| 摘要: | 根據市場調查機構Gartner分析，目前全球資料量正以每年59%的速度成長，平均每兩年就成長1倍，而資料型態也由傳統的結構型資料轉變為非結構型資料為主，其中70%~80%都是日誌檔案、圖片、影像、感應設備等所產生的非結構型資料，在醫療界，由於全民健保的推動，醫療每月申報的資料，如健保資料庫，及醫學中心級以上的PACS影像資料庫等也都以巨量資料的方式成長，面對如此巨大且快速成長的資料量，傳統的資料處理技術顯得不足，因此(BigData)巨量資料的處理技術開始成熟，目前台灣企業包括醫療界，對巨量資料的關注，還處於摸索階段。
本研究是針對目前NoSQL非關聯式資料庫中，技術較為完整與成熟的MongDB，作為分散式儲存與運算的架構，與Google Maps服務作整合，提供使用者可以由(SaaS)服務，從醫療巨量資料中快速取得醫療資訊分析，協助醫療決策者從海量醫療資料中，取得協助診斷判斷的訊息。並藉由此探討NoSQLDB運算結合GIS地理資訊系統的架構運用在台灣重大傷病的分析與實作。
According to a Gartner report, the current amount of data is growing at an annual rate of 59%, and on average, every two years it doubles. Data type also changes from traditional structured data to predominantly non-structural data, wherein 70% to 80% are the log files, graphics, imaging, sensor devices that produce non-structured data. In the healthcare sector of Taiwan, the National Health Insurance (NHI) produces the huge volume of data from monthly reporting, PACS image database, and medical centers. Moreover, it is rapidly growing, which poses challenges for traditional data processing technology. Therefore, many organizations are trying to find solutions to handle huge amounts of data (Big Data). Therefore, in this study, we used Google Maps framework. We implemented distributed computing that used NoSQL (non-relational databases) and MongDB (distributed storage architecture). This architecture was integrated with the Google Maps framework. The developed system will facilitate speedy access to huge amounts of NHI data (Big data) thereby assisting various healthcare stakeholders, such as doctors, public health professionals, researchers. |
