Maintenance of Discovered Functional Dependencies: Incremental Deletion

Wang, Shyue-Liang; Tsou, Wen-Chieh; Lin, Jiann-Horng; Hong, Tzung-Pei

doi:10.1007/978-3-540-44999-7_55

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…The surveys by Liu et al [77] and Papenbrock et al [99] present and compare different FD profiling techniques from both a theoretical and a practical perspective, respectively. To discover FDs incrementally or maintain the FDs of a dynamic dataset over time, various incremental profiling algorithms exist [20,108,121].…”

Section: Functional Dependencies (Fds)mentioning

confidence: 99%

Data dependencies for query optimization: a survey

2021

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Effective query optimization is a core feature of any database management system. While most query optimization techniques make use of simple metadata, such as cardinalities and other basic statistics, other optimization techniques are based on more advanced metadata including data dependencies, such as functional, uniqueness, order, or inclusion dependencies. This survey provides an overview, intuitive descriptions, and classifications of query optimization and execution strategies that are enabled by data dependencies. We consider the most popular types of data dependencies and focus on optimization strategies that target the optimization of relational database queries. The survey supports database vendors to identify optimization opportunities as well as DBMS researchers to find related work and open research questions.

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Section: Functional Dependencies (Fds)mentioning

confidence: 99%

Data dependencies for query optimization: a survey

2021

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“…5.1.4. Also, Wang et al present an approach for maintaining discovered Fds after data deletions [134]. From a data cleaning standpoint, there are solutions for incremental detection of Fd and Cfd violations [50], and incremental data repairing with respect to Fds and Cfds [30].…”

Section: Profiling Dynamic Datamentioning

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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Incremental Discovery of Inclusion Dependencies

Shaabani

Meinel

2017

Proceedings of the 29th International Conference on Scientific and Statistical Database Management

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Maintenance of Discovered Functional Dependencies: Incremental Deletion

Cited by 5 publications

References 4 publications

Data dependencies for query optimization: a survey

Data dependencies for query optimization: a survey

Profiling relational data: a survey

Incremental Discovery of Inclusion Dependencies

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