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Intl Symposium on Big Data, Deep Learning & Advanced Predictive Analytics

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Drexel University

3141 Chestnut Street

Philadelphia, PA 19104

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You are invited to the 5th International Symposium on Big Data, Deep Learning and Advanced Predictive Analytics that takes place 5 November 2017 aty Drexel University, PA, USA. This event is sponsored by Data Analytica.

Presentation: AI Robotics – SLAM Status Quo and Future Directions

Speaker: Dominique Heger, PhD, is the Founder & CEO DHTechnologies, / Data Analytica, Texas, USA

Simultaneous Localization and Mapping (SLAM) consists of the concurrent construction of a model of the environment (labeled as the map) and the estimation of the state of the robot moving within it. The past decade has seen major SLAM advancements and new SLAM algorithms. The bulk of work has focused on improving the computational efficiency while ensuring consistent and accurate estimates for the map and the vehicle pose. Further, some research emphasis has been on issues such as non-linearity, data association and landmark characterization, all of which are vital to move a practical and vigorous SLAM implementation forward. It is safe to state that we are at a point with SLAM where we can enable large-scale real-world SLAM applications and witness a steady transition of the technology into the main-stream industry. The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle very large numbers of landmarks that may be present in real life scenarios. Kalman filter-based algorithms (as an example) require time that is quadratic to the number of landmarks to incorporate each sensor observation and hence scalability is a concern. In this talk, we analyze the current state of SLAM and consider future directions. By considering the status quo, we describe open research challenges and new research opportunities that deserve watchful scientific investigation. One of the topics discussed in the talk revolves around the impact that deep learning will or should have on SLAM. Actual demonstrations will support the discussion.

Presentation Plus LIVE DEMO: Management of The Analytic Lifecycle for Big Data

Speaker: Alain Biem, PhD,is a Data scientist and former vice president of Analytics and chief scientist of advanced solutions delivery at Opera Solutions

The Analytic Lifecycle involves building, deploying, and maintaining a variety of analytic models, on a variety of computing platforms, for a variety of tasks. The Management of the Analytic Lifecycle for Big Data, at rest or in motion, is a challenging endeavor requiring the delicate utilization and leveraging of various Big Data platforms and software assets, as data evolve. In this presentation, we describe the management of Big Data Analytics lifecycle as an essential part of the data lifecycle and as a pre-requisite in all Big Data viable solutions. We will use the IBM Big Data Platform, which is a stack of software assets, to illustrate specific solutions to issues related analytic lifecycle management.

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Drexel University

3141 Chestnut Street

Philadelphia, PA 19104

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