Stream 2016 was March 22-23, 2016 in Washington DC. The purpose of this meeting was to follow up on issues identified in STREAM2015 and also covered data from scientific instruments of interest to the Department of Energy.
Registration information, meeting logistics, and additional details can be found at: http://www.orau.gov/streaming2016
Data streaming from on-line instruments, large scale simulations, and distributed sensors such as those found in transportation systems and urban environments point to the growing interest and important role of streaming data and related real-time steering and control. As part of a two part workshop series, we are organizing STREAM2016 to identify application and technology communities in this area and to clarify challenges that they face.
The report of the first workshop in the series (STREAM2015) held in October 2015 can be found at: http://streamingsystems.org/finalreport.html STREAM2016 followed from STREAM2015 and focused on the features, requirements and challenges of DOE applications and the hardware and software systems needed to support them.
STREAM2016 was held March 22-23, 2016 in Washington DC. The purpose of this meeting was to follow up on issues identified in STREAM2015 and will also cover data from scientific domains of interest to the Department of Energy.
Members of the community were invited to submit a 1-2 page White Paper/Statement of Interest in areas of relevance to STREAM2016's scope and objectives including issues raised in STREAM2015. Participants were selected based upon relevance of submissions as well as strategic balance of expertise. White papers were due by February 21 and were to be sent to Sophia Pasadis (email@example.com).
STREAM2015 and STREAM2016 are made possible by support from the National Science Foundation (Division of Advanced Cyberinfrastructure), Department of Energy (Advanced Scientific Computing Research) and Air Force Office of Scientific Research (AFOSR)
Geoffrey Fox (Indiana) firstname.lastname@example.org Shantenu Jha (Rutgers) email@example.com Lavanya Ramakrishnan (LBL) firstname.lastname@example.org
|Gagan Agrawal||Can Commercial BigData Ideas Benefit Analysis of Instrument Data?||Can Commercial BigData Ideas Benefit Analysis of Instrument Data?|
|Mohsen Amini||HLSAAS: HIGH-LEVEL LIVE VIDEO STREAMING AS A SERVICE||HLSAAS: HIGH-LEVEL LIVE VIDEO STREAMING AS A SERVICE|
|Roger Barga||Moving Towards Streaming-Data Analysis|
|Jack B. Dennis||Programming Model and Architecture for Real Time Streaming||Programming Model and Architecture for Real Time Streaming|
|Chen Ding||Timescale Stream Statistics for Hierarchical Management||Timescale Stream Statistics for Hierarchical Management|
|Salman Habib||Streaming Data in Cosmology|
|Marty Humphrey||Leveraging Public Clouds for DOE Environmental Streaming Data||Leveraging Public Clouds for DOE Environmental Streaming Data|
|Geoffrey Fox, Supun Kamburugamuve||WebPlotViz: Browser Visualization of High Dimensional Streaming Data with HTML5||WebPlotViz: Browser Visualization of High Dimensional Streaming Data with HTML5|
|Dimitrios Katramatos||Streaming Data Analysis on the Wire||Streaming Data Analysis on the Wire|
|Darren J. Kerbyson||Processing Large Scale Streaming Data from High Energy Physics Workflows|
|Raj Kettimuthu||Computing and networking challenges in supporting streaming applications||Computing and Networking Challenges in Supporting Streaming Applications|
|Shweta Prabhat Khare||Distributed Reactive Stream Processing||Distributed Reactive Stream Processing|
|Eugene Kirpichov||Dataflow / Apache Beam - A Unified Model for Batch and Streaming Data Processing|
|Scott Klasky||Stream Processing for Remote Collaborative Data Analysis||Stream Processing for Remote Collaborative Data Analysis|
|Kerstin Kleese Van Dam||Reliable Performance for Streaming Analysis Workflows||Reliable Performance for Streaming Analysis Workflows|
|Andre Luckow||Pilot-Streaming: Design Considerations for a HPC Stream Processing Framework||Pilot-Streaming: Design Considerations for a Stream Processing Framework for High-Performance Computing|
|Andre Martin||Elastic and Secure Energy Forecasting in Cloud Environments||Elastic and Secure Energy Forecasting in Cloud Environments|
|Benji Maruyama||Autonomous Experimentation Applied to Carbon Nanotube Synthesis||Autonomous Experimentation Applied to Carbon Nanotube Synthesis|
|Nina Mishra||Robust Random Cut Forest Based Anomaly Detection On Streams||Robust Random Cut Forest Based Anomaly Detection on Streams|
|Klaus Mueller||Mining Behavior Patterns in Streaming Multivariate Data||Mining Behavoir Patterns in Streaming Multivariate Data|
|Srinivasan Parthasarathy||Ego-net Sketching for Streaming Graph Analytics||Ego-net Sketching for Streaming Graph Analytics|
|Brian Quiter||Radiological Search – a Long-Standing Streaming Application||Radiological Search – a Long-Standing Streaming Application|
|Karthik Ramasamy||Streaming in Practice||Streaming in Practice|
|Alex Szalay||Streaming in Astronomy|
|Nathan Tallent||Processing Streaming Data In High Energy Physics Workflows|
|Craig Tull||Real-time Streaming Analysis for BES User Facilities|
|Vakhtang Tsulaia||Streaming in ATLAS|
|Steering Complex Systems using a Dynamic, Data-Driven Modeling Approach||Steering Complex Systems Using a Dynamic, Data-Driven Modeling Approach|
|Matt Wolf||Rethinking streaming system construction for next-generation collaborative science||Rethinking Streaming System Construction for Next-Generation Collaborative Science|
|John Wu||Technology for Distributed Streaming Analytics||Connecting Large Experimental Facility and Computing Facility with Streaming Analytics|
|Dean Williams||The Earth System Grid Federation (ESGF)|
|Shinjae Yoo||Streaming Manifold Learning and DOE Applications||Streaming Manifold Learning and DOE Applications|
|Dantong Yu||Deep learning for analyzing NSLS-II data stream||Deep learning for analyzing NSLS-II Data Stream|
STREAM2016 Agenda available.