Name
Abstract | ||
|---|---|---|
One of the major hurdles in maintaining long-lived electronic systems is that electronic parts become obsolete, no longer available from the original suppliers. When this occurs, an engineer is tasked with resolving the problem by finding a replacement that is "as similar as possible" to the original part. The current approach involves a laborious manual search through several electronic portals and data books. The search is difficult because potential replacements may differ from the original and from each other by one or more parameters. Worse still, the cumbersome nature of this process may cause the engineers to miss appropriate solutions amid the many thousands of parts listed in industry catalogs.In this paper, we address this problem by introducing the notion of a parametric "distance" between electronic components. We use this distance to search a large parts data set and recommend likely replacements. Recommendations are based on an adaptive nearest-neighbor search through the parametric data set. For each user, we learn how to scale the axes of the feature space in which the nearest neighbors are sought. This allows the system to learn each user's judgment of the phrase "as similar as possible." |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1145/956750.956842 | KDD |
Keywords | Field | DocType |
original supplier,data book,electronic portal,adaptive nearest-neighbor search,parts acquisition eportal,electronic part,large parts data,long-lived electronic system,original part,laborious manual search,electronic component,nearest neighbor,feature space,nearest neighbor search,query by example | k-nearest neighbors algorithm,Data mining,R-tree,Fixed-radius near neighbors,Best bin first,Computer science,Ball tree,Query by Example,Artificial intelligence,Cover tree,Nearest neighbor search,Machine learning | Conference |
ISBN | Citations | PageRank |
1-58113-737-0 | 4 | 0.80 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael Alonso | 1 | 119 | 33.92 |
| Jeffrey A Bloom | 2 | 377 | 52.32 |
| Hua Li | 3 | 26 | 5.40 |
| Chumki Basu | 4 | 574 | 160.00 |