June 20 - 22 - Tokyo, Japan
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AI & Deep Learning [clear filter]
Wednesday, June 20


Getting Insights from IoT Data with Apache Spark and Apache Bahir - Luciano Resende, IBM & Adriano Arantes, Hitachi
The Internet of Things (IoT) is all about connected devices that produce and exchange data, and producing insights from these high volumes of data is challenging. On this session, we will start by providing a quick introduction to the MQTT protocol, and focus on using AI and machine learning techniques to provide insights from data collected from IoT devices. We will present some common AI concepts and techniques used by the industry to deploy state-of-the-art smart IoT systems. These techniques allow systems to determined patterns from the data, predict and prevent failures as well as suggest actions that can be used to minimize or avoid IoT device breakdowns on an intelligent way beyond rule-based and database search approaches. We will finish with a demo that puts together all the techniques discussed in an application that uses Apache Spark and Apache Bahir support for MQTT.

avatar for Adriano S. Arantes

Adriano S. Arantes

Senior Research Scientist, Hitachi
Over 12 years of experience in database management systems involving core database implementation, database design, indexing methods, query processing and optimization, data quality and customer data integration.Over 5 years of experience working on bioinformatics developing tools... Read More →
avatar for Luciano Resende

Luciano Resende

Data Science Platform Architect, IBM
Luciano Resende is a Data Science Platform Architect at IBM Spark Technology Center. He has been contributing to open source at The ASF for over 10 years, he is a member of ASF and is currently contributing to various big data related Apache projects around the Apache Spark ecosystem... Read More →

Wednesday June 20, 2018 14:20 - 15:00
Private Dining


Akraino: A Technical Overview - Shane Wang, Intel
In the past decade, there has been a concerted effort among companies to move infrastructure to centralized clouds, enabled by virtualization. With the rise of IoT and explosion of device-generated data, the pendulum has begun to swing the other way, placing resources for analytics and specialized functions near the user end of the network.  IoT and 5G use cases demand performance determinism of computing, high network throughput with low latency and highly available services at the edge. The reach of these edge nodes will expand over the coming years as carriers update to 5G, amplifying the need for hardened infrastructure services. The Akraino Edge Stack aims to address this model of computing. 

Attend this session for an overview of the Akraino Edge Stack’s architecture, project organization, governance, and roadmap. You will also gain an understanding of how AkrainoEdge Stack interoperates with other community projects sponsored by the Linux Foundation and OpenStack Foundation.

avatar for Shane Wang

Shane Wang

Engineering Manager, Intel
Shane Wang is an engineering manager for networking and storage at Intel's System Software Products. He has participated in or led his team on research and development of open source software projects such as Xen, tboot, Yocto and OpenStack. Since 2015, he has served as an individual... Read More →

Wednesday June 20, 2018 15:10 - 15:50
Private Dining


Industrialize Data Science and Machine Learning - Amine Slimane, Talend
The pace of business disruption is accelerating, meaning today’s organizations need to become more data agile in order to compete and innovate. It’s no secret that big data initiatives are becoming more pervasive. But in order to process this vast amount of data, companies need data science and machine learning to find valuable insights. As they move to build smart applications powered by big data and IoT, new challenges are arising including how to move data science and machine learning into production. Oftentimes it is a laborious, manual-coding process that can take up weeks or months.

In this session we’ll discuss how you can:
• Pre-process data in order to train data models
• Operationalize data science and advanced analytics
• Embed machine learning into real-time big data projects to accelerate time-to-insight


Wednesday June 20, 2018 17:10 - 17:50
Private Dining