Possible and certain keys for SQL

Köhler, Henning; Leck, Uwe; Link, Sebastian; Zhou, Xiaofang

doi:10.1007/s00778-016-0430-9

Cited by 47 publications

(43 citation statements)

References 36 publications

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“…This is particularly appealing to our keys since the experiments in Section 6 confirm that i) key sets for which Armstrong tables do not exist are rare, and ii) keys that represent real application semantics can be enforced efficiently in SQL database systems. Our findings illustrate the impact of nulls on the theory of Armstrong databases, and have revealed a technical error in previous research [29], see Section B of the appendix in the technical report [27].…”

Section: Structural Characterizationsupporting

confidence: 58%

“…While Armstrong tables are claimed to exist for any set of weak/strong FDs [29], we discovered a technical error in the proof. Indeed, in the technical report [27], Example 15 on page 42 shows a set of strong and weak FDs for which no Armstrong table exists.…”

Section: Related Workmentioning

confidence: 99%

“…We conclude and comment on future work in Section 7. Proofs and further material are available in a technical report [27].…”

mentioning

confidence: 99%

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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: Structural Characterizationsupporting

confidence: 58%

Section: Related Workmentioning

confidence: 99%

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Possible and certain SQL keys

KöhlerHenning¹,

LinkSebastian²,

Zhou³

2015

Proc. VLDB Endow.

Self Cite

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“…One or two dimensions alone are inadequate to capture an accurate and complete picture of the overall data quality, which can span a large number of dimensions (Jayawardene et al 2013b). Additionally, these solutions are generally underpinned by assumptions relating to the availability of significant meta-data [e.g., data distributions (Dasu and Johnson 2003), thresholds (Song and Chen 2011) and probabilities (Köhler et al 2015)], which may not be readily available for open or repurposed datasets.…”

Section: )mentioning

confidence: 99%

Discovering Data Quality Problems

2019

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Existing methodologies for identifying data quality problems are typically user-centric, where data quality requirements are first determined in a top-down manner following well-established design guidelines, organizational structures and data governance frameworks. In the current data landscape, however, users are often confronted with new, unexplored datasets that they may not have any ownership of, but that are perceived to have relevance and potential to create value for them. Such repurposed datasets can be found in government open data portals, data markets and several publicly available data repositories. In such scenarios, applying top-down data quality checking approaches is not feasible, as the consumers of the data have no control over its creation and governance. Hence, data consumers-data scientists and analysts-need to be empowered with data exploration capabilities that allow them to investigate and understand the quality of such datasets to facilitate well-informed decisions on their use. This research aims to develop such an approach for discovering data quality problems using generic exploratory methods that can be effectively applied in settings where data creation and use is separated. The approach, named LANG, is developed through a Design Science approach on the basis of semiotics theory and data quality dimensions. LANG is empirically validated in terms of soundness of the approach, its repeatability and generalizability.

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“…For example, in the case of medical data, it might be discovered that carcinogenicity depends functionally from a set of attributes. Since the problem of discovering the complete set of FDs for a given relation is P-hard [23], each approach performs different strategies to minimize, as much as possible, the necessary computations. The proposed approaches can be grouped in two main categories, the top-down approaches and the bottomup approaches.…”

Section: Discovering Keys and Fds In Relational Databasesmentioning

confidence: 99%

TKBG — The knowledge based grep using self-key discovery and semantic linking for online resources

Poorani

Baba

et al. 2015

2015 Online International Conference on Green Engineering and Technologies (IC-GET)

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the tremendous growth of information resources in the World Wide Web (WWW) made the information retrieval process inefficient and irrelevant due to poor linking of data. One of challenging issues is to find the relevant information from web. Semantic web brings solution by meaningful information retrieval through unique semantic links. In this project, we propose a Self-Key Discovery algorithm based on Sematic Linking Network which allows systematically acquire keys to link RDF(Resource Description Framework) data resources with Semantic relationships among subject Ontologies. Data published on the WWW are usually generated automatically, thus may contain enormous information, duplicates or may be incomplete. RDF is used to represent web content which is the initial stage for semantic linking. SKD algorithm used for RDF data set only, Creation of RDF data is important step to implement semantic linking of data. Proposed Self Key Discovery technique is to performing semantic Linking on knowledge domain clusters using an Ontology Guided Data Linkage (OGDL) framework. This framework allows self-organization of contributing data resources through the discovery of semantic Keys, by performing Linking data of ontological domain knowledge relating to RDF resources. The framework thus automates the discovery of Key to link data across unrelated Resources, and different RDF data set for concept clustering and cluster mapping. In this project, demonstrate the feasibility of our Self Key Discovery algorithms through semantic links to set of RDF Resources, and run on real-world datasets.

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Possible and certain keys for SQL

Cited by 47 publications

References 36 publications

Possible and certain SQL keys

Possible and certain SQL keys

Discovering Data Quality Problems

TKBG — The knowledge based grep using self-key discovery and semantic linking for online resources

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