Lessons from the implementation of an adaptive parts acquisition ePortal

ABSTRACT

In recent work we have developed a novel approach to the design and implementation of an online portal (ePortal) to help application engineers find replacements for electronic parts that have become obsolete (and hence will no longer be produced). Our approach makes use of machine learning techniques to improve the performance of a database search function. However, the purpose of this note is not to describe in detail the application nor our technical solution - that has been done elsewhere (see [1,2]). Rather, it is our intention to present some of the lessons learned from our project. Below, we provide a brief introduction to the technical approach, concentrate on several of the most salient lessons, and conclude with a description of the current state of the project.

REFERENCES

• 1: Alonso, R., Bloom, J., and Li, H. SmartSearch for Obsolete Parts Acquisition. Technical Report, Sarnoff Corporation, Princeton, NJ, 2002
• 2: Rafael Alonso , Jeffrey A. Bloom , Hua Li , Chumki Basu, An adaptive nearest neighbor search for a parts acquisition ePortal, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956842]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering; Clustering; Search process

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition

General Terms:
Algorithms, Design, Experimentation, Management, Measurement, Performance

Keywords:
adaptive search, k-nearest neighbor classification, lessons leaned, normalization, query by example, user profiling