| 姓名 |
烏莉雅(Lailatul Ulya)
查詢紙本館藏 |
畢業系所 |
資訊工程學系 |
| 論文名稱 |
基於網頁 MCES 分析系統的分析過程加速
(Acceleration of Analysis Process of a Web-Based Micro-Crack Analytic System)
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| 相關論文 | |
| 檔案 |
 [Endnote RIS 格式]
[Bibtex 格式]
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| 摘要(中) |
現今，幾乎每一家公司都利用科技來幫助提升他們的利潤。Micro Crack Estimator System (MCES) 是一種能幫助管理者得知一周內生產線上哪台機器良率最低導致產生最多破片的軟體設計。MCES是一種網站平台，因此使用者可以使用它進行遠端分析。但是，目前所碰上的問題是網頁處理時間太耗時，舉例來說: MCES網站在分析第一周數據時花費了9.09分鐘，然而到了第六十五周更是需要花費16.29分鐘才能分析完成。這種長時間分析的等待時間會導致使用者失去能立即查看分析結果的樂趣。
利用改進MCES分析功能中的某些部份，以減少Python程式的處理時間。在本研究中，嘗試了九種方法去改進它，而其中的兩種方法更是有顯著的加速效果。像是以第六十五周的結果來比較，處理時間從原本的16.29分鐘大幅的減少到1.88分鐘。原始結果與加速分析結果之間的準確度差異僅有0.2%。根據Mann-Kendall test, 由於網站處理時間並不會隨著資料量的大小上升或下降，因此加速版本比原始版本更具有可擴展性。 |
| 摘要(英) |
Nowadays, almost every company uses technology to help them increase their profit. Micro Crack Estimator System (MCES) is one of the software that designed to help the manager to know which production machine that has the lowest production rate while also produces the highest bad pieces in a week. MCES take website as it is platform, so user can analyze the data remotely. The problem is, currently MCES website spends 9.09 minutes for 1st week and increasing more until 16,29 minutes for 65th week. This long-time waiting make user lost their interest to check the results immediately after it is analyzed.
There are some parts of the MCES analyze feature than can be improved, to cut the python processing time. Nine approaches have been tried on this study, and two of them give a significant result to speed up the time process. The time process decreased from 16,29 minutes to 1,88 minutes for the 65th week data. The accuracy difference between the original and speed up analysis results is only 0.2%. The speed up version is also more scalable than the original version as neither upward or downward monotonic trend exists, according to the Mann-Kendall test. |
| 關鍵字(中) |
★ 加速
★ Python 處理時間
★ Django 網站
★ 可擴展性
★ 平行化 |
關鍵字(英) |
★ speed up
★ python processing time
★ django website
★ scalability
★ parallelization |
| 論文目次 |
摘要 iv
ABSTRACT v
Acknowledgment vi
TABLE OF CONTENTS vii
LIST OF TABLES xi
I. Introduction 1
1.1. Background 1
1.2. Motivation 2
1.3. Research Objective 4
1.4. Contribution 4
1.5. Thesis Structure 5
II. Literature Review 6
2.1. Django Web Framework 6
2.2. Mann-Kendall Test 8
2.3. Parallelization 9
III. MCES System Overview 11
3.1. MCES System Overview 11
3.1.1. Pre-processing Part 14
3.1.2. EM Algorithm Part 15
3.1.3. Post-processing Part 17
3.2. Issues on Analysis Feature MCES v1.2 18
3.2.1. Inefficient work flow process 18
3.2.2. Recursive part on post-processing 20
3.2.2.1. Inefficient I/O Data Write 20
3.2.2.2. Slower Database I/O When the Database Size Increase 21
3.2.2.3. Specific Parts on Post-Processing Take a Lot Time 21
IV. Research Method 23
4.1. Methodology 23
4.2. Proposed Solution 24
4.2.1. Inefficient work flow process 24
4.2.1.1. Paralleling the Pre and EM Process 24
4.2.1.2. Redesign EM Algorithm as EM v1.2 24
4.2.1.3. Redesign the Pre-processing v1.3 26
4.2.2. Inefficient I/O Data Write 26
4.2.2.1. Only Save Specific Changed Period to Database 27
4.2.2.2. Save Results Directly to Database using cursor.executemany() Django 27
4.2.3. Slower Database I/O When the Database Size Increase 27
4.2.3.1. Change MySQL Temp Directory from Disk to RAM 27
4.2.4. Specific Parts on Post-Processing Take a Lot Time 27
4.2.4.1. Paralleling the Post-processing 27
4.2.4.2. Use Csv File to Substitute 8 Tables on Database 28
4.2.5. Other Solutions 28
4.2.5.1. Use Better Machine 28
V. Experiments and Results 30
5.1. Experiments 30
5.1.1. Paralleling the Pre and EM Process 30
5.1.2. Redesign EM Algorithm as EM v1.2 31
5.1.3. Only Save Specific Changed Period to Database 31
5.1.4. Save Results Directly to Database using cursor.executemany() Django 32
5.1.5. Change MySQL Temp Directory from Disk to RAM 32
5.1.6. Paralleling the Post-processing 33
5.1.7. Use Csv File to Substitute 8 Tables on Database 34
5.1.8. Redesign the Pre-processing v1.3 34
5.1.9. Use Better Machine 34
5.1.10. Results 34
5.2. Final Results 35
5.2.1. Time 35
5.2.2. Accuracy 37
5.2.3. Trend 37
VI. Conclusion 40
6.1. Conclusion 40
6.2. Suggestion 40
Bibliography 41 |
| 參考文獻 |
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[9] Peck, R. 2014. Statistics: Learning from Data, Preliminary Edition. Brooks/Cole. Boston, USA
[10] Dauzon, S., Bendoraitis, A., Ravindran, A. 2016. Django: Web Development with Python. Packt Publishing. Birmingham, UK.
[11] Django Official Website. June 2009. [Online]. Available at: http://www.djangoproject.com.
[12] Pohlert, T. 2020. Non-Parametric Trend Tests and Change-Point Detection. [Online]. Available at: https://cran.r-project.org/web/packages/trend/vignettes/trend.pdf
[13] Project Description pyMannKendall. 2020. [Online]. Available at: https://pypi.org/project/pymannkendall/
[14] JaJa, J. 1997. An Introduction to Parallel Algorithms. Addison Wesley. USA
[15] Best Practice of Bulk_Create for Massive Records. [Online]. Available at : https://stackoverflow.com/questions/32805766/best-practice-of-bulk-create-for-massive-records
[16] Myers, D.G. 2010. Social psychology (Tenth ed.). McGraw-Hill Higher Education. New York.
[17] Optimizing the MySQL Server. [Online]. Available at : https://dev.mysql.com/doc/refman/8.0/en/optimizing-server.html
[18] Pandas is Faster to Load CSV than SQL. [Online]. Available at : https://stackoverflow.com/questions/43874559/pandas-is-faster-to-load-csv-than-sql/50809892 |
| 指導教授 |
梁德容
張欽圳(Deron Liang
Chin-Chun Chang)
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審核日期 |
2020-7-28 |
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