For the EFL teachers' training in China.
thanks for Andy's presentation version in English. the Chinese version translated by John Wu, from Shaoguan, China
johnwuchina@gmail.com
For the EFL teachers' training in China.
thanks for Andy's presentation version in English. the Chinese version translated by John Wu, from Shaoguan, China
johnwuchina@gmail.com
Injection of Herbicides into Rhizomes of Knotweeds and Other Invasive SpeciesJohn Lampe
We ran trials on this method for a few years as did other organizations and individuals. Ultimately, we did not find its efficacy to outweigh the labor involved. Therefore, Green Shoots decided not to launch the product. We have however had real success with foliar applications using our Green Shoots Foam Herbicide Dispenser. John Lampe gave a presentation on that system at the 2014 Upper Midwest Invasive Species Conference: http://www.slideshare.net/johnlampe/how-to-kill-i.
Building New Hampshire Awards of Excellence 2010Bearbradley
The 2010 Building New Hampshire Awards of Excellence were presented at the first annual Building New Hampshire Trade Show and Conference on November 4th. The awards were presented to builders for their outstanding achievements in green and sustainable building of new or remodeled residential and commercial structures in NH.
The Cloud Computing China Congress (CCCC http://www.cloudcomputingchina.org ) is specially designed for senior IT and line of business executives evaluating and making purchasing decisions in the areas of on-demand infrastructure and software services.
趨勢科技董事長張明正表示:「100年前人類是以發電機自行產生所需的電力,經過20年後由電力公司成為電力的提供者,用戶只需要依照使用量支付電費即可,不需要花費龐大資金自行建置發電廠,而雲端運算正是基於同樣的概念產生。」張明正董事長接著說明:「雲端運算分成三種不同面向,分別是Software as a Service(SaaS)、Platform as a Service(PaaS)、Infrastructure as a Service(IaaS),企業可以依照經營型態決定導入順序。」
Opening Keynote for HadoopCon 2014
我們的身邊、網路上,圍繞著太多的 Big Data 論述與技術,Hadooper 今天聚集在這裡,都已經是 Big Data 的相關利益者,然而, 今天我們所理解的 Big Data,大部分都是透過自身的體驗而來,但 Hadoop Ecosystem 太過龐雜,Use Case 不同,必須取不同的 OSS 專案來完成,如此想來,我們哪一個人何曾看過所有的 Big Data 風景呢?
此 Talk 告訴我們如何透過更多的風景之窗,將 Big Data 的不同天地,看得更多更透。
Summary of Insights Learned from the Data Science Program Team TrainingFred Chiang
Who really has the skills and talents to leverage the most value out of data? The Data Science Program (DSP) was co-founded by Code for Tomorrow and Etu. We believe that building and deploying a data science team consisting of members who possess and have the ability to utilize their different skill sets from a variety of industries is more practical and realistic. Versus hoping to find an individual data scientist who is an expert in a wide variety of technical fields ranging from math, statistics, and visualizations, as well as a solid background in other fields such as business, communication, and etc. The Data Science Program has identified four pertinent categories to place our members into. These four categories are Campaigner, Data Analyst, Data Hygienist, and Designer. Each team will have these four categories filled. During the training every team learns how to do data processing, data analysis, and visualization together with the sole purpose to use these skills to solve a common problem. After four weeks of intensive study, each team comes up with enterprise-grade team projects demonstrating the innovation of data-driven businesses.
After two rounds of DSP Team Training, DSP has accumulated 10 team projects and has graduated more than 60 alumni who are passionate about data science. During this journey of developing and deploying teams trained in data science, the most valuable aspects we walked away with was the witnessing of members growing in confidence from the learning and experience, the building of team work, and the overall growth of each individual. At the end of the day, our hope of as members of DSP, including myself is to instill and motivate more people to devote themselves to the exploration of data science. Now think about how you can do the same.
Big Data 102 - Crossovers 成長之旅導覽 (Keynote for Big Data Taiwan 2013)Fred Chiang
總結阻礙企業導入 Big Data 解決方案的因素,除了大環境的景氣因素,其餘幾乎可歸結為對「價值」與「技術」的不確定與不熟悉。此場將帶領大家預覽 Big Data Taiwan 2013 整天的內容精華,具體說明 Big Data 的「價值」洞見與展現,「技術」養成與發展,配合戰略探討與驅動,以降低企業的不確定感,協助數據價值策略的發展。
ESD 2012 Keynote: What Is the next Big Data?Fred Chiang
This is my keynote slides for Etu Solution Day 2012 which was held on Dec, 20, 2012 @Taipei, Taiwan. I had summarized the market status of Big Data in Taiwan and predicted the trend in 2013.
2012.05.24 於 「Big Data Taiwan 2012」的 Keynote 講稿。
主講者:Etu 副總經理/ 蔣居裕
《議題簡介》
無論是企業區域網路,還是開放的網際網路,在巨大的結構化與非結構化資料的背後,其實充滿著各種行為意圖,以及人、事、物、時、地的多維度關聯。商業的日益競爭,已經來到了一個除了講求行銷創意,還要擁有巨量資料處理與分析技術,才能出奇制勝的時代。有人形容 Big Data 的價值挖掘,就像是在攪拌混凝土,若在尚未完成前就中斷,將導致前功盡棄,全無可用的窘境。對 Big Data 的意圖與關聯探索,必須是 End-to-End 全程的照料,方得實現。本議程將舉例說明這個有序到永續的過程,讓聽者更能領略意圖與關聯充滿的世界。
6. 雲端服務的三種交付模式 (Delivery Models)
Software as a Service (SaaS)
• 軟體即服務
Platform as a Service (PaaS)
• 平台即服務
Infrastructure as a Service (IaaS)
• 基礎架構即服務
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12. IaaS 與 PaaS 的差異
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Hypervisor
Hypervisor
Hypervisor
Hypervisor
Virtualization Management Cloud Application Framework
Run-time RDB Big Table
Index Search Memcache
Scaling &
Elasticity
Control
PaaS:運算資源加總合一
IaaS:運算資源切割分離
程式性 API Set
管理性 API Set RESTful
26. 創新並演進著的雲端運算
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雲端運算相關服務或技術 創新 演進
零資本支出創業 (Zero-CAPEX Startup) V
服務導向商業模式 (Service-oriented Biz Model) V
服務規模快速伸縮性 (Rapid Elasticity on Capacity) V
運算資源預備性 (Resource Pooling, Provisioning) V
虛擬化技術 (Virtualization) V
虛擬化管理技術 (Virtualization Management) V
分散式運算 (Distributed Computing) V
雲端程式設計模型 (Cloud-based App Design Patten) V
程式設計語言 (Programming Language) V
雲端程式執行環境 (Cloud-based App Run-time Environment) V
多租戶系統架構 (Multi-tenant System Architecture) V
貨櫃式資料中心 (Container-based Datacenter) V
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Copyright 2010 TCloud Computing Inc.
雲端運算概念性的定義
Cloud computing is a model for enabling ubiquitous,
convenient, on-demand network access to a shared pool of configurable
computing resources (e.g., networks, servers, storage, applications, and
services) that can be rapidly provisioned and released with minimal
management effort or service provider interaction.
Definition from the latest draft of the NIST Working Definition of Cloud Computing,
published by the U.S. Government's National Institute of Standards and Technology
雲端運算是服務模式,而不是特定技術。
• 雲端運算服務必須透過網路存取。
• 雲端運算共享資源,可以被使用者快速取得與釋放
29. 雲端運算的特徵
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source: Cloud Computing Use Case Discussing Group (http://groups.google.com/group/cloud-computing-use-cases)
1.快速伸縮性 (Rapid Elasticity)
2. 計量服務性 (Measured Service)
3. 按需自我服務性 (On-Demand Self-Service)
4. 普遍網路存取性 (Ubiquitous Network Access)
5. 資源預備性 (Resource Pooling, Provisioning)
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30. 雲端運算 [特徵]×[交付模式] 矩陣
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Service
Provider
特 徵
(IT/Developer)
IaaS
(Developer)
PaaS
(End-User)
SaaS
Provider
家裡事
資源預備性
(Resource Pooling, Provisioning)
V V V
快速伸縮性
(Rapid Elasticity)
V V V
Provider
家外事
計量服務性
(Measured Service)
V V V
按需自我服務性
(On-Demand Self-Service)
V V V
普遍網路存取性
(Ubiquitous Network Access)
- - ?
公 有 雲
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36. 雲端服務巨人對 OSS 的 Take & Give
Cloud Service Provider Major Open Source Software Used or Contributed
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37. OSS 對雲端運算發展的重要性
1. 降低大規模雲端服務的成本
想像一下,如果 Google、Amazon、
Twitter、或 Facebook 必須付 OS、
middleware、與 hypervisor 的授權
費,金額會是多少?
2. 全球社群一起來創新
Open Source Community for One,
One for the Open Source Community
3. 避免 Vendor Lock-in
如果有必要,可以自行修改 Source
Code。
Company Number of Server
Google 1,000,000+
Amazon 200,000
Facebook 30,000
Rackspace 56,671
Akamai 61,000
Refer to: http://gizmodo.com/5517041/googles-
insane-number-of-servers-visualized
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