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BI Refactoring - Maximizing Reporting Performance with Azure Tabular Models

BI Refactoring – Maximizing Reporting Performance with Azure Tabular Models

Organizations can improve their reporting performance by using Azure Tabular Models instead of traditional SQL multidimensional models. Tabular models offer faster query response times, scalability, and cost-effectiveness. To convert to a tabular model, businesses can analyze their existing cube, create a new model, and deploy it on Azure Analysis Services. Tabular models provide more efficient decision-making based on data, making it a valuable tool for businesses.

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4 Ways the Shipping Industry is Revolutionizing Its Operations

4 Ways the Shipping Industry is Revolutionizing Its Operations

The shipping industry faces the challenge of coordinating a complex web of interconnected systems, resulting in a lack of transparency and visibility in the supply chain. Discover how digital technology and analytics have transformed the industry by providing real-time tracking, optimizing operations, reducing costs, and improving customer satisfaction.

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Harnessing the Power of Artificial Intelligence to Improve Patient Flow in Hospitals

Harnessing the Power of Artificial Intelligence to Improve Patient Flow in Hospitals

Hospitals around the world face major challenges in managing high patient flow in critical departments. Especially during unexpected surges in patient admissions, healthcare institutions struggle to use the limited resources effectively. However, predictive data modeling is emerging as a powerful tool for hospitals to accurately forecast demand and allocate resources more effectively. It can also help hospitals achieve better patient outcomes, reduce costs, and create a more efficient healthcare system. 

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Machine Learning & Deep Learning Techniques for Anomaly Detection in Medical Imaging

Machine Learning & Deep Learning Techniques for Anomaly Detection in Medical Imaging

Medical imaging, such as CT, MRI, and X-rays, has grown in importance in recent decades. Human experts such as radiologists and physicians primarily interpret medical images in clinics. Researchers and clinicians have begun to benefit from computer-assisted interventions in the analysis of medical images with the help of machine learning techniques.

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