有效率的平行頻繁樣式挖掘與建立語言學習物件樣式之方法

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研究生：	黃俊宏 Chun-Hong HUANG
論文名稱：	有效率的平行頻繁樣式挖掘與建立語言學習物件樣式之方法 Efficient parallel frequent pattern mining and a method for establishing language learning object patterns
指導教授：	呂永和 Yung-Ho Leu
口試委員:	葉耀明 Yao-Ming Yeh 楊維寧 Wei-Ning Yang 陳雲岫 Yun-Shiow Chen 查士朝 Shi-Cho Cha 呂永和 Yungho Leu
學位類別：	博士 Doctor
系所名稱：	管理學院 - 資訊管理系 Department of Information Management
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	80
中文關鍵詞：	條件頻繁樣式集、單層平行頻繁樣式挖掘、多重層分割平行頻繁樣式挖掘、合作學習、動態文字物件化
外文關鍵詞：	CPC, PFIM, SLPFIM, MLPFIM, EDOM
相關次數：	點閱：243 下載：0
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近年來，資料採礦及數據處理是資訊產業界的重要議題，大數據及各種文本資料被廣泛使用，數據的處理耗費大量的處理時間及數據空間，數據處理的效率及數據使用空間的有效利用，一直都是相當重要的研究問題。本論文以三篇研究組成，主要聚焦於平行化頻繁樣式挖掘的效率提昇，以及網頁文本中物件樣式的數據空間減少與物件樣式存取效率的提昇。
第一篇我們發表一個平行侯選頻繁樣式集挖掘演算法(CPC)，此演算法依1-itemsets分割交易資料庫為次交易資料，稱為侯選樣式集，侯選樣式集再經由不同的執行緒同時進行處理，執行緒可採用現存各種FIM演算法。實驗結果顯示，CPC演算法於四核心個人電腦中可提昇原有eclat FP-growth及Apriori演算法2至20倍的效率，而且CPC演算法容易實現，尤其是儲存於資料庫中的資料集。
第二篇我們發表一個多重分割平行頻繁樣式挖掘演算法(MLPFIM)，針對單層分割平行頻繁樣式挖掘演算法(SLPFIM)加以改進，多重分割平行頻繁樣式挖掘演算法將重度耗時1-itemsets的次交易資料集，進行再分割為相對應的次次交易資料集，藉此達到負載平衡，以降低SLPFIM的效能瓶頸。實驗結果顯示，我們的多重分割平行頻繁樣式挖掘演算法，相較於單執行緒FIM演算法可達23倍的效能提昇，相較於單層分割平行頻繁樣式挖掘演算法亦有2.14倍的效能提昇。相對於於稀疏的資料集，MLPFIM對密集資料集的效能提昇較顯著。
第三篇論文則針對網上文本各物件樣式的物件化研究，我們發表一種有效率動態文字物件化方法(Efficient Dynamic Objectization Method，EDOM)提供更直覺的網路文本操作模式，可同時提供字音、字義及註記的功能，此外，句子物件可提供整句的註記、翻譯及發音的功能，註記內容並具備合作學習的功能。本研究透過科技接受模式 (Technology Acceptance Model，TAM) 問卷及實際的試用比較，研究結果顯示，受訪者對EDOM應用於閱讀理解及字彙學習，有相當高的滿意度，試用比較部份，EDOM注釋的易用性顯著高於Google Dictionary，EDOM Annotation的易用性與有用性顯著高於Annotator。EDOM演算法可節省網路文本中物件樣式的儲存空間，減少物件化造成的網路流量增加，並已獲得中華民國發明專利。

In recent years, data mining and data processing are important issues in the information industry. Big data and various text materials are widely used. Data processing consumes a lot of processing time and data space. The efficiency of data processing and the effective use of memory space have always been very important research issues. This thesis consists of three articles. We focus on the improvement of parallel frequent pattern mining and the reduction of data space and access efficiency of object pattern in online texts.
The first article proposed a parallel FIM algorithm for a multi-core computer system. By using the LINQ queries, the proposed algorithm divides the transaction database into frequent 1-itemsets based subsets of transaction database, called the conditional pattern collections. The conditional pattern collections are then processed concurrently by different threads of the computer system using the existing implementations of different FIM algorithms. The experimental results showed that the proposed algorithm achieves 2, 4 and 20 folds of speedup over the existing implementations of the eclat, FP-growth and Apriori algorithms, respectively, in a four-core Intel i7 personal computer system. Moreover, the CPC algorithm is easy to implement, especially the dataset be stored in the database. According to the experiments, the MLPFIM provided better improvements for the dense datasets than those of the sparse datasets.
The second paper presents a parallel frequent itemset mining algorithm on a cluster of personal computers. To facilitate parallel frequent itemset mining, we use prefix path based method to decompose a transactional dataset into its frequent 1-itemset sub-datasets. We called the parallel frequent itemset mining algorithm based on the frequent 1-itemset sub-dataset decomposition the single-level parallel frequent itemset mining algorithm (SLPFIM) in our PC cluster platform. To mitigate the bottleneck caused by time-consuming 1-itemset sub-datasets, we propose a multi-level parallel frequent itemset mining (MLPFIM) algorithm to further decompose the time-consuming 1-itemset sub-datasets into their corresponding sub-sub-datasets. The fine granule of the sub-sub-datasets enhances the load balancing in parallel frequent itemset mining. The experimental results showed that the SLPFIM offered a maximum of 11.9 times speed-ups against the non-parallel execution of the FP-Growth algorithm while the MLPFIM achieved a maximum of 23.1 times speed-ups against the non-parallel execution of the FP-Growth algorithm. The experimental results also showed that the MLPFIM offered a maximum of 2.14 times speed-ups against the SLPFIM.
The third article is aimed at objectizing the patterns in an online text. We proposed an efficient dynamic text objectization method (EDOM) to facilitate intuitive operation on the online texts. EDOM offers a more intuitive operation to access the functions of pronunciation, meaning, and annotation on a word, phrase or sentence. Additionally, EDOM allows cooperative learning between users.
To evaluate the acceptance of the EDOM component by the public, we prepared a technology acceptance model (TAM) questionnaire to acquire the opinion on using the EDOM component and other comparison tools. According to the questionnaire, the respondents were highly satisfied with the EDOM tool in reading comprehension and vocabulary learning. For user's experience, the ease of use of the EDOM Gloss function was significantly higher than that of the Google Dictionary. Furthermore, the ease of use and usefulness of the EDOM Annotation function were significantly higher than that the Annotator. Besides, the EDOM component is very easy to be included into teaching materials. The EDOM algorithm can save the storage space of an object pattern in the online text and reduce the network traffic for transmission of the object. EDOM has acquired an invention patent certificate of the Republic of China, Taiwan.

Index of Contents
論文摘要    II
Abstract    III
Acknowledgements    IV
List of Figures    IX
List of Tables    XI
Chapter1
Introduction    1
1-1 Research motivation    1
1-2 Problem definition    1
1-3 Terms definition    2
1-4 Research method    4
Chapter2
The Conditional Pattern Collection Algorithm for Parallel Frequent Itemset Mining on a Multi-Core Computer System    5
2-1Introduction    5
2-2 Background    7
1. FP-tree     7
2. Language Integrated Query    8
3. Storage format of transaction database    8
2-3 Disk I/O Bottleneck    9
2-4 The conditional pattern collection based algorithm    10
1. Generating conditional pattern collections    10
2. Task scheduling based on conditional pattern collections    14
2-5 Experimental results    16
1. Experimental setup    16
2. Datasets     16
3. Performance comparison    16
4. Execution time of different steps in FIM    18
5. Load Balancing    19
2-6 Discussions    21
1. Complexity reduction by divide-and-conquer     21
2. Disk I/O bottleneck prevention by using multiple disks    22
3. Load balancing by randomization    22
Chapter 3
Multi-Level Dataset Decomposition for Parallel Frequent Itemset Mining on a Cluster of Personal Computers    23
3-1 Introduction    23
3-2. Related work    23
3-3 The proposed algorithms    25
1. Prefix-path based 1-itemset sub-dataset    25
2. Execution time estimation of frequent 1-itemset sub-datasets    27
3. System architecture and the frequent itemset mining algorithm    31
3-4. Experimental setup and experimental results    33
1. Experimental setup    34
2. Experimental results    35
Chapter 4
On the Development and Evaluation of the EDOM Web Reading component    41
4-1 Introduction    41
4-2 Related Work    43
1. Reading and vocabulary learning environment    43
2. Web gloss in CAVL    45
3. Web annotation function    46
4-3 Discussion     46
1. The existing Web gloss    46
2. The existing Web annotation     48
4-4 The EDOM algorithm and its features    49
1. The EDOM data structure    49
2. The EDOM features    51
4-5 Evaluation    52
Chapter 5 Conclusion    58
References    60
Published Works    67
List of Figures
Fig. 2-1 The FP-tree of transaction database in Table 2    8
Fig. 2-2 File format vs Table format of a transaction database    9
Fig. 2-3 Generating frequent 1-itemsets    11
Fig. 2-4 Using LINQ queries to construct the reduced transaction Table    12
Fig. 2-5 The link-path algorithm and the prefix path Table    13
Fig. 2-6 An execution of the Link-path algorithm    14
Fig. 2-7 LINQ query to generate conditional pattern collections    15
Fig. 2-8 The Conditional Pattern Collections (CPCs)    15
Fig. 2-9 Execution time of FP-Growth    17
Fig. 2-10 Execution time of eclat    17
Fig. 2-11 Performance on accidents dataset    17
Fig. 2-12 Disk I/O bottleneck    17
Fig. 2-13 Apriori on T40I10D100K    18
Fig. 2-14 Apriori on the accidents dataset    18
Fig. 2-15 Load balancing by randomization    20
Fig. 2-16 Performance improvement by randomization    21
Fig. 3-1 Frequent1-itemset sub-dataset decomposition of a transactional dataset     27
Fig. 3-2 The logarithmic relationship between the order of a frequent 1-itemset and the     28
Fig. 3-3 Execution time distribution on the Chess dataset    29
Fig. 3-4 Execution time distribution on the Kosarak dataset    29
Fig. 3-5 System architecture    31
Fig. 3-6 The frequent itemset mining algorithm    33
Fig. 3-7 Speed-ups of the MLPFIM and SLPFIM vs. non-parallel FP-Growth    35
Fig. 3-8 The execution times of different algorithms    35
Fig. 3-9 The execution times for different numbers of threads    36
Fig. 3-10 The execution times of the algorithms for different numbers of threads    37
Fig. 3-11 The execution times of the SPFIM and MLPFIM on Kosarak dataset    38
Fig. 3-12 Speed-ups on accidents dataset with support count set at 1700    40
Fig. 4-1 Journey of a vocabulary item    41
Fig. 4-2 Readlang    47
Fig. 4-3 VoiceTube    48
Fig. 4-4 Annotor.org    49
Fig. 4-5 EDOM data structure    50
Fig. 4-6 EDOM Opteration    52
Fig. 4-7 EDOM Gloss compare to Google Dic.    54
Fig. 4-8 EDOM Annotation compare to Annotator    55




List of Tables
Table 1-1 An example of k-itemsets    2
Table 2-1 A transaction database    8
Table 2-2 Execution time with different number of disks    10
Table 2-3 The transaction Table    10
Table 2-4 Experimental environment    16
Table 2-5 The Datasets    16
Table 2-6 Execution time of different steps in FIM    19
Table 2-7 Execution time summary    21
Table 3-1 Execution time comparison of 1-itemset sub-datasets    30
Table 3-2 Execution time analysis of the FP-Growth program on different datasets    31
Table 3-3 Specification of the personal computers    34
Table 3-4 The experimental datasets    34
Table 3-5 Bottleneck analysis    37
Table 3-6 Execution time analysis of a specific thread    39
Table 3-7 The execution times of all the threads on Kosarak dataset    39
Table 4-1 Evaluation on the Ease of Use and Usefulness of EDOM Gloss    53
Table 4-2 Evaluation on Ease of Use and Usefulness of EDOM Annotations    55
Table 4-3 Time of Trial Use of EDOM and Annotator+Google Dic.    56
Table 4-4 Function comparison    56



                                

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