IEEE Big Data 2018 Workshop: Big Data For Digital Twins

Workshop Title: Big Data for Digital Twins

December 10-13, 2018

Seattle, WA

PART OF: IEEE Big Data 2018

http://cci.drexel.edu/bigdata/bigdata2018

** There is a 1-day registration option **

New

Agenda

2:30-2:50 "INVITED TALK: Feeding the Digital Twin: Basics, Models and Lessons Learned from Building an IoT Analytics Toolbox" - Dominik Riemer

Semantic Models and Ontologies for Digital Twins

2:50-3:00 "Towards Semantically Enhanced Digital Twins", Evgeny Kharlamov, Francisco Martin-Recuerda, Brandon Perry, David Cameron, Roar Fjellheim, and Arild Waaler

3:00-3:10 "Representing Industrial Data Streams in Digital Twins using Semantic Labeling", Philipp Zehnder and Dominik Riemer

3:10-3:20 "Linking an Asset and a Domain Specific Ontology for a Simple Asset TimeSeries Application", Charbel El Kaed, Victor Danilchenko, Francois Delpech, John Brodeur, and Alexis Radisson

Digital Twins in practice -Big Data with real-time IoT

3:20-3:30 "Data-driven Digital Twin approach for process optimization: an industry use case", Dejan Milenovic and Nenad Stojanovic

3:30-3:40 "Simulation-ready digital twin for realtime management of logistics systems", Benjamin Korth, Christian Schwede, and Markus Zajac

3:40-3:50 "Continuous real-time anomaly detection in flexible production: D2Lab-based use case", Nenad Stojanovic and Milan Jovic

3:50-4:00 "MyFitnessDigitalTwin Data Analytics driven Continuous Improvement of the Trainee Performances in the Fitness, Aleksandar Stojanovic and Milan Djordje

Digital Twins & Standardization

4:00-4:10 "INVITED TALK: Potential standardisation opportunities related to Big Data and Digital Twins", Richard Mark Soley, PhD, CEO of the Object Management Group (OMG), and CEO of the Industrial Internet Consortium (IIC)

4:10-4:15 Concluding discussion – common approaches and issues emerging from the workshop

Introduction to workshop

Digital twins are dynamic digital or virtual software replications of physical assets, products, and systems. Popular applications areas include areas such as Manufactured products (i.e. cars, wind turbines, airplanes, ships), Industrial processes (Energy systems, Power plants, Smart Grids, Oil & Gas production), and Buildings and Infrastructures (Smart Buildings, Intelligent Transportation Systems, bridge monitoring).

Digital twins have moved from concept to reality very rapidly in recent years. IDC predicts that by 2020, 30% of global 2000 companies will use data from digital twins of IoT-connected products and assets to improve product-innovation success rates and organizational productivity, achieving gains of up to 25%.

In October 2017 Gartner added digital twins to its top 10 strategic technology trends for 2018. With an estimated 21 billion connected sensors and endpoints by 2020, digital twins could exist for billions of things in the near future. Potentially billions of dollars of savings in maintenance repair and operation and optimized IoT asset performance are expected.

However, the huge potential of digital twin technology is currently mainly reflected in the better design of an asset, based on the extensive simulations in various conditions, requiring huge computing resources (usually HPC). On the other hand, the explosion of (I)IoT has introduced advanced sensing of an industrial asset (product, process, system) that is enriched with the real-time perception of surrounding environment in order to enable real-time management of an asset. Real-world data enables a new quality in interpreting the model. This capability enables more efficient (run-time) operation of an asset through increased real-world situational awareness. By delivering a quantitative foundation, big data analytics enables digital twins to empower enterprises to both rapidly identify new improvement opportunities and diagnose and correct problems before they reach a critical level.

Consequently, new industry challenges call for a tight integration of the simulation and data analytics approaches:

the simulations can be treated as a new and an extremely intensive source of (virtual/future) data that will be processed using data analytics, and
generated (IoT / real) data can be treated as an objective and expert-independent source for the interpretation and validation of the numerical models.

Therefore, exploiting the full potential of Digital Twins will depend on the further interaction of two key technologies: Big Data for exploring the value of created data including HPC/Processing facilities to support processing and simulations. This goal will require at least two types of innovations related to hybridization of the models and data:

model-driven: new simulations that will combine numerical and data-driven models and
big data-driven: new data analytics methods that can be applied to past, current and future data.

Research topics included in the workshop

Modeling and management of a digital twin
Handling Data Volume, Variety, Velocity and Veracity for digital twins
Integration and interlinking of physical (data-driven) and digital (numerical) models
Hierarchical twinning - building up twins (and analyses) of more complex systems based on component twins
Data Analytics on simulation data generated from digital twins, including on-line analytics methods
Hybrid Analytics - first order models combined with data-driven models
Bridging simulation - based modeling with data-driven modeling
Architectures for integration of simulations and data analytics
Real-time interactivity and synchronization of the physical device and the twin
Optimizing the real - world system behavior by refining both the data-driven model and the computational model
Methodologies for continuous model improvement
Lifecycle tracking of assets through digital twins
Digital twin validation
Closing the loop between the digital and real-world
Applications such as monitoring, control, asset management and maintenance, powered by Hybrid Twins
Digital twin and big data case studies from particular domains: manufacturing, industrial processing, energy systems, smart buildings, transportation systems, personal twins, medical twins.

Important dates

Oct 22, 2018: Due date for full workshop papers submission
Nov 1, 2018: Notification of paper acceptance to authors
Nov 15, 2018: Camera-ready of accepted papers
Dec 10 - 13, 2018: Workshop [exact date TBD]

Paper Submission

All papers accepted for the workshop will be included in the Proceedings published by the IEEE Computer Society Press, made available at the IEEE Big Data Conference. Please submit a full-length paper (up to 10 page IEEE 2-column format) through the online submission system. See: https://wi-lab.com/cyberchair/2018/bigdata18/scripts/ws_submit.php. We also encourage submission of short papers (up to 4 pages) reporting work in progress. Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to "formatting instructions" below).

Formatting Instructions 8.5" x 11" (DOC, PDF)

LaTex Formatting Macros

Program Committee Members

Prof. Dr. David Maier, Portland State University, Portland, Oregon

Dr. Lea Shanley, Southwest Big Data Hub, Raleigh, North Carolina

Dr. Jukka Nurminen, VTT Technical Research Centre of Finland Ltd, Finland

Dr. Ruben Costa, UNINOVA-INSTITUTO DE DESENVOLVIMENTO DE NOVAS TECNOLOGIAS-ASSOCIACAO, Portugal

Dr. Aníbal Reñones, Fundación Cartif, Spain

Roberta Turra, CINECA - Consorzio Interuniversitario, Italy

Dr. Dominik Riemer, FZI FORSCHUNGSZENTRUM INFORMATIK AM KARLSRUHER INSTITUT FUR TECHNOLOGIE, Germany

Program Chairs

Dr. Arne J. Berre (SINTEF)

Dr. Ljiljana Stojanovic (Fraunhofer IOSB)

Dr. Nenad Stojanovic (NISSATECH)