects In this paper We summarize the state of the art suggest architectural extensions and identify research prob lems in the areas of warehouse modeling and design data cleansing and loading data refreshing and purging metadata management extensions ...