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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

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Supporting Dynamic Quantization for High-Dimensional Data Analytics.

Gheorghi Guzun¹, Guadalupe Canahuate¹

¹Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa 52242.

Proceedings of the Exploredb'17. International Workshop on Exploratory Search in Databases and the Web (4Th : 2017 : Chicago, Ill.)

|May 15, 2018

Summary

This summary is machine-generated.

Traditional distance metrics struggle with high-dimensional data. This study explores bit-sliced indices and introduces Query dependent Equi-Depth (QED) quantization for improved similarity searches and data clustering in big data analytics.

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Area of Science:

Data Science
Computer Science
Machine Learning

Background:

Similarity searches are crucial for exploratory data analysis.
Traditional distance metrics falter with high-dimensional data, limiting their effectiveness.
Indexing is essential for interactive, ad-hoc queries in high-dimensional datasets.

Purpose of the Study:

Investigate the utility of bit-sliced indices for exploratory analytics on high-dimensional big data.
Introduce and evaluate a novel dynamic quantization technique, Query dependent Equi-Depth (QED).
Assess the effectiveness of QED in characterizing high-dimensional similarity and improving classification accuracy.

Main Methods:

Utilized bit-sliced indices for similarity searches and data clustering.
Developed and applied Query dependent Equi-Depth (QED) quantization.
Compared QED performance against traditional distance functions for kNN classification.

Main Results:

Bit-sliced indices show potential for exploratory analytics in high-dimensional big data.
QED quantization effectively characterizes high-dimensional similarity.
QED demonstrated improvements in kNN classification accuracy compared to traditional methods.

Conclusions:

Bit-sliced indices are a viable tool for high-dimensional big data analytics.
QED quantization offers a promising approach for enhancing similarity measures.
The proposed methods improve the accuracy of kNN classification in high-dimensional spaces.