Integrating data mining with SQL databases: OLE DB for data mining

Netz, Amir; Chaudhuri, Surajit; Fayyad, Usama M.; Bernhardt, Jeff

doi:10.1109/icde.2001.914850

Cited by 45 publications

(33 citation statements)

References 6 publications

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“…In both studies, however, the mining results cannot be explicitly manipulated. Netz et al [2001] and Chaudhuri et al [2002] explore the integration of data mining and relational databases (e.g., adding a classification capability to SQL server via the OLE-DB interface). A classification model M can be created and later populated with different data sets to give predictions, via the so-called prediction joins.…”

Section: Count( * ) R1mentioning

confidence: 99%

“…Subsequently, researchers realized the importance of setting mining in the larger context of knowledge discovery from databases (KDD) involving other components. One key component is how to integrate mining with the underlying database systems [Sarawagi et al 1998;Netz et al 2001;Chaudhuri et al 2002]. As a further step, a few data mining "query languages" have been proposed [Han et al 1996;Meo et al 1996;Imielinski and Virmani 1999], based on ad hoc extensions to SQL.…”

Section: Introductionmentioning

confidence: 99%

“…Having seen the power that mining loop brings to a data mining algebra, we can ask whether there is an alternative to mining loop, that is somehow less powerful. Indeed, there have been several attempts (e.g., [Boulicaut et al 1999;Netz et al 2001]) at incorporating specific data mining tasks into a database system, where the whole task (e.g., frequent itemsets, decision trees, etc.) is captured as a "black box" operator.…”

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confidence: 99%

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Expressive power of an algebra for data mining

Calders

Lakshmanan

et al. 2006

ACM Trans. Database Syst.

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The relational data model has simple and clear foundations on which significant theoretical and systems research has flourished. By contrast, most research on data mining has focused on algorithmic issues. A major open question is "what's an appropriate foundation for data mining, which can accommodate disparate mining tasks." We address this problem by presenting a database model and an algebra for data mining. The database model is based on the 3W-model introduced by Johnson et al. [2000]. This model relied on black box mining operators. A main contribution of this paper is to open up these black boxes, by using generic operators in a data mining algebra. Two key operators in this algebra are regionize, which creates regions (or models) from data tuples, and a restricted form of looping called mining loop. Then, the resulting data mining algebra MA is studied and properties concerning expressive power and complexity are established. We present results in three directions: (1) expressiveness of the mining algebra; (2) relations with alternative frameworks, and (3) interactions between regionize and mining loop.

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Section: Count( * ) R1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Expressive power of an algebra for data mining

Calders

Lakshmanan

et al. 2006

ACM Trans. Database Syst.

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“…To this end, several query languages with data mining extensions have been proposed, such as Mine-Rule [8], DMQL [9], and OLE DB [10]. These languages are designed to generate the rules from the data rather than allow queries over the discovered rules.…”

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confidence: 99%

A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal Patterns

Winarko

Roddick

2008

IEEE Trans. Knowl. Data Eng.

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Abstract-A number of algorithms have been proposed for the discovery of temporal patterns. However, since the number of generated patterns can be large, selecting which patterns to analyze can be nontrivial. There is thus a need for algorithms and tools that can assist in the selection of discovered patterns so that subsequent analysis can be performed in an efficient and, ideally, interactive manner. In this paper, we propose a signature-based indexing method to optimize the storage and retrieval of a large collection of relative temporal patterns.Index Terms-Content-based data mining queries, organizing temporal patterns, signature-based indexing methods.

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“…Although several KDD query languages have been proposed (e.g. Mine-Rule [20], MSQL [16], DMQL of DBMiner [11,23]), few methods have been proposed in the other directions; for instance, OLE DB for Data Mining [29], the Discovery Board system [37], or the system proposed in Ref. [21].…”

Section: Introductionmentioning

confidence: 99%

Efficient storage and querying of sequential patterns in database systems⋆

Νανόπουλος

Zakrzewicz

Morzy

et al. 2003

Information and Software Technology

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The number of patterns discovered by data mining can become tremendous, in some cases exceeding the size of the original database. Therefore, there is a requirement for querying previously generated mining results or for querying the database against discovered patters. In this paper, we focus on developing methods for the storage and querying of large collections of sequential patterns. We describe a family of algorithms, which address the problem of considering the ordering among elements, that is crucial when dealing with sequential patterns. Moreover, we take into account the fact that the distribution of elements within sequential patterns is highly skewed, to propose a novel approach for the effective encoding of patterns. Experimental results, which examine a variety of factors, illustrate the efficiency of the proposed method. q

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Integrating data mining with SQL databases: OLE DB for data mining

Abstract: Abstract

Cited by 45 publications

References 6 publications

Expressive power of an algebra for data mining

Expressive power of an algebra for data mining

A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal Patterns

Efficient storage and querying of sequential patterns in database systems⋆

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