Probabilistic Keys for Data Quality Management

Brown, Pieta; Link, Sebastian

doi:10.1007/978-3-319-19069-3_8

Cited by 13 publications

(8 citation statements)

References 31 publications

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“…All application areas of keys deserve new attention in the light of possible and certain keys. It is interesting to combine possible and certain keys with possibilistic or probabilistic keys [8,26].…”

Section: Discussionmentioning

confidence: 99%

Possible and certain SQL keys

KöhlerHenning¹,

LinkSebastian²,

Zhou³

2015

Proc. VLDB Endow.

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Driven by the dominance of the relational model, the requirements of modern applications, and the veracity of data, we revisit the fundamental notion of a key in relational databases with NULLs. In SQL database systems primary key columns are NOT NULL by default. NULL columns may occur in unique constraints which only guarantee uniqueness for tuples which do not feature null markers in any of the columns involved, and therefore serve a different function than primary keys. We investigate the notions of possible and certain keys, which are keys that hold in some or all possible worlds that can originate from an SQL table, respectively. Possible keys coincide with the unique constraint of SQL, and thus provide a semantics for their syntactic definition in the SQL standard. Certain keys extend primary keys to include NULL columns, and thus form a sufficient and necessary condition to identify tuples uniquely, while primary keys are only sufficient for that purpose. In addition to basic characterization, axiomatization, and simple discovery approaches for possible and certain keys, we investigate the existence and construction of Armstrong tables, and describe an indexing scheme for enforcing certain keys. Our experiments show that certain keys with NULLs do occur in real-world databases, and that related computational problems can be solved efficiently. Certain keys are therefore semantically well-founded and able to maintain data quality in the form of Codd's entity integrity rule while handling the requirements of modern applications, that is, higher volumes of incomplete data from different formats.

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Section: Discussionmentioning

confidence: 99%

Possible and certain SQL keys

KöhlerHenning¹,

LinkSebastian²,

Zhou³

2015

Proc. VLDB Endow.

Self Cite

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“…Research about constraints on probabilistic data is still in its infancy [5,26,38]. Qualitative approaches to uncertain data deal with either query languages or extensions of functional dependencies [4].…”

Section: Related Workmentioning

confidence: 99%

Cardinality constraints on qualitatively uncertain data

Hall

Köehler

Link

et al. 2015

Data & Knowledge Engineering

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Modern applications require advanced techniques and tools to process large volumes of uncertain data. For that purpose we introduce cardinality constraints as a principled tool to control the occurrences of uncertain data. Uncertainty is modeled qualitatively by assigning to each object a degree of possibility by which the object occurs in an uncertain instance. Cardinality constraints are assigned a degree of certainty that stipulates on which objects they hold. Our framework empowers users to model uncertainty in an intuitive way, without the requirement to put a precise value on it. Our class of cardinality constraints enjoys a natural possible world semantics, which is exploited to establish several tools to reason about them. We characterize the associated implication problem axiomatically and algorithmically in linear input time. Furthermore, we show how to visualize any given set of our cardinality constraints in the form of an Armstrong sketch. Even though the problem of finding an Armstrong sketch is precisely exponential, our algorithm computes a sketch with conservative use of time and space. Data engineers may therefore compute Armstrong sketches that they can jointly inspect with domain experts in order to consolidate the set of cardinality constraints meaningful for a given application domain.

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“…While for the rest of our definitions, to the best of our knowledge, there is no work to compare with. Conversely, we plan to use related works performed in XML [9,8] and relational databases [6] to define semantically similar constraints and applications.…”

Section: Definition Of Constraints For Rdfmentioning

confidence: 99%

“…Since there is no other work that considers SPARQL property paths nor probabilistic constraints, there is a lack of manually-annotated gold standards that we can use. We will generate new evaluation datasets to show the effectiveness of our approach, considering different data sources such as Web Data Commons 6 . In order to measure scalability, these datasets must be of different sizes (from thousands up to millions).…”

Section: Discovery Of Constraintsmentioning

confidence: 99%

“…Formally, constraints C2 and C4 are known as integrity constraints (used to ensure accuracy and consistency of data), whilst constraints C1 and C3 are known as cardinality constraints (used to specify a minimum and maximum bound for relationships with properties that an entity can have [29,15]). C5 is special in the sense it expresses the marginal probability by which the constraint holds in the dataset [4,6]. C6 is known as syntactic pattern constraint [17], and restrict the syntax that the values of a property can have.…”

Section: Introductionmentioning

confidence: 99%

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On Learnability of Constraints from RDF Data

Muñoz

2016

The Semantic Web. Latest Advances and New Domains

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RDF is structured, dynamic, and schemaless data, which enables a big deal of flexibility for Linked Data to be available in an open environment such as the Web. However, for RDF data, flexibility turns out to be the source of many data quality and knowledge representation issues. Tasks such as assessing data quality in RDF require a different set of techniques and tools compared to other data models. Furthermore, since the use of existing schema, ontology and constraint languages is not mandatory, there is always room for misunderstanding the structure of the data. Neglecting this problem can represent a threat to the widespread use and adoption of RDF and Linked Data. Users should be able to learn the characteristics of RDF data in order to determine its fitness for a given use case, for example. For that purpose, in this doctoral research, we propose the use of constraints to inform users about characteristics that RDF data naturally exhibits, in cases where ontologies (or any other form of explicitly given constraints or schemata) are not present or not expressive enough. We aim to address the problems of defining and discovering classes of constraints to help users in data analysis and assessment of RDF and Linked Data quality.

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Probabilistic Keys for Data Quality Management

Cited by 13 publications

References 31 publications

Possible and certain SQL keys

Possible and certain SQL keys

Cardinality constraints on qualitatively uncertain data

On Learnability of Constraints from RDF Data

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