A soa-based e-government data integration

Data Integration presents a core issue in the Palestinian e-Government Technical Framework. The currently used data integration model relies on the Integrated Central Database which lacks quality attributes such as: interoperability and flexibility. We purpose a SOA-based approach for data integration that achieves the above attributes. We present and analyze the current architecture and implementation of the Palestinian e-Government Integrated Central Database model. We transform the current model into a SOA framework that is realized using Enterprise Service Bus (ESB) and Web Services. The proposed framework offers database replication and connectivity functionalities for the Central Database. The proposed framework is evaluated using a scenario-based software architecture evaluation method and proves that it achieves the framework goals of quality attributes: interoperability and flexibility. Moreover, a prototype of the framework is implemented and validates the framework correctness. A specific usage is presented and further proves that the framework accomplishes its functionality and quality attributes

A conceptual SOA-based framework for e-Government central database

The Central Database is one of the core components in the Palestinian e-Government technical framework. The Central Database model lacks features such as: interoperability, flexibility, and manageability. The purpose of this paper is to propose a SOA based solution for the Central Database that achieves the above features

Knowledge Discovery of Electricity Consumption and Payment Fulfillment

Gaza Strip resulted in humanitarian crisis. The two reasons behind this shortage, as stated by Gaza Electricity Distribution Company (GEDCO) are: the high rate of electricity consumption and the electricity subscribers' low rate of payment. In this paper, data mining methods are applied to seven months of electricity bills data set for Home-Type subscribers. Firstly data preparation and preprocessing is conducted; secondly, different methods of data mining are applied which are: outlier, clustering, association, and classification. The discovered patterns are interpreted to help build an association and classification model to assist overcoming electricity shortage problems. The model will help GEDCO on focusing to increase the number of bills payers and hence increase its the revenue, which will eventually result in increasing the Electricity that company can distribute to subscribers