Mathematics to converge IoT, Cloud and Big Data @ Hadoop Summit, 2016

This is a past event

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There will be networking session for members of all the meetups from 5-6pm, an hour before they start, and drinks will be provided.

6:00 P.M. - 6:30 P.M. Introduction and Networking

6:30 P.M. - 7:10 P.M. Session 1

Title: Mathematical Bridges Between Old and New

Abstract: The computing world seems lately to be all a quiver about the novelty deep learning models and how they seem so mysterious. In fact, the basic ideas behind these systems are very closely related to commonly known algorithms like k-means clustering.

I will present a simple example of an anomaly detector built using k-means clustering and show how it provides a insight into how much more advanced models such as neural networks and recurrent networks.Best regards,

Speaker: Ted Dunning - Chief Application Architect, MapR

Speaker Bio: Ted Dunning is Chief Application Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects . Ted has been very active in mentoring new Apache projects and is currently serving as vice president of incubation for the Apache Software Foundation . Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems. He built fraud detection systems for ID Analytics (LifeLock) and he has 24 patents issued to date and a dozen pending. Ted has a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting.


7:10 P.M. - 7:15 P.M. Q/A

7:15 P.M. - 7:55 P.M. Session 2

Title: Mathematical Model to unify IoT, Big Data and Artificial Intelligence in the Cloud

Abstract: The insights hidden in the vast and growing oceans of data available from IoTs are extremely valuable but current approaches don’t scale to IoT volumes. The future realization of IoT’s promise is dependent on machine learning to find the patterns and correlations to be stored and made available as “AI in the cloud”. The companies will be able to infuse their own services with such intelligence available in the cloud that can improve almost every aspect of our daily lives. Machine learning generally uses models based on statistics. However, derivatives in calculus provide valuable rate of change information over volumes of data leading to the use of anti-derivatives for stronger predictions. The purpose of this presentation is to implement a model iterating over a sequence of computing stages based on Calculus (CAL), Statistics (STAT) and database normalization (DN) in order to (a) seamlessly combine “AI in the cloud”, (b) perform joins over information components and (c) reduce overall processing time with enhanced power of predictions. An example implementation of the model using Spark and Mathematica will be presented.

Speakers: Dr. S. Sarkar, Organizer of Big Data Science Meetup and A. Sarkar, Software Engineer, AyushNet

Speaker Bios:

7:55 P.M. - 8:00 P.M. Q/A

Sponsored by:

O'Reilly Media (