A Horizontal Fragmentation Algorithm for the Fact Relation in a Distributed Data Warehouse

Data warehousing is one of the major research topics of appliedside database investigators. Most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data are based. Unfortunately, this approach is very expensive and tends to ignore the advantages realized during the past decade in the area of distribution and support for data localization in a geographically dispersed corporate structure. This research investigates building distributed data warehouses with particular emphasis placed on distribution design for the data warehouse environment. The article provides an architectural model for a distributed data warehouse, the formal definition of the relational data model for data warehouse and a methodology for distributed data warehouse design along with a "horizontal" fragmentation algorithm for the fact relation. Most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data is based. The centralized data warehouse is very expensive and tends to ignore the advantages realized during the past decade in the areas of distribution and support for data localization in a geographically dispersed corporate structure. Further, it would be unwise to enforce a centralized data warehouse when the operational systems exist over a widely distributed geographical area. The distributed data warehouse supports the decision makers by providing a single view of data even though that data are physically distributed across multiple data warehouses in multiple systems at different branches. Currently, the field of distributed data warehouse in terms of architecture and design is considered an important research problem that needs investigation. This research contributes to the problem of distributed data warehouse architecture and design by: Keywords distributed data warehouse architecture, distributed data warehouse design, horizontal fragmentation. Extending the preliminary architecture model that has been presented in [8] by proposing a distributed data warehouse system architecture and describing the functionality of its components

Encryption Quality Analysis of the RCBC Block Cipher Compared with RC6 and RC5 Algorithms

In this paper, we investigate the encryption quality of the robust chaotic block cipher (RCBC) algorithm; which is based on chaotic map. In addition to visual inspection of images encryption testing, five analytical metrics are developed for analyzing the encryption quality. These metrics are used to evaluate several encrypted images factors include: maximum deviation, irregular deviation, information entropy, correlation coefficients, and avalanche effect. Comparison of the encryption quality for RCBC, RC6 and RC5 implantations to digital images are performed. In the experimental results, we have made our tests using color images Lena, Cman, and Peppers, each of size 512x512 pixels, as the original images (plain-images). Results show better quality of the RCBC

WMSS: A Web-Based Multitiered Surveillance System for Predicting CLABSI

Central-line-associated bloodstream infection (CLABSI) rates are a key quality metric for comparing hospital quality and safety. Manual surveillance systems for CLABSIs are time-consuming and often limited to intensive care units (ICUs). A computer-automated method of CLABSI detection can improve the validity of surveillance. A new web-based, multitiered surveillance system for predicting and reducing CLABSI is proposed. The system has the capability to collect patient-related data from hospital databases and hence predict the patient infection automatically based on knowledge discovery rules and CLABSI decision standard algorithms. In addition, the system has a built-in simulator for generating patients’ data records, when needed, offering the capability to train nurses and medical staff for enhancing their qualifications. Applying the proposed system, both CLABSI rates and patient treatment costs can be reduced significantly. The system has many benefits, among which there is the following: it is a web-based system that can collect real patients’ data from many IT resources using iPhone, iPad, laptops, Internet, scanners, and hospital databases. These facilities help to collect patients’ actual data quickly and safely in electronic format and hence predict CLABSI efficiently. Automation of the patients’ data diagnosis process helps in reducing CLABSI detection times. The system is multimedia-based; it uses text, colors, and graphics to enhance patient healthcare report generation and charts. It helps healthcare decision makers to review and approve policies and surveillance plans to reduce and prevent CLABSI