A new method for computing fuzzy functional dependencies in relational database systems

Vučetić, Miljan; Hudec, Miroslav; Vujošević, Mirko

doi:10.1016/j.eswa.2012.11.019

Cited by 25 publications

(12 citation statements)

References 23 publications

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“…When considering all attributes in a database whilst determining relations between attributes, FFDs could end in a combinatorial explosion [20] and LSs are cycled in execution of a large number of rules [7]. How can we avoid this issue?…”

Section: A Computational Burdenmentioning

confidence: 99%

“…Recently, a new method for computing FFDs was proposed in [20] for fuzzy databases. To discover FFDs, the following definitions have been applied:…”

Section: Ffds For Extracting Relational Knowledgementioning

confidence: 99%

“…Applying this methodology we are able to discover FFDs and therefore compute a measure which enables us to determine the proportion of tuples in a relation where FFDs are satisfied. The proportion parameter P is calculated in the following way [20]:…”

Section: Ffds For Extracting Relational Knowledgementioning

confidence: 99%

“…Keeping in mind the aforementioned points, Fuzzy Functional Dependencies (FFDs) [17,20,21] and Linguistic Summaries (LSs) [8,11,16] could provide the solution. FFDs are able to detect attributes that have a high value of dependency considering their respective whole domains.…”

Section: Introductionmentioning

confidence: 99%

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Synergy of linguistic summaries and fuzzy functional dependencies for mining knowledge in the data

Hudec

Vučetić

Vujošević

2014

2014 18th International Conference on System Theory, Control and Computing (ICSTCC)

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Databases contain potentially valuable knowledge that should be processed and interpreted in order to be useful. In this context, we examine synergy of fuzzy functional dependencies and linguistic summaries. Strength of dependency between two examined attributes in the whole database can be detected and expressed by fuzzy functional dependencies. If dependency exists but is not sufficiently strong, linguistic summaries are a convenient method for revealing the character of dependency. Both methods simulate human reasoning in looking for the knowledge by expressions of linguistic terms which ensure that similar entities are treated similarly and mined knowledge is represented in an understandable way. The database of municipal statistics is a suitable for mining relational knowledge due to a variety of collected data which could provide significant support for policy and decision making. Finally, we conclude by discussing practical issues and topics for further research.

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Section: A Computational Burdenmentioning

confidence: 99%

“…Recently, a new method for computing FFDs was proposed in [20] for fuzzy databases. To discover FFDs, the following definitions have been applied:…”

Section: Ffds For Extracting Relational Knowledgementioning

confidence: 99%

Section: Ffds For Extracting Relational Knowledgementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Synergy of linguistic summaries and fuzzy functional dependencies for mining knowledge in the data

Hudec

Vučetić

Vujošević

2014

2014 18th International Conference on System Theory, Control and Computing (ICSTCC)

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“…Fortunately, fuzzy logic provides variety of approaches like fuzzy functional dependencies, conformance between attributes, similarity between their values, flexible implications and variety of aggregations. Some of these functions were applied in new approach for calculating fuzzy functional dependencies [34]. The future task will be experimenting on the data and knowledge base related to the public administration and statistics.…”

Section: Knowledge Systems In Relational Databasesmentioning

confidence: 99%

Collecting and managing fuzzy data in statistical relational databases

Hudec¹,

Praženka²

2016

SJI

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Abstract. Statistical institutes are focusing on variety of data sources from traditional surveys to big-data. Many of these data and concepts can be expressed as crisp values. But many other data cannot be expressed by precise values. In order to collect, store and manage the fuzziness in data we have adapted the fuzzy meta model as an extension of traditional relational database. Furthermore, experts' knowledge often contains vagueness and subjectivity. If we store this knowledge in a fuzzy database we can build knowledge management systems capable to cope with fuzziness. Statistical institutes cooperate in the data exchange. We have briefly discussed a simple way of extending the SDMX standard to cope with the fuzzy data in a way that does not influence exchanging precise values. Our research was focused on examining promising ways for managing fuzziness of real world because statistical institutes have been starting to analyze variety of promising data sources where not all data are always precise.

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Aggregation of Fuzzy Conformances

Hudec

Vučetić²

2019

New Trends in Aggregation Theory

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A new method for computing fuzzy functional dependencies in relational database systems

Cited by 25 publications

References 23 publications

Synergy of linguistic summaries and fuzzy functional dependencies for mining knowledge in the data

Synergy of linguistic summaries and fuzzy functional dependencies for mining knowledge in the data

Collecting and managing fuzzy data in statistical relational databases

Aggregation of Fuzzy Conformances

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