Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Visualizing time-oriented data-A systematic view
Computers and Graphics
GVP model based temporal visualisation of user-centric data
International Journal of Metadata, Semantics and Ontologies
A swarm-inspired projection algorithm
Pattern Recognition
In-formation flocking: an approach to data visualization using multi-agent formation behavior
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Building a front end for a sensor data cloud
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
Data clustering and visualization using cellular automata ants
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Toward a methodology for agent-based data mining and visualization
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Interactive visual analysis of temporal cluster structures
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Agent based simulation output analysis
Proceedings of the Winter Simulation Conference
Simulating multivariate time series using flocking
Proceedings of the Winter Simulation Conference
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This research demonstrates how principles of self-organization and behavior simulation can be used to represent dynamic data evolutions by extending the concept of information flocking, originally introduced by Proctor & Winter [1], to time-varying datasets. A rule-based behavior system continuously controls and updates the dynamic actions of individual, three-dimensional elements that represent the changing data values of reoccurring data objects. As a result, different distinguishable motion types emerge that are driven by local interactions between the spatial elements as well as the evolution of time-varying data values. Notably, this representation technique focuses on the representation of dynamic data alteration characteristics, or how reoccurring data objects change over time, instead of depicting the exact data values themselves. In addition, it demonstrates the potential of motion as a useful information visualization cue. The original information flocking approach is extended to incorporate time-varying datasets, live database querying, continuous data streaming, real-time data similarity evaluation, automatic shape generation and more stable flocking algorithms. Different experiments prove that information flocking is capable of representing short-term events as well as long-term temporal data evolutions of both individual and groups of time-dependent data objects. An historical stock market quote price dataset is used to demonstrate the algorithms and principles of time-varying information flocking.