بایگانی برچسب برای: Data warehouse

Design and implementation of data warehouse.[taliem.ir]

Design and Implementation of Data Warehouse with Data Model using Survey-based Services Data

Various business organization or government bodies are enhancing their decision making capabilities using data warehouse. For government bodies, data warehouse provides a means by enabling policy making to be formulated much easier based on available data such as survey-based services data. In this paper we present a survey-based service data with the design and implementation of a Data Warehouse framework for data mining and business intelligence reporting. In the design of the data warehouse, we developed a multidimensional Data Model for the creation of multiple data marts and design of an ETL process for populating the data marts from the data source. The development of multiple data marts will enable easier report generation by identifying common dimension amongst the data marts. The cross-join capabilities of the data marts through common dimensions, demonstrate the ability to easily drill across the data marts for cross data analysis and reporting. In addition, we also have incorporate data quality checking on the data source as well as data detection rules to filter out unmatched data schema and data range from being stored in the data warehouse for analysis.
An automatic method of data warehouses.[taliem.ir]

An Automatic Method of Data Warehouses Multidimension Modeling for Distributed Information Systems

Nowadays many companies built enterprise level data warehouses (DW) for decision making support. However explosive data accumulated in distributed databases in company or across companies with the widely use of Computer Supported Cooperative Work in Design (CSCWD) technologies. Therefore it becomes a time costing task for engineers to construct the multidimensional model of data warehouse. This research presents an ontology approach to eliminate data source heterogeneity aiming to design the conceptual structure of data warehouse automatically. The proposed approach includes a domain ontology mete-data model, which consists of data, concept, ontology and resource repositories, to describe the semantic meaning of the data sources. The supplydriven and demand-driven methodologies are combined together to construct the concept model of DW. By supply-driven method, domain ontology system is extracted bottom-up from data sources; on the other hand, by demand-driven method, relationships of the concepts in the ontology system are extended top-down according to the business process. Furthermore, candidate of the data warehouse concept model is derived according to the relationships of the concepts in the ontology system. After discussing the process of data warehouse designing, a case study is given to show how our method is used in clinic domain. The result shows that ontology method could help users designing data warehouse more easily.