Grid computing is moving into two ways. The Computational Grid focuses on reducing execution time of applications that require a great number of computer processing cycles. The Data Grid provides the way to solve large scale data management problems. Data intensive applications such as High Energy Physics and Bioinformatics require both Computational and Data Grid features. Job scheduling in Grid has been mostly discussed from the perspective of computational Grid. However, scheduling on Data Grid is just a recent focus of Grid computing activities. In Data Grid environment, effective scheduling mechanism considering both computational and data storage resources must be provided for large scale data intensive applications. In this paper, we describe new scheduling model that considers both amount of computational resources and data availability in Data Grid environment. We implemented a scheduler, called Chameleon, based on the proposed application scheduling model. Chameleon shows performance improvements in data intensive applications that require both large number of processors and data replication mechanisms. The results achieved from Chameleon are presented .

Data quality and Data Source Management is one of the key success factors for data warehouse project. Many data warehouse projects fail due to poor quality of the data. It is believed that the problems can be fixed later and because of that, a lot of time will be spent to fix the error. If low quality data fed into the data warehouse system, the result will be not accurate if these data are used in the decision making. Many data warehouse and business intelligence projects failure are due to wrong or low quality data. Therefore this paper will underpins several aspects such as Total Data Quality Management (TDQM), ISO 9001:2008 and Quality Management System (QMS) in order to address data quality problems in the early stage and find out the best procedure to manage the data sources. To find a standard procedure in managing data source base on ISO 9001:2008 standard, process in managing data source is identified and a compared to the ISO 9001:2008 Quality Management System (QMS) requirements. As a result, this process is viewed as a kind of production process and relate to the concepts of quality management known from the manufacturing and service domain. More precisely, a high quality management system in managing data source is proposed. This system is based on ISO 9001:2008 standard and hopes it can help organizations in implementing and operating quality management system. By using ISO 9001:2008 framework to the process of managing data source, this approach will be similar to the manufacturing concept that hasan added advantage when compared to traditional approaches in managing data source.

Many organizations look for a proper way to make better and faster decisions about their businesses. Data warehouse has unique features such as data mining and ad hoc querying on data collected and integrated from many of the computerized systems used in organization. Data warehouse can be built using a number of architectures. Each one of the architecture has its own advantages and disadvantages. Thispaper investigates five different data warehouse architectures: centralized data warehouse, independent data mart, dependent data mart, homogeneous distributed data warehouse andheterogeneous distributed data warehouse and subsequently an optimal plan will be described and then all data warehouse architectures mentioned above will be parametrically measured by this method. Finally as case study, a ompany’s problem in their data accessing system is studied and the best architecture is chosen to accommodate the needs of the company’s system.