Natalino is Head of Data Science at Teradata, where he provides consultancy services and delivers big/fast data solutions for data-driven applications such as predictive analytics, personalized marketing, man-machine interaction, fraud and cyber security. O'Reilly author and advocate of Open Source Software, and the Apache Software Foundation. Polyglot programmer and passionate about distributed processing projects such as Spark, Flink, Cassandra, Kafka, Akka. Previously, served as Enterprise Data Architect at ING in the Netherlands. Before that, he has served as senior researcher at Philips Research Laboratories in the Netherlands, on the topics of system-on-a-chip architectures, distributed computing and compilers.
All-round Technology Manager, Product Developer, and Innovator with 15+ years track record in research, development and management of distributed architectures and scalable services and applications. Blogs regularly about big data, analytics, data science and scala reactive programming at natalinobusa.com
Cities are vibrant and rich places where plenty of interactions happen every day. Since the digitalization of many services, digital traces are produced by companies and citizens as the carry their activities in the city. This substrate of data is ideal for advanced analytics where locations and activities are transformed into recommendations, alerts, and suggestion. The advantages of data-driven analysis are vast. Think for instance of queue and crowd management for public events, predictive reservation systems for services such as governmental and city services.
Think of parking and traffic analysis and predictive maintenance to minimize inconveniences to citizens. Think of better and more detailed analysis of people gathering and transportation hotspots and a better prevention of incidents. For all this services, understanding spatial and temporal geolocated event is crucial. Thanks to the latest advances in data science at scale and big data technologies, we have now the technologies and the tools to support this analysis for smarter services and an efficient planning of city events, resources and services. Join this lecture on data-driven driven services for smart data cities! For further information please have a look at the following: http://www.oreilly.com/live-training/geo-located-data.html