Query Join Processing over Uncertain Data for Decision Tree Classifiers

Dr. V. Yaswanth Kumar

Query Join Processing over Uncertain Data for Decision Tree Classifiers

Dr. V. Yaswanth Kumar * §

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§ JNTU, Kakinada.

Query Join Processing over Uncertain Data for Decision Tree Classifiers

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Abstract

Traditional decision tree classifiers work with the data whose values are known and precise. We can also extend those classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty measurement/quantization errors, data staleness, and multiple repeated measurements. Rather than abstracting uncertain data by statistical derivatives, such as mean and median, the accuracy of a decision tree classifier can be improved much if the complete information of a data item is used by utilizing the Probability Density Function (PDF). In particular, an attribute value can be modelled as a range of possible values, associated with a PDF. The PDF function has only addressed simple queries such as range and nearestneighbour queries. Queries that join multiple relations have not been addressed with PDF. Despite the significance of joins in databases, we address join queries over uncertain data. We propose semantics for the join operation, define probabilistic operators over uncertain data, and propose join algorithms that provide efficient execution of probabilistic joins especially threshold. In which we avoid the semantic complexities that deals with uncertain data. For this class of joins we develop three sets of optimization techniques: item-level, page-level, and index-level pruning. We will compare the performance of these techniques experimentally.

References

10 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. V. Yaswanth Kumar. 2012. "Query Join Processing over Uncertain Data for Decision Tree Classifiers". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 12 (GJCST Volume 12 Issue C12).

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GJCST Volume 12 Issue C12

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-C Classification H.2.8

Submission ReceivedDecember 12, 2011
Peer Review Double Blind
Handling Editor
Accepted January 2, 2012
Published August 21, 2012

Version of record

v1.2

Issue date

August 21, 2012

Language

English

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Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Dr. V. Yaswanth Kumar , G. Kalyani (PhD/Dr. count: 1)

View Count (all-time): 442

Total Views (Real + Logic): 4433

Total Downloads (simulated): 261

Publish Date: 2012 01, Sun

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Total Views: 4.4K

Total Downloads: 261

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