Theoretical Analysis of Wavelet Synopsis on Partitioned Data Sets

Author

Chulyun Kim

Author_Institution

Dept. of Software Design & Manage., Gachon Univ., Seongnam, South Korea

fYear

2013

fDate

24-26 June 2013

Firstpage

1

Lastpage

2

Abstract

Wavelet synopsis is one of most popular dimensionality reduction methods and has been studied in various areas such as query optimization, approximate query answering, feature selection, etc. Currently, the size of data becomes much larger and the distributed data processing is increasingly important. The MapReduce well known as Google´s data processing environment is the most popular distributed platform with good scalability and fault tolerance. Thus, recently, the algorithms to construct wavelet synopses on the MapReduce platform were proposed. In this paper, we theoretically analyze wavelet synopsis on partitioned data sets. Although the wavelet synopsis on partitioned data sets was proposed in recent work, only the algorithmic implementation and experimental results were given but there was no theoretical analysis. Thus, we study theoretical analysis of the properties of wavelet synopsis on partitioned data sets and the correctness of merging them.

Keywords

data compression; data mining; distributed processing; fault tolerant computing; wavelet transforms; Google data processing environment; MapReduce; MapReduce platform; algorithmic implementation; dimensionality reduction methods; distributed data processing; distributed platform; fault tolerance; partitioned data sets; theoretical wavelet synopsis analysis; Approximation algorithms; Approximation methods; Data processing; Distributed databases; Time complexity; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Science and Applications (ICISA), 2013 International Conference on

Conference_Location

Suwon

Print_ISBN

978-1-4799-0602-4

Type

conf

DOI

10.1109/ICISA.2013.6579454

Filename

6579454