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Issue No.06 - November/December (2010 vol.16)
pp: 900-907
Robert Kincaid , Agilent Laboratories
ABSTRACT
Electronic test and measurement systems are becoming increasingly sophisticated in order to match the increased complexity and ultra-high speed of the devices under test. A key feature in many such instruments is a vastly increased capacity for storage of digital signals. Storage of $10^9$ time points or more is now possible. At the same time, the typical screens on such measurement devices are relatively small. Therefore, these instruments can only render an extremely small fraction of the complete signal at any time. SignalLens uses a Focus+Context approach to provide a means of navigating to and inspecting low-level signal details in the context of the entire signal trace. This approach provides a compact visualization suitable for embedding into the small displays typically provided by electronic measurement instruments. We further augment this display with computed tracks which display time-aligned computed properties of the signal. By combining and filtering these computed tracks it is possible to easily and quickly find computationally detected features in the data which are often obscured by the visual compression required to render the large data sets on a small screen. Further, these tracks can be viewed in the context of the entire signal trace as well as visible high-level signal features. Several examples using real-world electronic measurement data are presented, which demonstrate typical use cases and the effectiveness of the design.
INDEX TERMS
Focus+Context, Lens, Test and Measurement, Electronic Signal, Signal Processing
CITATION
Robert Kincaid, "SignalLens: Focus+Context Applied to Electronic Time Series", IEEE Transactions on Visualization & Computer Graphics, vol.16, no. 6, pp. 900-907, November/December 2010, doi:10.1109/TVCG.2010.193
REFERENCES
[1] W. Aigner, S. Miksch, and W. Muller, Visualizing time-oriented data—A systematic view, Computers & Graphics, vol. 31, pp. 401–409, 2007.
[2] P. Buono, A. Aris, C. Plaisant et al. Interactive pattern search in time series, In Proc. SPIE, pp. 175–186, 2005.
[3] M. Carpendale, and C. Montagnese, A framework for unifying presentation space, In Proceedings of the ACM Symposium on User interface Software and Technology, pp. 70, 2001.
[4] S. Carpendale, J. Ligh, and E. Pattison, Achieving higher magnification in context, In Proceedings of the ACM Symposium on User interface Software and Technology, pp. 71–80, 2004.
[5] A. Cockburn, A. Karlson, and B. B. Bederson, A Review of Overview plus Detail, Zooming, and Focus plus Context Interfaces, ACM Computing Surveys, vol. 41, pp. 1–31, 2008.
[6] G. Furnas, Generalized fisheye views, ACM SIGCHI Bulletin, vol. 17, pp. 23, 1986.
[7] G. Furnas, A fisheye follow-up: further reflections on focus+context, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1008, 2006.
[8] A. L. Goldberger, L. A. N. Amaral, L. Glass et al. , PhysioBank, PhysioToolkit, and PhysioNet - Components of a new research resource for complex physiologic signals, Circulation, vol. 101, pp. E215–E220, 2000.
[9] L. Gomella and S. Haist, Clinician's Pocket Reference: McGraw-Hill Medical, 2003, p390.
[10] C. Gutwin, Improving focus targeting in interactive fisheye views, In Proceedings of the SIGCHI Conference on Human factors in Computing Systems, pp. 267–274, 2002.
[11] C. Gutwin and A. Skopik, Fisheyes are good for large steering tasks, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 208, 2003.
[12] H. Hochheiser and B. Shneiderman, Interactive exploration of time series data, Lecture Notes in Computer Science, pp. 441–446, 2001.
[13] E. Keogh, S. Lonardi, and B. Chiu, Finding surprising patterns in a time series database in linear time and space, In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp. 550–556, 2002.
[14] W. Kester and A. Devices, Data Conversion Handbook: Newnes, 2005, p. 608.
[15] R. Kincaid and K. Dejgaard, MassVis: Visual analysis of protein complexes using mass spectrometry, In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, pp. 163–170, 2009.
[16] R. Kincaid and H. Lam, Line graph explorer: scalable display of line graphs using Focus+ Context, In Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 404–411, 2006.
[17] N. Kumar, N. Lolla, E. Keogh et al. Time-series bitmaps: A practical visualization tool for working with large time series databases, In SIAM 2005 Data Mining Conference, pp. 531–535, 2005.
[18] J. Lamping, R. Rao, and P. Pirolli, A focus+ context technique based on hyperbolic geometry for visualizing large hierarchies, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 408, 1995.
[19] Y. Leung and M. Apperley, A review and taxonomy of distortion-oriented presentation techniques, ACM Transactions on Computer-Human Interaction (TOCHI), vol. 1, pp. 126–160, 1994.
[20] J. Lin, E. Keogh, S. Lonardi et al. Finding motifs in time series, In Proc. of the Workshop on Temporal Data Mining, pp. 53–68, 2002.
[21] R., Lopez-Hernandez, D. Guilmaine, M. J. McGuffin et al. A layer-oriented interface for visualizing time-series data from oscilloscopes, In Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), 2010, pp. 41–48, 2010.
[22] P. McLachlan, T. Munzner, E. Koutsofios et al. LiveRAC: Interactive visual exploration of system management time-series data, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1483–1492, 2008.
[23] E. Pietriga, and C. Appert, Sigma lenses: focus-context transitions combining space, time and translucence, In Proceeding of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1343–1352, 2008.
[24] E. Pietriga, C. Appert, and M. Beaudouin-Lafon, Pointing and beyond: an operationalization and preliminary evaluation of multi-scale searching, In Proceedings of the SIGCHI Conference on Human Factors in Computing Aystems, pp. 1224, 2007.
[25] C. Plaisant, D. Carr, and B. Shneiderman, Image-browser taxonomy and guidelines for designers, IEEE Software, pp. 21–32, 1995.
[26] W. H. Press, Numerical recipes in C++, 2nd ed. Cambridge; New York: Cambridge University Press, 2002.
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