On similarity relations in fuzzy relational databases

Potoczny, Henry

doi:10.1016/0165-0114(84)90070-8

Cited by 22 publications

(5 citation statements)

References 6 publications

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“…While generating equivalence groups is considered desirable, the computational complexity is of concern as far as massive data pertain in real applications. With various efforts [14,15,[29][30][31][32][33][52][53][54], the complexity has been reduced from O(n 5 ) to O(n 2 ) on time and O(n 2 ) on space [15]. It is worth noticing that these levels of complexity need to be further improved in the context of large data scales.…”

Section: Related Workmentioning

confidence: 99%

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An efficient incremental method for generating equivalence groups of search results in information retrieval and queries

Zhang

Wei

2012

Knowledge-Based Systems

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Section: Related Workmentioning

confidence: 99%

“…Based on the seeds, W k can be iteratively updated according to the following properties. Note that, due to the transitivity in transitive closure [10,30,32,33], for any i, j, m = 1,2,. . .…”

Section: Generating M þ K Via Iteratively Updatingmentioning

confidence: 99%

An efficient incremental method for generating equivalence groups of search results in information retrieval and queries

Zhang

Wei

2012

Knowledge-Based Systems

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“…Researchers in fuzzy relational database systems have developed various paradigms to deal with uncertainty and inexactness (Buckles & Petry, 1982a, b;Potoczny, 1984;Zadeh, 1965;Zemankova-Leech, 1984). In this article, the notion of distinguishability is used to measure the degree to which two values of an attribute are dissimilar.…”

Section: Formulation Of a Fuzzy Relational Modelmentioning

confidence: 98%

A database model for medical consultation

Anvari

1991

J. Am. Soc. Inf. Sci.

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The database model presented in this article is suitable for applications in which queries may require noncrisp references to certain attributes. The data item (attribute) values may be crisp or fuzzy. For instance, such adjectives as "high" or "normal" may be attribute values for the attribute "blood pressure." A disease or a condition can be described by a number of symptoms which may be crisp alphanumeric values or fuzzy terms such as "high" or "normal." A query into this database can retrieve diseases which have "similar" symptoms. The similarity or "indistinguishability" is a measure defined by the database user on the relations that describe a family of diseases. This database system in conjunction with a rule base can provide the framework for a medical consultation system.

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“…A second view of fuzziness in data bases relies on the notion of interchangeability or tolerance relations defined on attribute domains Petry, 1982, 1987 ;Cayrol et al, 1982 ;Potoczny, 1984 ;McClelland et al, 1988 ;Zemankova, 1989 ;Rundensteiner et al, 1989). For instance, this approach may be used to represent data issued from some measurement devices in order to represent the error on the measure.…”

Section: What 'Fuzzy Data Base' Means ?mentioning

confidence: 99%

An Introduction to the Fuzzy Set and Possibility Theory-Based Treatment of Flexible Queries and Uncertain or Imprecise Databases

Bosc

Prade

1997

Uncertainty Management in Information Systems

129

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In this paper, it is shown that fuzzy sets and possibility theory provide an homogeneous framework for the representation of both imprecise/uncertain information and soft queries with a flexible interpretation. Incompletely known information as well as flexible query handling capabilities are expected to extend the range of applications for future database management systems. The term fuzzy databases which is extensively used in the specialized literature covers several different meanings which are reviewed. A special emphasis is put on flexible queries addressed to regular databases. Such queries enables the user to easily express preferences among more or less admissible attribute values. Several approaches for introducing flexibility, including fuzzy sets, are compared. A query language based on SQL is outlined and some issues related to query processing are discussed. In addition, possibility theory proves to be useful for representing imperfectly known data and soft constraints. Pattern matching is extended for handling this kind of data, as well as flexible queries.

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On similarity relations in fuzzy relational databases

Cited by 22 publications

References 6 publications

An efficient incremental method for generating equivalence groups of search results in information retrieval and queries

An efficient incremental method for generating equivalence groups of search results in information retrieval and queries

A database model for medical consultation

An Introduction to the Fuzzy Set and Possibility Theory-Based Treatment of Flexible Queries and Uncertain or Imprecise Databases

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