Discover Dependencies from Data—A Review

Liu, Jixue; Li, Jiuyong; Liu, Chengfei; Chen, Yongfeng

doi:10.1109/tkde.2010.197

Cited by 129 publications

(73 citation statements)

References 37 publications

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“…In our example the agree set for the pair of tuples(1,2) is ag{A} Similarly we have ag (1,6), ag(2,7) ag(3,4) ={B_D_E} ag(3,5)={E} ag(4,5)={C,E},so agree sets of r are ag(r)={A,BDE, E,CE}. The maximal sets from the agree sets as follows, in this example, we have max (A, r) = {BDE, CE} and the complement of the maximal set of A is cmax {A, r} = {AC, ABD}.…”

Section: Fig 2: Steps In Dep-miner Algorithmmentioning

confidence: 93%

“…In fact X o contains the attributes on all the parent nodes of X and all the dependent nodes of the parent nodes [1].…”

Section: Quasi-closure Of Setmentioning

confidence: 99%

“…We denote by max(dep(r),A) the set of maximal sets for A [4]. [1] A free set P is a set of attributes such that removing an attribute from P decreases the number of P's distinct values. Free sets correspond to left hand sides of FDs.…”

Section: Maximal Setmentioning

confidence: 99%

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Mining Functional Dependency in Relational Databases using FUN and Dep-Miner: A Comparative Study

Chavan¹,

Verma²

2013

IJCA

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Database is a collection of tables of data items, if the database is organized according to relational model it is called relational database. In a relational database, a logical and efficient design is just as critical. A poorly designed database may provide erroneous information, or may even fail to work properly may be difficult to use. Most of these problems are the result of two bad design features called redundant data and anomalies. Database normalization is the process of designing a database satisfying a set of integrity constraints, efficiently and in order to avoid inconsistencies when manipulating the database. Most of the research work has been devoted to functional dependencies. There are several algorithms have been developed in the past year like TANE, FD_Mine FD_Discover, Dep-Miner, FUN, FD Analysis using Rough sets, FD discovery by Bayes Net. In This paper we present a comparative study over Dep-Miner and FUN. We compare the working process of Dep-Miner and FUN using a simple example.

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Section: Fig 2: Steps In Dep-miner Algorithmmentioning

confidence: 93%

“…In fact X o contains the attributes on all the parent nodes of X and all the dependent nodes of the parent nodes [1].…”

Section: Quasi-closure Of Setmentioning

confidence: 99%

See 1 more Smart Citation

Mining Functional Dependency in Relational Databases using FUN and Dep-Miner: A Comparative Study

Chavan¹,

Verma²

2013

IJCA

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“…While these approaches cannot reduce the worst-case exponential complexity of dependency discovery, experimental studies have shown that column-based approaches work well on tables containing a very large number of rows and row-based approaches work well for wide tables [6,113]. For more details on the computational complexity of various Fd and Ind discovery algorithms, we refer the interested reader to [94].…”

Section: Summaries and Sketchesmentioning

confidence: 99%

Profiling relational data: a survey

Abedjan¹,

2015

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Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases.

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“…In particular, entity integrity is enforced by primary keys [34]. Core problems investigate reasoning [21], Armstrong databases [18], and discovery [22,24,32,33,40]. Applications include anomaly detection [43], consistency management [4], consistent query answers [3,28], data cleaning [17], exchange [16], fusion [36], integration [9], profiling [35], quality [38], repairs [6], and security [7], schema design [14], query optimization [23], transaction processing [2], and view maintenance [37].…”

Section: Related Workmentioning

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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Discover Dependencies from Data—A Review

Cited by 129 publications

References 37 publications

Mining Functional Dependency in Relational Databases using FUN and Dep-Miner: A Comparative Study

Mining Functional Dependency in Relational Databases using FUN and Dep-Miner: A Comparative Study

Profiling relational data: a survey

Possible and certain SQL keys

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