Flexible Query Answering Using Distance-Based Fuzzy Relations

Bodenhofer, Ulrich; Küng, Josef; Saminger, Susanne

doi:10.1007/11964810_10

Cited by 7 publications

(7 citation statements)

References 32 publications

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“…Similar approaches can be found in the fields of preference modelling [10] or utility theory [6]. Further applications comprise, but are clearly not limited to, computer-assisted assessment [18] and flexible database queries [2]. Particular classes of binary aggregation functions have been investigated in the framework of many-valued logics [11,12] or probability distribution functions, in particular copulas [16].…”

Section: Introductionmentioning

confidence: 98%

On representations of 2-increasing binary aggregation functions

Durante

Saminger‐Platz

Sarkoci

2008

Information Sciences

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Section: Introductionmentioning

confidence: 98%

On representations of 2-increasing binary aggregation functions

Durante

Saminger‐Platz

Sarkoci

2008

Information Sciences

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“…These kinds of fuzzy conditions are described by membership functions and each tuple in databases has a unique degree of membership for fuzzy conditions of a fuzzy query [25]. Specially, the fuzzy query can acquire an accurate range for all tuples with the α-cut [9,20]. In [26], Ma et al propose the general SQL fuzzy query based on fuzzy set theory.…”

Section: Fuzzy Queriesmentioning

confidence: 99%

“…Tahani et al [21] firstly advocated the use of fuzzy sets for querying conventional databases, where imprecise conditions inside queries were taken as fuzzy sets. Besides, Bodenhofer et al [20] integrate the distance-based fuzzy relations into the flexible query answering system. The benefits are allowing to use simple distances as a common basis for defining both types of relations.…”

Section: Fuzzy Queriesmentioning

confidence: 99%

“…With the above denotation, any fuzzy query can be transformed into precise conditions within different cuts. Existing fuzzy query techniques [20,26] only query in a cut F α when the threshold α is given. An uncertain database D is instantiated into a possible world assuming mutual independence of each possible value.…”

Section: Preliminariesmentioning

confidence: 99%

“…In [21], Tahani et al firstly advocate the use of fuzzy set theory for querying conventional databases, where fuzzy query conditions are seen as fuzzy sets. An available strategy to answer fuzzy queries is returning the α-cut [20] associated with each fuzzy query. But these works for coping with querying over uncertain data require considerable improvement.…”

Section: Introductionmentioning

confidence: 99%

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Efficient fuzzy ranking queries in uncertain databases

Wang

et al. 2011

Appl Intell

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Recently, uncertain data have received dramatic attention along with technical advances on geographical tracking, sensor network and RFID etc. Also, ranking queries over uncertain data has become a research focus of uncertain data management. With dramatically growing applications of fuzzy set theory, lots of queries involving fuzzy conditions appear nowadays. These fuzzy conditions are widely applied for querying over uncertain data. For instance, in the weather monitoring system, weather data are inherent uncertainty due to some measurement errors. Weather data depicting heavy rain are desired, where "heavy" is ambiguous in the fuzzy query. However, fuzzy queries cannot ensure returning expected results from uncertain databases.In this paper, we study a novel kind of ranking queries, Fuzzy Ranking queries (FRanking queries) which extend the traditional notion of ranking queries. FRanking queries are able to handle fuzzy queries submitted by users and return k results which are the most likely to satisfy fuzzy queries in uncertain databases. Due to fuzzy query conditions, the ranks of tuples cannot be evaluated by existing ranking functions. We propose Fuzzy Ranking Function to calculate tuples' ranks in uncertain databases for both attribute-level and tuple-level uncertainty models. Our ranking function take both the uncertainty and fuzzy semantics into account. FRanking queries are formally defined based on Fuzzy Ranking Function. In the processing of answering FRanking queries, we present a pruning method which safely prunes unnecessary tuples to reduce the search space. To further improve the efficiency, we design an efficient algorithm, namely Incremental Membership Algorithm (IMA) which efficiently answers FRanking queries by evaluating the ranks of incremental tuples under each threshold for the fuzzy set. We demonstrate the effectiveness and efficiency of our methods through the theoretical analysis and experiments with synthetic and real datasets.

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Efficient Fuzzy Top-k Query Processing over Uncertain Objects

Wang

Lin

et al. 2010

Lecture Notes in Computer Science

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Flexible Query Answering Using Distance-Based Fuzzy Relations

Cited by 7 publications

References 32 publications

On representations of 2-increasing binary aggregation functions

On representations of 2-increasing binary aggregation functions

Efficient fuzzy ranking queries in uncertain databases

Efficient Fuzzy Top-k Query Processing over Uncertain Objects

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