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Visual transformation for interactive spatiotemporal data mining

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Abstract

Analytical models intend to reveal inner structure, dynamics, or relationship of things. However, they are not necessarily intuitive to humans. Conventional scientific visualization methods are intuitive, but limited by depth, dimension, and resolution. The purpose of this study is to bridge the gap with transformation algorithms for mapping the data from an abstract space to an intuitive one, which include shape correlation, periodicity, multiphysics, and spatial Bayesian. We tested this approach with the oceanographic case study. We found that the interactive visualization increases robustness in object tracking and positive detection accuracy in object prediction. We also found that the interactive method enables the user to process the image data at less than 1 min per image versus 30 min per image manually. As a result, our test system can handle at least 10 times more data sets than traditional manual analyses. The results also suggest that minimal human interactions with appropriate computational transformations or cues may significantly increase the overall productivity.

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Correspondence to Yang Cai.

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Yang Cai is Director of Ambient Intelligence Laboratory and Faculty of Cylab and Institute of Complex Engineered Systems (ICES), Carnegie Mellon University, and Professor of Industrial Design at Modern Industrial Design Institute, Zhejiang University, P.R. China. He was Systems Scientist at Human–Computer Interaction Institute, Senior Scientist in CMRI at CMU, and Senior Designer for Daimler Chrysler. Cai’s interests include pattern recognition, visualization, and Ambient Intelligence. He cochaired international workshops in Ambient Intelligence for Scientific Discovery, Vienna, 2004 and AmI for Everyday Life, Spain, 2005 and Digital Human Modeling, UK, 2006. He is Editor of the Lecture Notes in Artificial Intelligence, LNAI 3345 and LNAI 3864, published by Springer. He was NASA Faculty Fellow in 2003 and 2004.

Richard Stumpf is a Senior Oceanographer and Team Leader of Remote Sensing National Oceanic and Atmospheric Administration (NOAA), Center for Coastal Monitoring and Assessment, Silver Spring, MD, where he leads 6–10 team members developing remote sensing capabilities for NOAA. Dr. Stumpf has extensively published papers on remote sensing for monitoring and forecasting harmful algal blooms and river plumes. He received his Ph.D. in Oceanography.

Timothy Wynne is an oceanographer with I.M. Systems Group and NOAA. Primarily his work at NOAA has involved ocean color imagery with an emphasis on algal bloom detection. He has also used remotely sensed data to quantify resuspension events. He has a M.S. in Oceanography from Old Dominion University and a B.S. in Marine Science from the Richard Stockton College of New Jersey.

Michelle Tomlinson has been an Oceanographer with the Center for Coastal Monitoring and Assessment, National Ocean Service, NOAA since 2002. Her current research focuses on the application of satellite-derived ocean color sensors (SeaWiFS, MODIS, MERIS) to detect, monitor, and forecast the occurrence of harmful algal blooms. This work has led to the development of an operational forecast system for harmful Karenia brevis blooms in the Gulf of Mexico. She received her B.S. in Marine Science Biology from Southampton College of Long Island University, and a M.S. in Oceanography from Old Dominion University.

Daniel Sai Ho (Daniel) Chung is a Master of Science Degree Student at the Institute of Networked Information, Carnegie Mellon University. He has been a Research Assistant in the Ambient Intelligence Laboratory since 2004, where he developed data mining and wireless video streaming systems for NASA and TRB-sponsored projects.

Xavier Boutonnier is a Research Assistant at Carnegie Mellon University, CYLAB—Ambient Intelligence Laboratory, Pittsburgh, PA, USA. He is a Master of Science Degree Student at the National Superior School of Electronics of Toulouse (ENSEEIHT) in France. He specialized in Signal, Image, Acoustic, and optimization. He has been working with Dr. Yang Cai on the NASA-sponsored data mining project. His favorite fields of application are Video, Image, acoustic, and other signal processing.

Matthias Ihmig is pursuing his Ph.D. in the area of software-defined radio at Munich Technical University, while he is working at BMW, Germany. He was an Intern Graduate Student at Carnegie Mellon University, USA. His interests include stereo vision, wireless networks, and intelligent systems.

Rafael Franco is a Master’s Degree Student in Electronics and Telecommunications at the Engineering School of ENSEEIHT in France. Currently, he is an Intern at Cylab working on assignments related to information visualization and wireless user positioning.

Nathaniel Bauernfeind is a Research Assistant at Carnegie Mellon University, CYLAB—Ambient Intelligence Laboratory, Pittsburgh, PA, USA. He is a Computer Science and Mathematics student in the School of Computer Science at CMU. His research interests include computational algorithms, 3D graphics programming, and artificial intelligence. He is working with Dr. Yang Cai on a driving simulator that focuses on algorithmic automation for General Motors.

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Cai, Y., Stumpf, R., Wynne, T. et al. Visual transformation for interactive spatiotemporal data mining. Knowl Inf Syst 13, 119–142 (2007). https://doi.org/10.1007/s10115-007-0075-5

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