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什麼是多媒體系統 ======== [![hackmd-github-sync-badge](https://hackmd.io/MiyStAQsTOSPbBXF1OKyIg/badge)](https://hackmd.io/MiyStAQsTOSPbBXF1OKyIg) - 多媒體系統Multimedia System是指在電腦應用系統中,組合兩種或兩種以上媒體的一種人機互動式資訊交流和傳播媒體,本篇將在[系列文第一天](https://ithelp.ithome.com.tw/articles/10233234)提到的4個主軸簡要說明: - 資料的類型。 - 資料輸入/輸出,以及中間過程的處理程序與方式(實踐IPO流程)。 - 處理過程資料ETL。 - 資料探索EDA。 資料的類型 ----- - 結構化資料 - 如整理好的資料庫、excel試算表等數據資料,因為有一定格式脈絡,也是最方便進行數據分析的呈現方式,相關格式包含Excel可開啟的`.xsl`、以逗點區隔的`.csv`等;資料庫又可分為關聯式資料庫SQL、非關聯式資料庫NoSQL。 - 半結構化資料 - **XML**:可延伸標記式語言(Extensible Markup Language,簡稱:XML),可以彈性自訂義標籤作為資料傳輸運用,參閱[wiki](https://zh.wikipedia.org/wiki/XML)。 - **JSON**:JavaScript物件表示法(JavaScript Object Notation),同樣用於資料交換,其精簡的結構符合現代網路資訊傳遞媒介的需求,參閱[wiki](https://zh.wikipedia.org/wiki/JSON)。 - 非結構化資料 - 文字: 相關格式如`.txt`、`.doc`等。 - 圖片: 相關格式如`.jpg`、`.bmp`、`.png`、`.gif`、`.gif`等。 - 聲音: 相關格式如`.wav`、`.mp3`、`.m4a`等。 - 影像: 相關格式如`.mp4`等。 - 資料的5V特性:**大**、**快**、**雜**、**疑**與**價值** - 大數據或巨量資料的名詞紅極一時,就像現在無處不AI,但大數據的定義真的只是指資料量大嗎?整理到最後約可歸納為5V特性,可以做為系統設計時參考: - Volume-大量,表示巨量資料規模量大,傳統的資料庫系統已無法負荷。 - Velocity-快速,表示處理的時效,針對資料使用情境的即時性處理需求。 - Variety-多樣,指的是資料的形態多樣,包含資料庫、XML 文件、文字、影音、串流、…等等結構性、半結構性、非結構性的資料。 - Veracity-真實性,指的是這些資料是否可靠、品質是否足夠、或是結構是否完整,亦即可信度是否充足。 - Value-價值,是巨量資料處理流程開始與結束都會牽涉到的特性,透過巨量資料處理產生更多有價值的資料。 - 上述5V配合時代演進、計算能力、傳輸能力及分析能力的變化,可預見在2020年起的5G時代,所謂的大、快、雜、疑與價值又有一番新思維。 資料輸入/輸出,以及中間過程的處理程序與方式(實踐IPO流程)。 -------------------------------- - 資料會有輸入(input)、輸出(output),中間要有處理(Process)的過程,就是IPO啦,這樣的資訊處裡流程在各種知識領域作法不同,畫出來的圖形也各有巧思,萬變不離其宗。 - 以聊天機器人而言,使用者輸入(input)文字/語音,經過後台處理(Process),再將結果輸出(output)為文字/圖片/影音/地圖等,一個系統就完成啦。可以把精神留在如何符合客戶需求及使用情境,設計能提供使用者價值的服務。 資料ETL。 ------ - 資料ETL,是指資料從來源端經過萃取(extract)、轉置(transform)、載入(load)至目的端的過程(參閱[wiki](https://zh.wikipedia.org/wiki/ETL))。如何以多種格式從多個來源收集資料,並將其移至一或多個資料存放區,這樣的資訊流是需要設計的,可參閱[微軟的介紹](https://docs.microsoft.com/zh-tw/azure/architecture/data-guide/relational-data/etl)有較詳盡的說明,並引用下圖做參考。![](https://docs.microsoft.com/zh-tw/azure/architecture/data-guide/images/etl.png) - 本系列文章會略為觸及相關知識,在實作時依照需求設計即可。 資料探索EDA ------- - 探索式資料分析(Exploratory Data Analysis)是當面對資料時,不用馬上把頭洗下去,而是先藉由基本統計、視覺化的觀察理解手上資訊的輪廓,也是資料驅動(data drivin)思維的一種行動方式,或許可以從資料中挖掘問題或發現有趣的可能性。常用的觀察方式包含眾數、四分位數、標準差、散佈圖、趨勢圖、盒鬚圖等,透過上述做發檢測與觀察異常值、缺失值、變量間的關係、建立基本假設等。 - 本系列文章的後續會提及作法,並結合聊天機器人輸出圖片。 小結 -- - 經過對多媒體系統的各種資料類型、處理流程IPO、資料ETL及資料探索EDA初步認識,也是對建立系統觀念的簡易梳理,下篇我們將進入程式的世界,我們明天見。

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