Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

Leveraging live data to realize the smart cities vision

Arun Kejariwal (Independent), Roman Smolgovsky (MZ)
4:20pm–5:00pm Wednesday, March 7, 2018
Data engineering and architecture
 Location: Expo Hall 1
Secondary topics:  Expo Hall

Largely due to the internet of things (IoT), the volume of live data is expected to grow exponentially for the foreseeable future. One of the key application domains leveraging live data is smart cities, but success depends on the availability of generic platforms that support high throughput and ultralow latency.

Arun Kejariwal and Francois Orsini offer an overview of Satori’s live data platform. The platform can be leveraged in a very wide variety of domains and will specifically help realize the promise of smart cities along the following fronts:

  • Transportation: Fleet management, smart logistics, smart roadways, connected vehicles
  • Safety: Emergency response, pedestrian and bike safety, crime forecasting, flood detection
  • Environment: Energy efficiency, air quality, water management, smart street lighting

Arun and Francois then walk you through a country-scale case study of the platform’s implementation. Satori has partnered with the New Zealand Transportation Agency (NZTA) to deliver a mobility-as-a-service (MaaS) marketplace of smart city apps and services that reduce traffic congestion in high-growth urban areas. In collaboration with site planners, data managers, and city administrators, Satori is coalescing streaming data feeds from all transportation data sources into a single live and reactive open data channel for the entire country of New Zealand.

Satori research scientists Sandra and Dhruv helped in the R&D and in putting together the content for the slide deck.

Photo of Arun Kejariwal

Arun Kejariwal

Independent

Arun Kejariwal is an independent lead engineer. Previously, he was he was a statistical learning principal at Machine Zone (MZ), where he led a team of top-tier researchers and worked on research and development of novel techniques for install-and-click fraud detection and assessing the efficacy of TV campaigns and optimization of marketing campaigns, and his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection; and he developed and open-sourced techniques for anomaly detection and breakout detection at Twitter. His research includes the development of practical and statistically rigorous techniques and methodologies to deliver high performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been presented at international conferences and published in peer-reviewed journals.

Roman Smolgovsky

MZ