Designing dashboards for managing model lifecycle

ABSTRACT

Data mining is being extensively used for analysis of large data collections. While there is previous work on dashboard support for visual mining of the data, there is little or no work on dashboard support for managing the lifecycle (e.g. health) of the data mining models themselves. Issues such as quick performance decay, large scale deployments, collaborative use, and real-time business integration of models necessitate this type of support. In this paper, based on a year long study, we first describe the six stages of the model lifecycle and the preliminary design of the backend system that helps users manage mining models. Next, we discuss the three dimensions to be considered for dashboard visualization of the model lifecycle: introspection, customization, and presentation.

REFERENCES

• 1: Stephen Few, Information Dashboard Design: The Effective Visual Communication of Data, O'Reilly Media, Inc., 2006
• 2: Ming C. Hao , Daniel A. Keim , Umeshwar Dayal , Jörn Schneidewind, Business process impact visualization and anomaly detection, Information Visualization, v.5 n.1, p.15-27, March 2006 [doi>10.1057/palgrave.ivs.9500115]
• 3: Jhilmil Jain , Ismail Ari , Jun Li, Understanding the challenges faced during the management of data mining models, Proceedings of the 2nd ACM Symposium on Computer Human Interaction for Management of Information Technology, November 14-15, 2008, San Diego, California [doi>10.1145/1477973.1477988]
• 4: Shadan Malik, Enterprise Dashboards: Design and Best Practices for IT, John Wiley & Sons, Inc., New York, NY, 2005

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.2 User/Machine Systems
          Subjects: Human factors

General Terms:
Design, Human Factors, Management

Keywords:
dashboards, data mining models, model lifecycle, visualization