Data Warehouses (DW) manage enterprise information that is queried for decision making purposes by using On-Line Analytical Processing (OLAP) tools. The establishment of security constraints in all development stages and operations of the DW is highly important since otherwise, unauthorized users may discover vital business information. The final users of OLAP tools access and analyze the infor...

BACKGROUND Data analysis in community health assessment (CHA) involves the collection, integration, and analysis of large numerical and spatial data sets in order to identify health priorities. Geographic Information Systems (GIS) enable for management and analysis using spatial data, but have limitations in performing analysis of numerical data because of its traditional database architecture....

The integration among DW, OLAP and GIS has been given considerable attention in recent years by many researchers and industrial corporations. This may be a result of: 1) DW/OLAP can improve GIS spatial queries whereas, 2) a GIS can provide better support to deal with the DW/OLAP geographic data. Some research about this integration has already been done. However, these approaches do not deal wi...

Data Warehouses (DWs) are large repositories of data aimed at supporting the decision-making process by enabling flexible and interactive analyses via OLAP systems. Rapid prototyping of DWs is necessary when OLAP applications are complex. Some work about the integration of Data Mining and OLAP systems has been done to enhance OLAP operators with mined indicators, and/or to define the DW schema....

During the last few years, On-Line Analytical Processing (OLAP) has emerged as a valuable tool for the analysis, navigation and reporting of hierarchically organized data from data warehouses. Still, it remains a challenging task to implement and deploy OLAP tools over large databases, since no standardized architecture exists, which describes the common components and functionality of OLAP too...

On-Line Analytical Processing (OLAP) is a trend in database technology, based on the multidimensional view of data. Multidimensional data (called multicubes) form the basic logical data model for OLAP applications and a significant number of proposals exist for cube models, without – in most cases – the distinct description of an equivalent “presentational” layer or model for the presentation o...

Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP) can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with...

On-line analytical processing (OLAP) has gained its popularity in database industry. With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and on-line analytical processing of spatial data. In this paper, we study methods for spatial OL...

In this paper, we prompt a new big data query online analysis and processing method based on decision tree model. The purpose of this dissertation is to realize the high-efficiency of multi-dimensional OLAP query by studying the key approaches for improving the OLAP query efficiency. In order to bring the full play of data analysis, the concept of on-line analytical mining (OLAM) is used for re...