Visualizing time-dependent key performance indicator in a graph-based analysis

The usage of visual analytics during the analysis of business warehouse calculated key performance indicators is one emerging challenge in modern business applications. On the one hand, a complex network of key performance indicators has to be supervised. On the other hand, within this network only few key performance indicators change obviously within a short period of time. The sole mapping of the complexity of a network of key performance indicators to a graph-based visualization only covers static information and neglects temporal dependencies. We present a new visualization approach for the enrichment of graph-based visualizations of key performance indicator networks by introducing a multi-encoded visualization of additional functional, contextual and temporal information. The should help the user to understand relationships between KPIs and alert him if something is going wrong.

[1]  Allison Woodruff,et al.  Guidelines for using multiple views in information visualization , 2000, AVI '00.

[2]  Tong-Yee Lee,et al.  Coherent Time-Varying Graph Drawing with Multifocus+Context Interaction , 2012, IEEE Transactions on Visualization and Computer Graphics.

[3]  S. Sheingold,et al.  Relationships Among Performance Measures for Medicare Managed Care Plans , 2001, Health care financing review.

[4]  J. Durbin,et al.  Testing for serial correlation in least squares regression. II. , 1950, Biometrika.

[5]  Yan Liu,et al.  Temporal causal modeling with graphical granger methods , 2007, KDD '07.

[6]  Matteo Golfarelli,et al.  Beyond data warehousing: what's next in business intelligence? , 2004, DOLAP '04.

[7]  Daniel A. Keim,et al.  Visual pattern discovery in timed event data , 2011, Electronic Imaging.

[8]  R. Matthews Storks Deliver Babies (p= 0.008) , 2000 .

[9]  Ramana Rao,et al.  A focus+context technique based on hyperbolic geometry for visualizing large hierarchies , 1995, CHI '95.

[10]  C. Granger,et al.  Spurious regressions in econometrics , 1974 .

[11]  Daniel A. Keim,et al.  Datenvisualisierung und Data Mining , 2002, Datenbank-Spektrum.

[12]  Martin Wollschlaeger,et al.  Advanced Concepts for Flexible Data Integration in Heterogeneous Production Environments , 2013 .

[13]  Sabine Cornelsen,et al.  Drawing Clusters and Hierarchies , 1999, Drawing Graphs.

[14]  S. E. Schaeffer Survey Graph clustering , 2007 .

[15]  J. Durbin,et al.  Testing for serial correlation in least squares regression. I. , 1950, Biometrika.

[16]  Stefan Hesse,et al.  Reference Model Concept for Structuring and Representing Performance Indicators in Manufacturing , 2012, APMS.

[17]  Ioannis G. Tollis,et al.  Dynamic Graph Drawings: Trees, Series-Parallel Digraphs, and Planar ST-Digraphs , 1995, SIAM J. Comput..

[18]  Peter Eades,et al.  Multilevel Visualization of Clustered Graphs , 1996, GD.

[19]  H. Simon,et al.  Spurious Correlation: A Causal Interpretation* , 1954 .

[20]  Daniel A. Keim,et al.  Visual Analytics Challenges , 2009 .

[21]  Gerardine DeSanctis,et al.  COMPUTER GRAPHICS AS DECISION AIDS: DIRECTIONS FOR RESEARCH* , 1984 .

[22]  R. Kelly Rainer,et al.  Introduction to Information Systems: International Student Version , 2013 .

[23]  Chris North,et al.  Visualizing Biological Pathways: Requirements Analysis, Systems Evaluation and Research Agenda , 2005, Inf. Vis..

[24]  Anastasia Bezerianos,et al.  Annotating BI visualization dashboards: needs & challenges , 2012, CHI.

[25]  Stoyan Tanev,et al.  Visualization of complex data relationships and maps: using the BLOOM platform to provide business insights , 2011, CompSysTech '11.

[26]  Rainer Groh,et al.  Towards a Model for the Integration of Time into a Graph-based Key Performance Indicator Analysis , 2014, SIGRAD.

[27]  Ayellet Tal,et al.  Dynamic Drawing of Clustered Graphs , 2004, IEEE Symposium on Information Visualization.

[28]  Volodymyr Vasyutynskyy,et al.  Layered architecture for production and logistics cockpits , 2012, Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012).

[29]  Ángel Ortiz Bas,et al.  Quantitative relationships between key performance indicators for supporting decision-making processes , 2009, Comput. Ind..

[30]  Andreas Kerren,et al.  Guiding the interactive exploration of metabolic pathway interconnections , 2012, Inf. Vis..

[31]  John T. Stasko,et al.  Visual Analytics for Converging-Business-Ecosystem Intelligence , 2012, IEEE Computer Graphics and Applications.

[32]  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.

[33]  R. Kaplan,et al.  The balanced scorecard--measures that drive performance. , 2015, Harvard business review.

[34]  A. Raftery,et al.  Model‐based clustering for social networks , 2007 .

[35]  Yurdaer N. Doganata,et al.  Visualizing meetings as a graph for more accessible meeting artifacts , 2011, CHI EA '11.

[36]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[37]  Tina Eliassi-Rad,et al.  Visual Analysis of Large Heterogeneous Social Networks by Semantic and Structural Abstraction , 2006 .

[38]  Frank Leymann,et al.  Identifying influential factors of business process performance using dependency analysis , 2011, Enterp. Inf. Syst..