Introduction

Kuper, Gabriel M.; Libkin, Leonid; Paredaens, Jan

doi:10.1007/978-3-662-04031-7_1

Cited by 30 publications

(45 citation statements)

References 5 publications

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“…Constraint databases [5,6] form an extension of relational databases [8] where the database can contain variables that are usually constrained by linear or polynomial equations.…”

Section: Constraint Databasesmentioning

confidence: 99%

“…Our method does not depend on any particular linear classification method. (2) Join: The linear constraint relations are joined together using a constraint database join operator [5,6].…”

Section: Reclassification With Constraint Databasesmentioning

confidence: 99%

“…BUDAPEST with features (1, 2, 3, 4, 5, 7, 9, 10) CLEVELAND with features (1,2,3,5,7,8,9,10) ZÜ RICH with features (1,2,3,5,6,7,9,10) In each subset, we used the first seven features to predict the last one. We used those three subsets in a similar manner as described in the above procedure.…”

Section: Data Integration Vs Classification Integrationmentioning

confidence: 99%

“…We generated the following two subsets from the original data set: DISEASE with features (3,4,5,7,8,9,10,13,14,16,17,20) DRUG with features (3,4,6,7,8,9,10,11,13,16,17,19) Results: In both subsets, we used the first eleven features to predict the last feature. Fig.…”

Section: Reclassification With An Oracle Vs Reclassification With Comentioning

confidence: 99%

“…Finding a single decision tree that contains both patient status and drug efficiency would provide a convenient solution to the above queries. In Section 4, we propose a novel constraint database-based [5,6] reclassification method that results in a single classifier and enables to answer many challenging queries on medical data.…”

Section: Introductionmentioning

confidence: 99%

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Classification integration and reclassification using constraint databases

Révész¹,

Triplet²

2010

Artificial Intelligence in Medicine

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“…Constraint databases [5,6] form an extension of relational databases [8] where the database can contain variables that are usually constrained by linear or polynomial equations.…”

Section: Constraint Databasesmentioning

confidence: 99%

“…Our method does not depend on any particular linear classification method. (2) Join: The linear constraint relations are joined together using a constraint database join operator [5,6].…”

Section: Reclassification With Constraint Databasesmentioning

confidence: 99%

Section: Data Integration Vs Classification Integrationmentioning

confidence: 99%

Section: Reclassification With An Oracle Vs Reclassification With Comentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification integration and reclassification using constraint databases

Révész¹,

Triplet²

2010

Artificial Intelligence in Medicine

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Aggregate Operators in Constraint Query Languages

Benedikt¹,

Libkin

2002

Journal of Computer and System Sciences

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We investigate the problem of how to extend constraint query languages with aggregate operators. We deal with standard relational aggregation, and also with aggregates specific to spatial data, such as volume. We study several approaches, including the addition of a new class of approximate aggregate operators which allow an error tolerance in the computation. We show how techniques of M. Karpinski and A. Macintyre (in ''Structures in Logic and Computer Science: A Selection of Essays in Honor of A. Ehrenfeucht,'' Springer Lecture Notes on Computer Science 1261, pp. 162-173, SpringerVerlag, Berlin, 1997) and P. Koiran (in ''FOCS '95,'' pp. 134-141) based on VC-dimension can be used to give languages with approximation operators, but also show that these languages have a number of shortcomings. We then give a set of results showing that it is impossible to get constraint-based languages that admit definable aggregation operators, both for exact operators and for approximate ones. These results are quite robust, in that they show that closure under aggregation is problematic even when the class of functions permitted in constraints is expanded. This motivates a different approach to the aggregation problem. We introduce a language FO+Poly+Sum, which permits standard discrete aggregation operators to be applied to the outputs of range-restricted constraint queries. We show that this language has a number of attractive closure and expressivity properties, and that it can compute volumes of linear-constraint databases.

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Mining Model Trees from Spatial Data

Malerba

Ceci

Appice

2005

Knowledge Discovery in Databases: PKDD 2005

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Abstract. Mining regression models from spatial data is a fundamental task in Spatial Data Mining. We propose a method, namely Mrs-SMOTI, that takes advantage from a tight-integration with spatial databases and mines regression models in form of trees in order to partition the sample space. The method is characterized by three aspects. First, it is able to capture both spatially global and local effects of explanatory attributes. Second, explanatory attributes that influence the response attribute do not necessarily come from a single layer. Third, the consideration that geometrical representation and relative positioning of spatial objects with respect to a reference system implicitly define both spatial relationships and properties. An application to real-world spatial data is reported.

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Introduction

Cited by 30 publications

References 5 publications

Classification integration and reclassification using constraint databases

Classification integration and reclassification using constraint databases

Aggregate Operators in Constraint Query Languages

Mining Model Trees from Spatial Data

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