Abstract
In any database, description files are essential to understand the data files in it. However, it is not uncommon that one is left with data files without any description file. An example is the aftermath of a system crash; other examples are related to security problems. Manual determination of the subject of a data file can be a difficult and tedious task particularly if files are look-alike. An example is a big survey database where data files that look alike are actually related to different subjects. Two data files on the same subject will probably have similar semantic structures of attributes. We detect the similarity between two attributes. Then we create clusters of attributes to compare the similarity of the subjects of two data files. And finally a machine learning technique is used to predict the subject of unseen data files.
| Original language | English (US) |
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| Title of host publication | Proceedings of the IEEE Knowledge & Data Engineering Exchange Workshop, KDEX |
| Editors | Anon |
| Place of Publication | Piscataway, NJ, United States |
| Publisher | IEEE |
| Pages | 172-179 |
| Number of pages | 8 |
| State | Published - 1997 |
| Externally published | Yes |
| Event | Proceedings of the 1997 IEEE Knowledge & Data Engineering Exchange Workshop, KDEX - Newport Beach, CA, USA Duration: Nov 4 1997 → Nov 4 1997 |
Other
| Other | Proceedings of the 1997 IEEE Knowledge & Data Engineering Exchange Workshop, KDEX |
|---|---|
| City | Newport Beach, CA, USA |
| Period | 11/4/97 → 11/4/97 |
ASJC Scopus subject areas
- General Engineering