Qualitative Cleaning of Uncertain Data

Köehler, Henning; Link, Sebastian

doi:10.1145/2983323.2983679

Cited by 5 publications

(9 citation statements)

References 33 publications

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“…The possibilistic grounding of this model was first presented in [44] where the duality between p-degrees and c-degrees is pointed out and exploited, but the model has its roots in an early proposal dealing with weighted tuple databases [31]. The possibilistic model has gained recent momentum in a series of articles that highlight its applications in data modeling [21,38], database design [45], and data cleaning [35]. In [44], a new class of possibilistic functional dependencies was introduced, and the equivalence of their associated implication problem to that of Horn clauses in possibilistic logic was proven.…”

Section: Possibilistic Data Models and Their Target Use Casesmentioning

confidence: 99%

“…Another important area of application for any form of constraints over this possibilistic model is data cleaning. Here, the details can be found in [35]. Basically, the p-degrees of tuples provide a new view of cleaning dirty data: Instead of viewing the data itself as dirty, we view the p-degrees associated with the data as dirty.…”

Section: Possibilistic Data Models and Their Target Use Casesmentioning

confidence: 99%

“…In this subsection we illustrate an application of possibilistic keys to data cleaning. In [35], possibilistic data cleaning was introduced. In classical data cleaning, a minimal sequence of update operations are applied to the data to restore consistency with respect to given set of constraints.…”

Section: Data Cleaningmentioning

confidence: 99%

“…This section illustrates this new view on our running example with respect to the class of possibilistic keys. For details of the algorithm we refer the interested reader to [35].…”

Section: Data Cleaningmentioning

confidence: 99%

“…Detailed experiments complement our theoretical analysis regarding the computational complexity of these problems. Primary application areas of possibilistic keys are found in database design and data cleaning, which have been investigated in related work [35,45]. A case study from web data extraction illustrates the use of possibility degrees in data integration as well as their acquisition.…”

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confidence: 99%

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Possibilistic keys

Balamuralikrishna

Jiang

Köhler

et al. 2019

Fuzzy Sets and Systems

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Possibility theory is applied to introduce and reason about the fundamental notion of a key for uncertain data. Uncertainty is modeled qualitatively by assigning to tuples of data a degree of possibility with which they occur in a relation, and assigning to keys a degree of certainty which says to which tuples the key applies. The associated implication problem is characterized axiomatically and algorithmically. Using extremal combinatorics, we then characterize the families of non-redundant possibilistic keys that attain maximum cardinality. In addition, we show how to compute for any given set of possibilistic keys a possibilistic Armstrong relation, that is, a possibilistic relation that satisfies every key in the given set and violates every possibilistic key not implied by the given set. We also establish an algorithm for the discovery of all possibilistic keys that are satisfied by a given possibilistic relation. It is shown that the computational complexity of computing possibilistic Armstrong relations is precisely exponential in the input, and the decision variant of the discovery problem is NP-complete as well as W[2]-complete in the size of the possibilistic key. Further applications of possibilistic keys in constraint maintenance, data cleaning, and query processing are illustrated by examples. The computation of possibilistic Armstrong relations and discovery of possibilistic keys from possibilistic relations have been implemented as prototypes. Extensive experiments with these prototypes provide insight into the size of possibilistic Armstrong relations and the time to compute them, as well as the time it takes to compute a cover of the possibilistic keys that hold on a possibilistic relation, and the time it takes to remove any redundant possibilistic keys from this cover.

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Section: Possibilistic Data Models and Their Target Use Casesmentioning

confidence: 99%

Section: Possibilistic Data Models and Their Target Use Casesmentioning

confidence: 99%

Section: Data Cleaningmentioning

confidence: 99%

“…This section illustrates this new view on our running example with respect to the class of possibilistic keys. For details of the algorithm we refer the interested reader to [35].…”

Section: Data Cleaningmentioning

confidence: 99%

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confidence: 99%

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Possibilistic keys

Balamuralikrishna

Jiang

Köhler

et al. 2019

Fuzzy Sets and Systems

Self Cite

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Handling Uncertainty in Relational Databases with Possibility Theory - A Survey of Different Modelings

Pivert

Prade

2018

Lecture Notes in Computer Science

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Mainstream approaches to uncertainty modeling in relational databases are probabilistic. Still some researchers persist in proposing representations based on possibility theory. They are motivated by the ability of this latter setting for modeling epistemic uncertainty and by its qualitative nature. Interestingly enough, several possibilistic models have been proposed over time, and have been motivated by different application needs ranging from database querying, to database design and to data cleaning. Thus, one may distinguish between four different frameworks ordered here according to an increasing representation power: databases with (i) layered tuples; (ii) certainty-qualified attribute values; (iii) attribute values restricted by general possibility distributions; (iv) possibilistic c-tables. In each case, we discuss the role of the possibility-necessity duality, the limitations and the benefit of the representation settings, and their suitability with respect to different tasks.

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Entity integrity management under data volume, variety and veracity

Link

2023

Knowl Inf Syst

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Qualitative Cleaning of Uncertain Data

Cited by 5 publications

References 33 publications

Possibilistic keys

Possibilistic keys

Handling Uncertainty in Relational Databases with Possibility Theory - A Survey of Different Modelings

Entity integrity management under data volume, variety and veracity

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