Relaxed Functional Dependencies—A Survey of Approaches

Caruccio, Loredana; Deufemia, Vincenzo; Polese, Giuseppe

doi:10.1109/tkde.2015.2472010

Cited by 90 publications

(49 citation statements)

References 77 publications

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“…In addition, CORDS recommends groups of columns on which to maintain certain simple joint statistics. Researchers on data cleansing also put lots of efforts on detecting functional dependencies including soft dependency and approximate dependency [8,25]. CORADD [21] proposes a correlationaware database designer to recommend the best set of materialized views and indexes for given database size constraints.…”

Section: Related Workmentioning

confidence: 99%

Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations

Tian

et al. 2019

Proceedings of the 2019 International Conference on Management of Data

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Database administrators construct secondary indexes on data tables to accelerate query processing in relational database management systems (RDBMSs). These indexes are built on top of the most frequently queried columns according to the data statistics. Unfortunately, maintaining multiple secondary indexes in the same database can be extremely space consuming, causing significant performance degradation due to the potential exhaustion of memory space. In this paper, we demonstrate that there exist many opportunities to exploit column correlations for accelerating data access. We propose Hermit, a succinct secondary indexing mechanism for modern RDBMSs. Hermit judiciously leverages the rich soft functional dependencies hidden among columns to prune out redundant structures for indexed key access. Instead of building a complete index that stores every single entry in the key columns, Hermit navigates any incoming key access queries to an existing index built on the correlated columns. This is achieved through the Tiered Regression Search Tree (TRS-Tree), a succinct, ML-enhanced data structure that performs fast curve fitting to adaptively and dynamically capture both column correlations and outliers. We have developed Hermit in two different RDBMSs, * Work done during an internship at IBM Research -Almaden.

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Section: Related Workmentioning

confidence: 99%

Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations

Tian

et al. 2019

Proceedings of the 2019 International Conference on Management of Data

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“…Accordingly, many generalizations have been proposed over the last decades, which were recently and exhaustively reviewed and classified in [6]. This classification separates (i) 345 extent relaxations from (ii) attribute-comparison relaxations.…”

Section: Related Work and Discussionmentioning

confidence: 99%

“…These generalizations can be divided into two main groups, depending 90 on the strategy to relax the semantics of FDs [6]. These two categories are (1) "extent-relaxing dependencies" and (2) "attribute-relaxing dependencies".…”

Section: Generalizations Of Functional Dependenciesmentioning

confidence: 99%

“…To overcome this problem, different generalizations of FDs have 10 been defined. These generalizations can be classified according to the criteria by which they relax the equality condition of FDs [6]. According to this classification, two main strategies are presented: "extent relaxation" and "attribute relaxation" (in agreement with the terminology introduced in [6]).…”

Section: Introductionmentioning

confidence: 99%

“…They cannot be directly handled in the present framework, as not only blocks of tolerances should be considered, but also the ordering of the objects in each block should coincide as well [38,6]. Actually, searching for these ordered dependencies is related to the mining of closed gradual itemsets [39].…”

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confidence: 99%

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Characterizing approximate-matching dependencies in formal concept analysis with pattern structures

Baixeries

Codocedo

Kaytoue

et al. 2018

Discrete Applied Mathematics

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Functional dependencies (FDs) provide valuable knowledge on the relations between attributes of a data table. A functional dependency holds when the values of an attribute can be determined by another. It has been shown that FDs can be expressed in terms of partitions of tuples that are in agreement w.r.t. the values taken by some subsets of attributes. To extend the use of FDs, several generalizations have been proposed. In this work, we study approximatematching dependencies that generalize FDs by relaxing the constraints on the attributes, i.e. agreement is based on a similarity relation rather than on equality. Such dependencies are attracting attention in the database field since they allow uncrisping the basic notion of FDs extending its application to many different fields, such as data quality, data mining, behavior analysis, data cleaning or data partition, among others. We show that these dependencies can be formalized in the framework of Formal Concept Analysis (FCA) using a previous formalization introduced for standard FDs. Our new results state that, starting from the conceptual structure of a pattern structure, and generalizing the notion of relation between tuples, approximate-matching dependencies can be characterized as implications in a pattern concept lattice. We finally show how to use basic FCA algorithms to construct a pattern concept lattice that entails these dependencies after a slight and tractable binarization of the original data.

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