Tag: big data

The Association of Public Health Laboratories (APHL) works with federal, state, and local agencies across the US to shape national and global health outcomes by strengthening public laboratory systems and building effective laboratory response in public health emergencies. To support the swift exchange of health information across public health laboratories, governments, and organizations, APHL teamed up with Ruvos, a consulting firm and AWS Partner that specializes in health informatics and information technology, to create a secure messaging platform on AWS.

Learn how to store historical geospatial data, such as weather data, on AWS using Amazon DynamoDB. This approach allows for virtually unlimited amounts of data storage combined with query performance fast enough to support an interactive UI. This approach can also filter by date or by location, and enables time- and cost- efficient querying.

There is power in data. Professors Monica Chiarini Tremblay and Rajiv Kohli at William & Mary’s Raymond A. Mason School of Business detail how Carlos Rivero, the former chief data officer of the Commonwealth of Virginia, created a foundation for data sharing in Virginia powered by multiple AWS solutions.

In the public sector, civic leaders must make sure that a wide variety of citizen services are delivered efficiently, responsibly, and successfully. To identify high performing operations and support those in need of improvement, decision-makers need real-time data in formats that are simple to interpret and understand. In my 25 years of experience serving the public sector, I’ve found that there are four foundational considerations for implementing a comprehensive public sector data analytics and performance management strategy.

For higher education institutions, there is a growing focus on the importance of improving the student experience, which touches on virtually everything colleges and universities do. And how can higher education institutions improve the student experience? Data. Learn how higher education institutions can use data best practices to turn their data into insight.

In this blog post, learn a high-level architecture, built on AWS, that uses a graph database to analyze unstructured and structured educational data that can, for example, help inform a recommendation to a student for the appropriate courses to take in their next semester based on multiple personalized data factors.

Airports receive data from dozens of disparate sources, but this data can lack the collective unity needed to see the bigger picture. The Cincinnati Northern Kentucky International Airport (CVG) saw an opportunity to turn down the proverbial volume on their data. Using AWS, CVG created a first-of-its-kind tool using data for predictive analytics and proactive notifications, now known as Enterprise Awareness & Situational Exceptions (EASE).

Cybersecurity analytics is a systematic methodology designed to collect, ingest, process, aggregate, and analyze security events. This methodology empowers organizations to proactively perform security investigations, powered by advanced analytics and machine learning (ML), which help mitigate cyber issues more effectively and efficiently at scale. Learn about the core components of a cybersecurity analytics framework and how organizations can use AWS to design a cybersecurity analytics platform with analytics and ML services.

King Hamad University Hospital (KHUH) and Bahrain Oncology Center is a 600-bed-hospital in Bahrain. Over the years, KHUH faced constraints with exponential growth of their on-premise storage needs, particularly with the medical images stored by their picture archiving and communication system (PACS). KHUH turned to AWS to develop a cost- and time-effective long-term storage solution, without making changes to their existing PACS, that reduced storage costs by 40%.

The healthcare technology (HealthTech) startup contextflow uses AWS to power its clinical decision support system that can help radiologists diagnose patients faster and with more accuracy. The software can search, retrieve, and compare historical diagnostic data to help guide radiologists in writing reports. It is currently being utilized to analyze lung computed tomography (CT) scans for interstitial lung diseases (ILD), chronic obstructive pulmonary disease (COPD), and lung cancer screenings.