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UDAS : big data analysis system focusing on data refinement and reusability

UDAS : big data analysis system focusing on data refinement and reusability

Material type
학위논문
Personal Author
최현진
Title Statement
UDAS : big data analysis system focusing on data refinement and reusability / Hyunjin Choi
Publication, Distribution, etc
Seoul :   Graduate School, Korea Unversity,   2019  
Physical Medium
viii, 117장 : 삽화, 도표 ; 26 cm
기타형태 저록
UDAS : Big Data Analysis System focusing on data refinement and reusability   (DCOLL211009)000000083085  
학위논문주기
학위논문(박사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2019. 2
학과코드
0510   6YD36   358  
General Note
지도교수: 백두권  
부록: 1. Detailed figures, 2. Built-in templates in UDAS  
Bibliography, Etc. Note
참고문헌: 장 75-83
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
Data analysis, Data visualization, Statistical analysis, Clouds, Data refinement, R,,
000 00000nam c2200205 c 4500
001 000045978968
005 20190416164339
007 ta
008 181226s2019 ulkad bmAC 000c eng
040 ▼a 211009 ▼c 211009 ▼d 211009
041 0 ▼a eng ▼b kor
085 0 ▼a 0510 ▼2 KDCP
090 ▼a 0510 ▼b 6YD36 ▼c 358
100 1 ▼a 최현진
245 1 0 ▼a UDAS : ▼b big data analysis system focusing on data refinement and reusability / ▼d Hyunjin Choi
246 1 1 ▼a 데이터 정제와 재사용에 중점을 둔 빅데이터 분석 시스템
260 ▼a Seoul : ▼b Graduate School, Korea Unversity, ▼c 2019
300 ▼a viii, 117장 : ▼b 삽화, 도표 ; ▼c 26 cm
500 ▼a 지도교수: 백두권
500 ▼a 부록: 1. Detailed figures, 2. Built-in templates in UDAS
502 1 ▼a 학위논문(박사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2019. 2
504 ▼a 참고문헌: 장 75-83
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a Data analysis, Data visualization, Statistical analysis, Clouds, Data refinement, R
776 0 ▼t UDAS : Big Data Analysis System focusing on data refinement and reusability ▼w (DCOLL211009)000000083085
900 1 0 ▼a Choi, Hyun-jin, ▼e
900 1 0 ▼a 백두권, ▼e 지도교수
900 1 0 ▼a Baik, Doo-kwon, ▼e 지도교수
945 ▼a KLPA

Electronic Information

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UDAS : big data analysis system focusing on data refinement and reusability (43회 열람)
View PDF Abstract Table of Contents
No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6YD36 358 Accession No. 123060855 Availability Available Due Date Make a Reservation Service B M
No. 2 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6YD36 358 Accession No. 123060856 Availability Available Due Date Make a Reservation Service B M
No. 3 Location Sejong Academic Information Center/Thesis(5F)/ Call Number 0510 6YD36 358 Accession No. 153081449 Availability Available Due Date Make a Reservation Service M
No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6YD36 358 Accession No. 123060855 Availability Available Due Date Make a Reservation Service B M
No. 2 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6YD36 358 Accession No. 123060856 Availability Available Due Date Make a Reservation Service B M
No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Sejong Academic Information Center/Thesis(5F)/ Call Number 0510 6YD36 358 Accession No. 153081449 Availability Available Due Date Make a Reservation Service M

Contents information

Abstract

As the digital economy based on information and communication technology (ICT) spreads, people's behavior as well as their thoughts are accumulated. Every time we use mobile devices and web services, a large amount of data is being generated, such as the digital footprint. Data that are the key to the digital economic value, but flooding of unclear data can lead to confusion for users. Over the past years, research on big data analysis has been actively conducted, and many services have been developed to find valuable data. However, low quality of raw data and data loss problem during data analysis make it difficult to perform accurate data analysis. With the enormous generation of both unstructured and structured data, refinement of data is becoming increasingly difficult. As a result, data refinement plays an important role in data analysis. In addition, as part of efforts to ensure research reproducibility, the importance of reuse of researcher data and research methods is increasing; however, the research on systems supporting such roles has not been conducted sufficiently. Therefore, in this dissertation, we propose a big data analysis system named Unified Data Analytics Suite (UDAS) that focuses on data refinement. UDAS performs data refinement based on the big data platform and ensures the reusability and reproducibility of refinement and analysis through the visual programming language interface. It also recommends open source and visualization libraries to users for statistical analysis. The qualitative evaluation of UDAS using the functional evaluation factor of the big data analysis platform demonstrated that the average satisfaction of the users is significantly high.

Table of Contents

ABSTRACT	i

1. Introduction	1
1.1 Research Motivation	1
1.2 Research Purpose	4
1.3 Research Taxonomy	5
1.4 Organization of the Dissertation	8

2. Related Works	9
2.1 Data Analysis Tools	9
2.2 Visual Programming Language	16

3. Design of UDAS	19
3.1 The architecture of UDAS	19
3.2 The reusability of data refinement functions in UDAS	22
3.3 The components of UDAS	24
3.3.1 ER Designer	24
3.3.2 Query Designer	24
3.3.3 Mapping Designer	25
3.3.4 Data Dictionary	25
3.3.5 Data Domain Component	26
3.4 Statistical Analysis and Visualization	26
3.5 Template for Refinement and Analysis	27

4. Implementation of UDAS	34
4.1 Data Collection	34
4.2 Data Refinement	36
4.2.1 Refinement Model	37
4.2.2 Analysis Model	38
4.2.2.1 ER Designer	38
4.2.2.2 Query Designer	41
4.2.2.3 Mapping Designer	41
4.3 Data Analysis	43
4.3.1 Analysis Model Designer	44
4.3.2 Visualization Component	44
4.3.3 Statistical function recommendation	45
4.4 Templates for refinement and analysis	54

5. Experiment and Evalution	56
5.1 Application Scenario	56
5.2 Analysis Results	58
5.3 Qualitative Evaluation	60

6. Conclustion	72
6.1 Conclusion	72
6.2 Future Work	73

Bibliography	75
Annex I Detailed Figures	84
Annex II Built-in templates in UDAS	103
Abstaract (Korean)	116