In conjunction with

IEEE International Conference on Big Data 2019

Building upon prior workshops, with support from

About STREAM-ML

Applications associated with streaming data and related real-time machine learning, steering and control are of growing interest and importance. The analysis of data streaming from on-line instruments, large scale simulations, and Internet of Things (IoT), an enormous amount of distributed sensors now enables near real-time steering and control of complex systems such as scientific experiments, transportation systems, and urban environments. STREAM-ML has the potential to provide more timely access to information and raise the quality and pace of decision making and, consequently, performance. There has been an explosion of new research and technologies for stream analytics arising from the academic and private sectors. We need to research steering scenarios involving active learning. Steering, which is inevitably real-time, might include realigning experimental sensors, control of autonomous vehicle or changing progress of simulations.

Advances in ML techniques, such as Deep Learning and neural networks, are the main enablers of knowledge work automation. Natural user interfaces, such as speech and image recognition are also highly benefiting from ML technologies. Compared to the economic impact of machine learning, next frontier is knowledge work automation that asserts the more attention toward the extraction of value out of data.

Location

Los Angeles, California

 

Important Dates

Oct 31, 2019: Abstracts due

Oct 31, 2019: Full Paper Submission due

Nov 10, 2019: Author Notification

Nov 20, 2019: Camera Ready Paper due

Dec 10, 2019: Workshop

Program Schedule

Tuesday, December 10, 2019
Room: Echo Park
Westin Bonaventure Hotel & Suites Located at 404 South Figueroa Street, Los Angeles, CA


Time        


Title


Presenter*/Authors list        


Affiliations  


Links         

4:20PM 

Welcome

Judy Qiu, Geoffrey Fox, Madhav Marathe

4:20PM
Performance Characterization and Modeling of
Serverless and HPC Streaming Applications

Andre Luckow*,
Shantenu Jha
BMW Group, Germany
Rutgers University, USA
slides
4:40PM Streaming Machine Learning Algorithms with Big
Data Systems

Vibhatha Abeykoon*,
Supun Kamburugamuve,
Kannan Govindrarajan,
Pulasthi Wickramasinghe,
Chathura Widanage,
Niranda Perera,
Ahmet Uyar,
Gurhan Gunduz,
Selahattin Akkas,
Gregor Von Laszewski

Indiana University, USA slides
5:00PM Benchmarking Deep Learning for Time Series:
Challenges and Directions

Xinyuan Huang*,
Geoffrey Fox,
Sergey Serebryakov,
Ankur Mohan,
Pawel Morkisz,
Debojyoti Dutta

Cisco Systems, USA
Indiana University, USA
Hewlett Packard Enterprise, USA
In-Q-Tel, USA
AGH University of Science and Technology, Poland
slides
5:20PM A Fast Video Image Detection using TensorFlow
Mobile Networks for Racing Cars

Selahattin Akkas*,
,Sahaj Singh Maini
Judy Qiu

Indiana University, USA slides
5:40PM MATRICS: A System for Human-Machine Hybrid
Forecasting of Geopolitical Events

David Huber,
Nigel Stepp,
Aruna Jammalamadaka,
Tiffany Kim,
Sam Johnson,
Dana Warmsley,
Tsai-Ching Lu*

HRL Laboratories, USA slides
6:00PM
DeepLite: Real-Time Deep Learning Framework for
Neighborhood Analysis

Duy Ho,
Raj Marri*,
Sirisha Rella
Yugyung Lee
University of Missouri - Kansas City, USA slides
6:20PM Adaptive Hoeffding Tree with Transfer Learning for
Streaming Synchrophasor Data Sets

Zakaria El Mrabet*,
Daisy Flora Selvaraj,
Prakash Ranganathan

University of North Dakota, USA slides
6:40PM

Discussions and Closing Remarks

Workshop Topics

  • Real-Time ML Applications for social media, sports, healthcare, financial transactions
  • Real-Time ML Applications for IoT, Cyberphysical Systems, Satellite and airborne monitors
  • Real-Time ML Applications for instruments like the LHC, Sequencers, Data Assimilation
  • Real-Time ML Applications for Astronomy, Light Sources, climate and Agriculture
  • Streaming ML and Analysis of Simulation Results
  • Data fusion and reduction for IoT devices
  • Distributed machine learning in a cloud environment
  • Streaming batch and online learning algorithms (CNN, RNN, LSTM, GAN, Neuromorphic)
  • Real-Time Training and inference
  • Distributed Machine Learning
  • Approximation algorithms for IoT devices
  • Programming and Runtime Model
  • Streaming Software Systems and Algorithm Library
  • Streaming Infrastructure on major commercial clouds (CPU, GPU, FPGA, TPU)
  • Streaming ML on Edge Devices
  • Streaming ML on IoT Devices
  • Steering and Human in the Loop
  • Real-Time Steering and Control
  • Metrics of performance and benchmarks
  • Security for Real-Time ML

Call for Papers

Members of the community are invited to submit a paper (6 pages for a full paper or 2 pages for an extended abstract) in areas of relevance to STREAM-ML's scope and objectives. The paper format is a two-column IEEE Conference Style. Papers must be electronically submitted using the submission system accessible on CyberChair.

All papers accepted will be included in the IEEE Big Data Conference Proceedings published by the IEEE Computer Society Press. At least one author of each accepted paper must register for the conference and present the paper at the workshop for the paper to be included in the conference proceedings. Details on the registration will be posted on the main conference's page.

Stay Updated!

We have a few things that we will be announcing in the coming weeks and months. Stay updated on program details, dates, events and people.

Workshop Chairs

Judy Qiu

Indiana University

Geoffrey Fox

Indiana University

Madhav Marathe

University of Virginia


Program Committee

Gabriel Antoniu

Inria de Rennes, France

Richard Carlson

U.S. Department of Energy, USA

Debojyoti Dutta

Cisco, USA

Xinyuan Huang

Cisco, USA

Shantenu Jha

Rutgers University, USA

Sangmi Pallickara

Colorado State University, USA

Shrideep Pallickara

Colorado State University, USA

Glenn Ricart

US Ignite, USA

Takuya Araki

NEC, Japan

Bob Hwang

Transportation Energy Center Director, Sandia National Lab, USA

Sergey Serebryakov

Cruise AI

Andrey Nikolaev

Intel Corporation