臺灣博碩士論文加值系統

有別於傳統關聯式資料庫需依賴JOIN才能進行跨表單查詢的作法，非關聯式資料庫（NoSQL：Not Only SQL）具有Schema-free的資料儲存特性與Sharding的資料分片機制，因此適合被用來處理巨量資料。此外，依據Shard Key設定值執行Sharding，將巨量資料切割成小範圍區塊來加速查詢速度。即便如此，就我們所知，目前尚未有一個成熟且系統化的方式管理（包含檢索與視覺化）巨量健保資料。因此，本研究以病患歸人檔之文件導向方式儲存巨量健保資料，並探討健保資料庫所提供的欄位屬性，歸納出12項在診斷治療上的重要欄位，將此選定為Shard Key進行Sharding並執行目標查詢（Targeted Query）以提高檢索效率。並以一範例進行查詢時間效能測試，據實驗結果顯示，本研究所提出的資料處理方法確實能大幅度地縮短巨量的健保資料的查詢時間。

NoSQL (Not Only SQL) database has schema-free data format and the function of sharding. Comparing the NoSQL database with the relational database, the NoSQL database is more suitable to handle the big data. The big data is sharding into small blocks which are based on shard keys to speed up queries answering. To our knowledge, there is not yet a mature and systematic approach (including retrieval and visualization) to managing the big data derived from the National Health Insurance Research Database. Therefore, our research used patient document-oriented way to average the big medical data storage and to explore the field properties of the health insurance research database. By summarizing 12 important fields as shard keys in theranostics, before executing target queries can improve search efficiency. According to our experimental results, it shows that the proposed method of data processing can indeed significantly reduce the big data query time.

致謝 i
中文摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究流程 3
第二章 文獻探討 5
2.1 NoSQL資料庫 5
2.2 MongoDB 6
2.2.1 BSON格式 7
2.3 以健保資料為題材之相關研究 8
第三章 研究設計 13
3.1 系統架構 13
3.2 研究材料 15
3.3 研究方法 17
3.3.1 資料重製（Reformulation） 17
3.3.2 資料前處理 19
3.3.3巨量健保資料儲存格式 22
3.3.4資料分片（Sharding） 24
3.3.5選擇合適的Shard Key 26
3.3.6目標查詢（Targeted Query） 27
第四章 系統實作與展示 28
4.1 系統實作環境 28
4.2 系統功能 29
4.3 查詢效能測試 30
4.4 系統展示 32
4.4.1 情境說明 32
4.4.2 情境搜尋 33
第五章 結果與討論 43
參考文獻 45

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