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[爆卦]Parameterized是什麼？優點缺點精華區懶人包

雖然這篇Parameterized鄉民發文沒有被收入到精華區：在Parameterized這個話題中，我們另外找到其它相關的精選爆讚文章

在 parameterized產品中有2篇Facebook貼文，粉絲數超過2,018的網紅Kewang 的資訊進化論，也在其Facebook貼文中提到， 剛在整理資料的時候看到這個忘了回在哪個討論串的回應，重新整理一下分享給大家好了。這是個關於到底用 ORM 還是 raw SQL 的宗教戰爭。 --- 這個 project 小編一開始是用 ORM，但才過個兩天就轉回 raw SQL 了。原因是有些 table name 有參數，用 ORM 的寫法...

　同時也有2部Youtube影片，追蹤數超過12萬的網紅prasertcbs，也在其Youtube影片中提到，สอนวิธีการดึงข้อมูลจาก view ใน MySQL มายัง Excel โดยใช้ Microsoft Query พร้อมกับวิธีการกำหนดเงื่อนไขการดึงข้อมูลผ่านทางพารามิเตอร์...

「parameterized」的推薦目錄
  • Kewang 的資訊進化論

    parameterized 在 Kewang 的資訊進化論 Facebook 的精選貼文

    2020-06-08 01:45:01
    有 13 人按讚

    剛在整理資料的時候看到這個忘了回在哪個討論串的回應，重新整理一下分享給大家好了。這是個關於到底用 ORM 還是 raw SQL 的宗教戰爭。

    ---

    這個 project 小編一開始是用 ORM，但才過個兩天就轉回 raw SQL 了。原因是有些 table name 有參數，用 ORM 的寫法會比較不直覺。

    所以小編在寫 raw SQL 的時候就會遇到幾個問題，像是參數如何傳到 SQL 語法裡面。這時候語言層的 string interpolation 跟 SQL 層的 parameterized 就很好用。

    但如果有用到 string interpolation 的話就一定要處理 SQL injection 的問題，還有就是 return 的時候是 cursor 還是實際的資料，這都是要自己去想清楚的。

    不過說到底就是看場景而定，小編自己現在是比較偏好寫 raw SQL 就是了。畢竟 raw SQL 有 performance issue 的時候，還比較好調整。

    * image from https://live.staticflickr.com/4030/4228947679_76ea6eff50_b.jpg

    #sql #orm

  • 國立陽明交通大學電子工程學系及電子研究所

    parameterized 在 國立陽明交通大學電子工程學系及電子研究所 Facebook 的最佳解答

    2014-04-23 05:13:31
    有 2 人按讚


    【Talk】Stochastic Simulation, Modeling & Optimization of Complex Systems via Parameterized Model Reduction

    Welcome all to join it !!!

    Time : 10:30am-12:00pm, April 29, 2014
    Venue : ED528, Engineering Building 4, National Chiao Tung University

    Prof. Luca Daniel
    Massachusetts Institute of Technology

    Abstract
    Many complex systems developed by engineers (e.g. iPads, sensor body networks, power delivery networks, magnetic resonance imaging machines) or found in nature (e.g. the human cardiovascular system, or the geophysical oil/water/gas reservoir networks) can be viewed as large collections of interconnected dynamical system components The performance or characteristics of each individual component critically depend on what engineers or scientist refer to as “second order effects”, and can be captured only by resorting to accurate partial differential equation descriptions (e.g. Poisson, Maxwell, Navier-Stokes equations etc…). In addition, such components are often affected by random uncertainties in parameters and in geometries. In the first part of this talk I will illustrate how recent advances in computational techniques have made it possible to quantify efficiently and accurately the effect of second order effects and random uncertainties in individual system components. In the second part of this talk I will show how parameterized model order reduction techniques are beginning to enable the efficient simulation, design and optimization of “entire” complex networks of interconnected dynamical systems. Examples of complex systems analysis and optimization will be presented from the electrical engineering world including network of integrated circuit interconnect, RF inductors, micro-electro-mechanical sensors, low noise RF amplifiers, and power amplifiers, as well as city/state wide power distribution grids. At the end of the talk I will outline how the same stochastic field solver and parameterized compact dynamical modeling techniques used for designing complex electronic systems can be used to handle complex systems in other fields (e.g. to control undesired local heat deposition in human tissues by the RF power used in high resolution MRI machines, or to diagnose diseases of the human cardiovascular system, or to enable water/oil/gas reservoir exploration.)

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