Real-Time Acquisition of Buyer Behaviour Data – The Smart Shop Floor Scenario

  • Bo Yuan
  • Maria Orlowska
  • Shazia Sadiq
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4365)


The emergence of a range of new technologies like auto-identification devices, active tags, and smart items has impacted profoundly on business software solutions such as supply chain management, logistics, and inventory control. Integration of automatic data acquisition with enterprise applications as well as potential to provide real-time analytic functionality is opening new avenues for business process automation. In this paper, we propose a novel application of these technologies in a retailing environment leading to the vision of a smart shop floor. We firstly present the infrastructure for the smart shop floor. We then demonstrate how the proposed infrastructure is feasible and conducive to real-time acquisition of buyer behaviour data through three selected queries. Complete algorithmic solutions to each query are presented to provide proof of concept, and further deliberations on analytic potential of the proposed infrastructure are also provided.


Data Capture in Real-time 


Regular Paper 


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  1. 1.
    Alexander, K., Gillian, T., Gramling, K., Kindy, M., Moogimane, D., Schultz, M., Woods, M.: IBM Business Consulting Services – Focus on the Supply Chain: Applying Auto-ID within the Distribution Center. Auto-ID Center, White paper IBM-AUTOID-BC-002 (September 2003)Google Scholar
  2. 2.
    Finkenzeller, K., RFID,: Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification, 2nd edn. John Wiley & Sons, New York (2003)Google Scholar
  3. 3.
    Bornhovd, C., Lin, T., Haller, H., Schaper, J.: Integrating Automatic Data Acquisition with Business Processes – Experiences with SAP’s Auto-ID Infrastructure. In: Proceedings of the 30th VLDB Conference, Toronto, Canada (2004)Google Scholar
  4. 4.
    EPCGlobal: EPC Tag Data Standards Version 1.1 Rev. 1.24, EPCGlobal, Standards Specification (April 2004),
  5. 5.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: The 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  6. 6.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: The 11th International Conference on Data Engineering, pp. 3–14 (1995)Google Scholar
  7. 7.
    Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: The 5th International Conference on Extending Database Technology, pp. 3–17 (1996)Google Scholar
  8. 8.
    Luo, C., Chung, S.: A Scalable Algorithm for Mining Maximal Frequent Sequences Using Sampling. In: The 16th International Conference on Tools with Artificial Intelligence, pp. 156–165 (2004)Google Scholar
  9. 9.
    Maimon, O., Rokach, L.: The Data Mining and Knowledge Discovery Handbook. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Bo Yuan
    • 1
  • Maria Orlowska
    • 1
  • Shazia Sadiq
    • 1
  1. 1.School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia QLD 4072, BrisbaneAustralia

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