LiveRAC: interactive visual exploration of system management time-series data

We present LiveRAC, a visualization system that supports the analysis of large collections of system management time-series data consisting of hundreds of parameters across thousands of network devices. LiveRAC provides high information density using a reorderable matrix of charts, with semantic zooming adapting each chart's visual representation to the available space. LiveRAC allows side-by-side visual comparison of arbitrary groupings of devices and parameters at multiple levels of detail. A staged design and development process culminated in the deployment of LiveRAC in a production environment. We conducted an informal longitudinal evaluation of LiveRAC to better understand which proposed visualization techniques were most useful in the target environment.

[1]  J.C. Roberts,et al.  State of the Art: Coordinated & Multiple Views in Exploratory Visualization , 2007, Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007).

[2]  Jock D. Mackinlay,et al.  Automating the design of graphical presentations of relational information , 1986, TOGS.

[3]  Ben Shneiderman,et al.  Interactive Exploration of Time Series Data , 2003 .

[4]  E. Tufte Beautiful Evidence , 2006 .

[5]  Pat Hanrahan,et al.  Query, analysis, and visualization of hierarchically structured data using Polaris , 2002, KDD.

[6]  Peter Jono McLachlan,et al.  LiveRAC : live reorderable accordion drawing , 2006 .

[7]  Alfred Kobsa,et al.  A Workplace Study of the Adoption of Information Visualization Systems. Proceedings of I-KNOW'03: 3rd International Conference on Knowledge Management, Graz, Austria , 2003 .

[8]  J. V. van Wijk,et al.  Cluster and calendar based visualization of time series data , 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99).

[9]  Nicholas Chen,et al.  TreeJuxtaposer : Scalable Tree Comparison using Focus + Context with Guaranteed Visibility , 2006 .

[10]  Heidrun Schumann,et al.  Visualizing time-oriented data - A systematic view , 2007, Comput. Graph..

[11]  Ben Shneiderman,et al.  Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies , 2006, BELIV '06.

[12]  Tamara Munzner,et al.  PRISAD: a partitioned rendering infrastructure for scalable accordion drawing (extended version) , 2006 .

[13]  Harri Siirtola,et al.  Interaction with the Reorderable Matrix , 1999, 1999 IEEE International Conference on Information Visualization (Cat. No. PR00210).

[14]  Edward R. Tufte,et al.  Envisioning Information , 1990 .

[15]  Tamara Munzner,et al.  An evaluation of pan & zoom and rubber sheet navigation with and without an overview , 2006, CHI.

[16]  Robert Kincaid,et al.  Line graph explorer: scalable display of line graphs using Focus+Context , 2006, AVI '06.

[17]  Steven P. Reiss,et al.  Stretching the rubber sheet: a metaphor for viewing large layouts on small screens , 1993, UIST '93.

[18]  Colin Ware,et al.  Information Visualization: Perception for Design , 2000 .

[19]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[20]  Daniel A. Keim,et al.  Visualizing large-scale telecommunication networks and services (case study) , 1999, VIS '99.

[21]  Ronald A. Rensink,et al.  On the Failure to Detect Changes in Scenes Across Brief Interruptions , 2000 .

[22]  Jock D. Mackinlay,et al.  The cognitive coprocessor architecture for interactive user interfaces , 1989, UIST '89.

[23]  Alfred Kobsa,et al.  Benefits of information visualization systems for administrative data analysts , 2003, Proceedings on Seventh International Conference on Information Visualization, 2003. IV 2003..

[24]  Jacques Bertin,et al.  Graphics and graphic information-processing , 1981 .

[25]  Ken Perlin,et al.  Pad: an alternative approach to the computer interface , 1993, SIGGRAPH.

[26]  Jessica Lin,et al.  Visually mining and monitoring massive time series , 2004, KDD.