Leveraging Big Data to Improve Traffic Incident Management NCHRP 2017 75

As the era of “Big Data” is upon us, there is an overwhelming number of data sources to draw from to improve traffic incident management (TIM). Big Data is not just “a lot of data,” it is a fundamental change in how to collect, analyze, and use data to uncover trends and relationships. In general, advances in technology have significantly increased data quantity, improved data quality, and enhanced data analytics. There is an urgent need for transportation agencies to become fluent in these technologies and to successfully integrate them within their operations so that they may benefit from the large set of existing and emerging Big Data data sources and tools. Big Data holds the potential for leveraging the greatest return on investment in traffic incident management and associated public safety outcomes.
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Using MySQL for Distributed Database Architectures


In modern data architectures, we’re increasingly moving from single node design systems to distributed architectures using multiple nodes often spread across multiple databases and multiple continents. Such architectures bring many benefits (such as scalability and resiliency), but can also bring a lot of pain if incorrectly architected.
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Rare Disease Patient Cohort Identification Using AI/ML Models and Federated EHR-based Real-world Data Infrastructure

As rare diseases are so rare, patients are often misdiagnosed for many years, or never correctly diagnosed. Electronic health records hold much important information that can be used to correctly diagnose and treat these patients, but identification of phenotypic sets is hard to extract from reams of data.
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Journey to the Cloud, Self-Service BI, and Data Lakes with Data Virtualization


Denodo DataFest, the premier agile data management and analytics conference, returned once again to New York and London in 2018. Our annual user conference brings together industry analysts, subject matter experts, business leaders, data management leaders to discuss data strategies to enable a successful journey to the cloud, self-service BI, and data lakes with data virtualization. The event invited attendees to watch demos, hear customer success stories, and educate themselves on the best practice implementations of data virtualization.
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Building a Modern Operational Data Warehouse


With data coming from so many different sources nowadays (both old and new, both internal and external), it is inevitable that data will arrive in many different structures, schema, and formats, with other variables for latency, concurrency, and requirements for storage and processing. When data types are extremely diverse and combined, we now call it “hybrid data.” This usually drives users to deploy many types of databases and different platforms to capture, store, process, and analyze the data, which in turn results in hybrid data management architectures.
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