November 8, 2019

112 words 1 min read

Big data opportunities in next-generation mobility

Big data opportunities in next-generation mobility

Evangelos Simoudis explores how data generated in and around increasingly autonomous vehicles and by on-demand mobility services will enable the development of new transportation experiences and solutions for a diverse set of industries and government types.
Talk Title Big data opportunities in next-generation mobility
Speakers Evangelos Simoudis (Synapse Partners)
Conference Strata + Hadoop World
Conf Tag Big Data Expo
Location San Jose, California
Date March 14-16, 2017
URL Talk Page
Slides Talk Slides
Video

Evangelos Simoudis explores how data generated in and around increasingly autonomous vehicles and by on-demand mobility services will enable the development of new transportation experiences and solutions for a diverse set of industries and government types. Topics include:

autonomous vehicle government big data
comments powered by Disqus
Collaboration in AI benefits humanity (sponsored by Intel)

Collaboration in AI benefits humanity (sponsored by Intel)

November 8, 2019

Artificial intelligence will accelerate both cancer research and the development of autonomous vehicles. Jason Waxman explains why the ultimate potential of AI will be realized through its societal benefits and positive impact on our world. Collaboration between industry, government, and academia are required to drive this societal innovation and deliver the scale and promise of AI to everyone.
Big data, AI, the genome, and everything (sponsored by Microsoft)

Big data, AI, the genome, and everything (sponsored by Microsoft)

November 8, 2019

Vijay Narayanan takes you on an inspiring journey exploring how the cloud, data, and artificial intelligence are powering and accelerating the genomic revolutionsaving and changing lives in the process.
Compressed linear algebra in Apache SystemML

Compressed linear algebra in Apache SystemML

November 7, 2019

Many iterative machine-learning algorithms can only operate efficiently when a large matrix of training data fits in the main memory. Frederick Reiss and Arvind Surve offer an overview of compressed linear algebra, a technique for compressing training data and performing key operations in the compressed domain that lets you build models over big data with small machines.
Data monetization: A telecommunications use case

Data monetization: A telecommunications use case

November 7, 2019

Dirk Jungnickel explains how Dubai-based telco leader du leverages big data to create smart cities and enable location-based data monetization, covering business objectives and outcomes and addressing technical and analytical challenges.
Debugging Apache Spark

Debugging Apache Spark

November 7, 2019

Much of Apache Sparks power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging than on traditional distributed systems. Holden Karau and Joey Echeverria explore how to debug Apache Spark applications, the different options for logging in Sparks variety of supported languages, and some common errors and how to detect them.
Determining the economic value of your data

Determining the economic value of your data

November 7, 2019

Organizations need a model to measure how effectively they are using data and analytics. Once they know where they are and where they need to go, they then need a framework to determine the economic value of their data. William Schmarzo explores techniques for getting business users to think like a data scientist so they can assist in identifying data that makes the best performance predictors.