促進因果探勘品質的區塊鏈資料分享平台

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姓名

楊昀傑(Yun-Chieh Yang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

促進因果探勘品質的區塊鏈資料分享平台
(A Blockchain Data Sharing Platform for Promoting the Quality of Causal Rule Mining)

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摘要(中)

本研究選擇去中心化的區塊鏈作為資料分享平台的系統架構，以區塊鏈和智能合約作為管理、協助和監督的角色，利用其固有的優勢，以不可更改的特性，可以追溯、監督平台上的所有動作，降低各參與組織間所需的信任門檻。本研究以較關聯規則探勘(association rule mining)更有目的性的因果規則探勘(causal rule mining)來顯現各組織間多面向和多維度的複雜關係。透過國立中央大學校務資料，來模擬校內組織間資料共享，並以欄位屬性多寡，區分為大規模、中規模和小規模的資料集。另外也透過美國職業籃球的資料，嘗試在不同的領域進行模擬，將這些規模複雜度、廣度有差距的資料進行因果規則分析比較，顯現在有效資料交流過後的資料環境下，結果有何差異。至於資料交流最在意的隱私保護，本研究會設計一個平台方作為管理資料的分享、匯集和分析的樞紐，各方參與分享的組織並不會直接接觸到其他組織的資料，以降低個人或各組織隱私外洩的可能，希望透過這樣的研究來推動整合社會上各領域整體資料的利用價值。最後根據本研究的實驗結果也顯現，將不同組織的資料進行合併，使有目的性的探勘資料集更豐富;這將使分析結果更具意義，奠定更確實的決策支援基礎。

摘要(英)

Our research chooses a decentralized blockchain as the system architecture of the data sharing platform, using blockchain and smart contracts as management, assistance and supervision roles, to take advantage of their inherent advantages to trace all the data on the platform with its immutable characteristics to reduce the level of trust required between participating organizations. Our study uses causal rule mining which is more purposeful than association rule mining, to reveal the multi-faceted and multi-dimensional complex relationships among organizations. Through the data of National Central University, we simulated the data exchange among organizations in NCU, our study decided to distinguish large-scale, medium-scale and small-scale data sets by the number of column attributes. In addition, we also utilize the data from NBA and NCAA to simulate apply in different fields. As for privacy protection, our research will design a platform as a hub for managing data sharing, aggregation, and analysis, and the organizations involved in sharing will not directly participate in the data of other organizations to reduce the possibility of privacy leakage for individuals or organizations. It is hoped that such a study will promote and integrate the value of data utilization in various fields of society. Finally, the experiment showed that combining data from different organizations enriches the purposeful exploration data set; this will make the analysis results more meaningful and provide a more solid basis for decision support.

關鍵字(中)

★ 智能合約
★ 區塊鏈
★ 去中心化
★ 因果規則探勘

關鍵字(英)

★ smart contract
★ blockchain
★ decentralized
★ causal rule mining

論文目次

摘要 II
ABSTRACT III
誌謝 IV
目錄 V
圖目錄 VII
表目錄 VIII
一、緒論 1
1-1. 研究背景及動機 1
1-2. 研究目的 2
1-2-1. 提升各參與組織的信任程度 2
1-2-2. 提升整體資料分析時資料的面向和覆蓋程度 2
1-2-3. 降低隱私資料在進行資料交流時產生隱私洩漏的可能 2
1-2-4. 顯現在不同規模的資料下做分析時，因果規則的結果有何差異 3
二、文獻探討 4
2-1. 區塊鏈 4
2-2. 以太坊 4
2-3. 智能合約 5
2-4. 數據共享的區塊鏈平台 6
2-5. 星際檔案系統 6
2-6. 回顧性隊列研究的因果規則探勘 7
2-7. 區塊鏈 VS 中心化資料庫 8
三、研究設計之系統架構與流程 9
3-1. 系統環境與平台架構 9
3-2. 資料集介紹 9
3-3. 系統開發流程 10
四、研究方法 11
4-1. 定義平台參與角色 11
4-1-1. 平台方 11
4-1-2. 組織方 11
4-2. 資料分享流程 11
4-3. 平台例外處理: 15
4-4. 智能合約設計和平台提供功能 15
4-5. 因果規則規模比較 16
五、系統實作 17
5-1. 資料分享平台的以太坊區塊鏈建立 17
5-2. 智能合約開發與測試 18
5-3. 智能合約開發與部屬 19
5-4. 使用者介面與應用程式開發整合 19
六、實驗結果與討論 20
6-1. 資料分析結果比較 20
6-1-1. 校務研究資料 20
6-1-2. NBA球員資料 23
6-2. 系統設備 26
6-3. 智能合約GAS FEE 28
七、結論 30
八、參考文獻 31

參考文獻

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指導教授

蔡孟峰(Meng-Feng Tsai)

審核日期

2022-7-7

推文