Name
Abstract | ||
|---|---|---|
Imprecise information is represented by fuzzy disjunctive information, and an extended fuzzy relational model is used to accommodate such information. In the presence of imprecise information, answers to a query can be categorized into two kinds of answers: sure answers and possible answers. To find more likely answers to a given query, the authors develop a method to measure the matching strength of each tuple as an answer to the query. The quality of an answer is higher in the case where less extra information is required and the more sure information is provided |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/5326.704592 | IEEE Transactions on Systems, Man, and Cybernetics, Part C |
Keywords | Field | DocType |
sure information,sure answer,extra information,matching strength,possible answer,fuzzy disjunctive information,imprecise information,fuzzy relational databases,likely answer,extended fuzzy relational model,tuple,entropy,fuzzy logic,relational databases,engineering management,indexing terms,fuzzy systems,measurement uncertainty,fuzzy sets,fuzzy set theory,database systems,database theory | Query optimization,Relational calculus,Query language,Information retrieval,Computer science,Fuzzy set operations,Sargable,Query by Example,Relational model,Type-2 fuzzy sets and systems | Journal |
Volume | Issue | ISSN |
28 | 3 | 1094-6977 |
Citations | PageRank | References |
9 | 0.69 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ding-An Chiang | 1 | 231 | 27.25 |
| N. P. Lin | 2 | 9 | 0.69 |
| Chien-Chou Shis | 3 | 16 | 1.36 |