16,826 research outputs found
Characterization of order-like dependencies with formal concept analysis
Functional Dependencies (FDs) play a key role in many fields
of the relational database model, one of the most widely used database
systems. FDs have also been applied in data analysis, data quality, knowl-
edge discovery and the like, but in a very limited scope, because of their
fixed semantics. To overcome this limitation, many generalizations have
been defined to relax the crisp definition of FDs. FDs and a few of their
generalizations have been characterized with Formal Concept Analysis
which reveals itself to be an interesting unified framework for charac-
terizing dependencies, that is, understanding and computing them in a
formal way. In this paper, we extend this work by taking into account
order-like dependencies. Such dependencies, well defined in the database
field, consider an ordering on the domain of each attribute, and not sim-
ply an equality relation as with standard FDs.Peer ReviewedPostprint (published version
Strongly possible functional dependencies for SQL
Missing data is a large-scale challenge to research and investigate. It reduces the statistical power and produces negative consequences that may introduce selection bias on the data. Many approaches to handle this problem have been introduced. The main approaches suggested are either missing values to be ignored (removed) or imputed (filled in) with new values. This paper uses the second method. Possible worlds and possible and certain keys were introduced in Köhler et.al., and by Levene et.al. Köhler and Link introduced certain functional dependencies (c-FD) as a natural complement to Lien's class of possible functional dependencies (p-FD). Weak and strong functional dependencies were studied by Levene and Loizou. We introduced the intermediate concept of strongly possible worlds that are obtained by imputing values already existing in the table in a preceding paper. This results in strongly possible keys (spKey's) and strongly possible functional dependencies (spFD's). We give a polynomial algorithm to verify a single spKey and show that in general, it is NP-complete to verify an arbitrary collection of spKeys. We give a graph-theoretical characterization of the validity of a given spFD X →sp Y. We show, that the complexity to verify a single strongly possible functional dependency is NP-complete in general, then we introduce some cases when verifying a single spFD can be done in polynomial time. As a step forward axiomatization of spFD's, the rules given for weak and strong functional dependencies are checked. Appropriate weakenings of those that are not sound for spFD's are listed. The interaction between spFD's and spKey's and certain keys is studied. Furthermore, a graph theoretical characterization of implication between singular attribute spFD's is given
characterization of order-like dependencies with formal concept analysis
Functional Dependencies (FDs) play a key role in many fields of the relational database model, one of the most widely used database systems. FDs have also been applied in data analysis, data quality, knowledge discovery and the like, but in a very limited scope, because of their fixed semantics. To overcome this limitation, many generalizations have been defined to relax the crisp definition of FDs. FDs and a few of their generalizations have been characterized with Formal Concept Analysis which reveals itself to be an interesting unified framework for characterizing dependencies, that is, understanding and computing them in a formal way. In this paper, we extend this work by taking into account order-like dependencies. Such dependencies, well defined in the database
field, consider an ordering on the domain of each attribute, and not simply an equality relation as with standard FDsPostprint (published version
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution
Entity resolution (ER), an important and common data cleaning problem, is
about detecting data duplicate representations for the same external entities,
and merging them into single representations. Relatively recently, declarative
rules called matching dependencies (MDs) have been proposed for specifying
similarity conditions under which attribute values in database records are
merged. In this work we show the process and the benefits of integrating three
components of ER: (a) Classifiers for duplicate/non-duplicate record pairs
built using machine learning (ML) techniques, (b) MDs for supporting both the
blocking phase of ML and the merge itself; and (c) The use of the declarative
language LogiQL -an extended form of Datalog supported by the LogicBlox
platform- for data processing, and the specification and enforcement of MDs.Comment: To appear in Proc. SUM, 201
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