Virtual Data Mart for Measuring Organizational Achievement Using Data Virtualization Technique (KPIVDM)

Critical Success Factors for Data Virtualization: A Literature Review

Data Virtualization (DV) has become an important method to store and handle data cost-efficiently. However, it is unclear what kind of data and when data should be virtualized or not. We applied a design science approach in the first stage to get a state of the art of DV regarding data integration and to present a concept matrix. We extend the knowledge base with a systematic literature review ...

Virtualization and Virtual Data Management for Field Data Sensing

High-data-rate sensing and processing in the field are important for emerging applications in intelligent transportation, surveillance, and environmental management. For these systems, virtualization and virtual data management bring the benefits of easy deployment, flexible control, and strong data containment. Despite limited resources in the field, we show that optimized virtualization suppo...

Data Mart A Data Warehouse Data Mart B Data

EEcient query processing is a critical requirement for data warehousing systems as decision support applications often require minimum response times to answer complex, ad-hoc queries having aggrega-tions, multi-ways joins over vast repositories of data. This can be achieved by fragmenting warehouse data. The data fragmentation concept in the context of distributed databases aims to reduce quer...

pattern recognition in maintenance data using methodologies data minitng (cade study isfahan regional power electric company)

فعالیت های نگهداری و تعمیرات اطلاعاتی را تولید می کند که می تواند در تعیین زمان های بیکاری و ارایه یک برنامه زمان بندی شده یا تعیین هشدارهای خرابی به پرسنل نگهداری و تعمیرات کمک کند. وقتی که مقدار داده های تولید شده زیاد باشند، فهم بین متغیرها بسیار مشکل می شوند. این پایان نامه به کاربردی از داده کاوی برای کاوش پایگاه های داده چندبعدی در حوزه نگهداری و تعمیرات، برای پیدا کردن خرابی هایی که موجب...

Measuring Interval Efficiency in Convex Hull of Data Using DEA