DYNAMIC DATA DRIVEN APPLICATION SYSTEMS: RESEARCH CHALLENGES AND OPPORTUNITIES

Dynamic Data Driven Applications Systems (DDDAS) is a paradigm where data is dynamically integrated into an executing application, and in reverse, the application dynamically steers the measurement process in a feedback control loop. Since its inception in 2000, the DDDAS concept has been successfully applied to a host of application areas. New technologies are emerging such as big data, the Internet of Things, and cloud/edge computing. With these trends DDDAS is poised to have large-scale impacts in areas such as smart cities, manufacturing, health care, and security, to name a few. Each author describes their views concerning the important research challenges facing the DDDAS paradigm and opportunities for impact in the years ahead.

• Publication year

  2018

• Venue

  Online World Conference on Soft Computing in Industrial Applications

• Publication date

  2018-12-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/WSC.2018.8632379
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Dynamic Data Driven Application Systems

  2020cited by this paper
• Handbook of Dynamic Data Driven Applications Systems

  2018cited by this paper
• Multispectral Image Fusion and Colorization

  2018cited by this paper
• Power consumption of future event list implementations in discrete event simulations

  2018cited by this paper
• Zero Energy Synchronization of Distributed Simulations

  2018cited by this paper
• Automatic model construction for the behavior of human crowds

  2017cited by this paper
• Online Analysis of Simulation Data with Stream-based Data Mining

  2017cited by this paper
• Purpose and benefits of hybrid simulation: Contributing to the convergence of its definition

  2017cited by this paper
• Airplane flight safety using error-tolerant data stream processing

  2017cited by this paper
• Dynamic, data-driven processing of multispectral video streams

  2017cited by this paper
• WSC 2067: What are the chances?

  2017cited by this paper
• Research Challenges in Modeling and Simulation for Engineering Complex Systems

  2017cited by this paper
• DDDAMS-Based Border Surveillance and Crowd Control via Aerostats, UAVs, and Ground Sensors

  2017cited by this paper
• Energy consumption of HLA data distribution management approaches

  2017cited by this paper
• Dynamic calibration of agent-based models using data assimilation

  2016cited by this paper
• An Artificial Intelligence-Based Approach for Simulating Pedestrian Movement

  2016cited by this paper
• Energy consumption of Data Driven traffic simulations

  2016cited by this paper
• Context-Enhanced Information Fusion: Boosting Real-World Performance with Domain Knowledge

  2016cited by this paper
• Grand challenges for modeling and simulation: simulation everywhere—from cyberinfrastructure to clouds to citizens

  2015cited by this paper
• The next generation of modeling & simulation: integrating big data and deep learning

  2015cited by this paper
• Data assimilation in agent based simulation of smart environments using particle filters

  2015cited by this paper
• The influence of memory on indoor environment exploration: A numerical study

  2015cited by this paper
• Random-point-based filters: analysis and comparison in target tracking

  2015cited by this paper
• A DDDAMS-based planning and control framework for surveillance and crowd control via UAVs and UGVs

  2013cited by this paper
• High-Level Information Fusion Management and System Design

  2012cited by this paper
• Calibrating dynamic pedestrian route choice with an Extended Range Telepresence System

  2011cited by this paper
• Agile Software Requirements: Lean Requirements Practices for Teams, Programs, and the Enterprise

  2011cited by this paper
• Extended BDI Framework for Modelling Human Decision-Making in Complex Automated Manufacturing Systems

  2008cited by this paper
• Decision field theory extensions for behavior modeling in dynamic environment using Bayesian belief network

  2008cited by this paper
• Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements

  2004cited by this paper

• Adaptive Synchronization and Pacing Control for Visual Interactive Simulation

  2024cites this paper
• Passive Radio Frequency-based 3D Indoor Positioning System via Ensemble Learning

  2023cites this paper
• Transforming Discrete Event Models To Machine Learning Models

  2023cites this paper
• Data Assimilation for Agent-Based Models

  2023cites this paper
• Reduction of the Computational Cost of Tuning Methodology of a Simulator of a Physical System

  2023cites this paper
• A framework proposal for machine learning-driven agent-based models through a case study analysis

  2022cites this paper
• A CASCADING ONLINE-SIMULATION FRAMEWORK TO OPTIMIZE INSTALLATION CYCLES FOR OFFSHORE WIND FARMS

  2021cites this paper
• Dynamic Data-Driven Self-healing Application for Phasor Measurement Unit Networks

  2020cites this paper
• Dynamic Data Driven Applications Systems: Third International Conference, DDDAS 2020, Boston, MA, USA, October 2-4, 2020, Proceedings

  2020cites this paper
• Blockchain Methods for Trusted Avionics Systems

  2019cites this paper
• A Study of Lightweight DDDAS Architecture for Real-Time Public Safety Applications Through Hybrid Simulation

  2019cites this paper