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Stream Processing Pocket Reference: Real-Time Any-Scale Data Processing

為了解決Cargo website的問題，作者Akidau, Tyler,Chernyak, Slava,Lax, Reuven 這樣論述：

Tyler Akidau is principal software engineer at Snowflake. Previously senior staff software engineer at Google, he was the technical lead for the Data Processing Languages & Systems group, responsible for Google’s Apache Beam efforts, Google Cloud Dataflow, and internal data processing tools like Goo

gle Flume, MapReduce, and MillWheel. His also a founding member of the Apache Beam PMC. Though deeply passionate and vocal about the capabilities and importance of stream processing, he is also a firm believer in batch and streaming as two sides of the same coin, with the real endgame for data proce

ssing systems the seamless merging between the two. He is the author of the 2015 Dataflow Model paper and the Streaming 101 and Streaming 102 articles on the O’Reilly website. His preferred mode of transportation is by cargo bike, with his two young daughters in tow.Slava Chernyak is a senior softwa

re engineer at Google Seattle. Slava spent over five years working on Google’s internal massive-scale streaming data processing systems and has since become involved with designing and building Windmill, Google Cloud Dataflow’s next-generation streaming backend, from the ground up. Slava is passiona

te about making massive-scale stream processing available and useful to a broader audience. When he is not working on streaming systems, Slava is out enjoying the natural beauty of the Pacific Northwest.Reuven Lax is a senior staff software engineer at Google Seattle, and has spent the past nine yea

rs helping to shape Google’s data processing and analysis strategy. For much of that time he has focused on Google’s low-latency, streaming data processing efforts, first as a long-time member and lead of the MillWheel team, and more recently founding and leading the team responsible for Windmill, t

he next-generation stream processing engine powering Google Cloud Dataflow. He’s very excited to bring Google’s data-processing experience to the world at large, and proud to have been a part of publishing both the MillWheel paper in 2013 and the Dataflow Model paper in 2015. When not at work, Reuve

n enjoys swing dancing, rock climbing, and exploring new parts of the world.Austin Bennett designs data systems to help move, share, gather insights and develop data products efficiently.

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疫情下航空業的競爭優勢-以中華航空與長榮航空為例

為了解決Cargo website的問題，作者劉宏雄 這樣論述：

疫情下航空業的競爭優勢-以中華航空與長榮航空為例 摘要因疫情影響，全球受到此一疫情海嘯衝擊最嚴重的產業非航空旅遊業莫屬了。本研究以資源基礎理論為架構主軸，對此產業進行深入探討，了解本國航空業者在疫情期間如何利用自身資源優勢，從疫情困境中脫穎而出。本研究採專家訪談的方式進行，訪談對象為七位資深航空專家，並採用半結構性問卷題目來進行資料蒐集。本研究得到以下結論。第一，航空業在資源價值性之黃金航線操作為客運包機、增加貨運營運、擴大載貨空間;在策略聯盟優勢

之實行以共掛班號、外籍旅客臺灣中轉、異業結盟等因應疫情之實務作為來延續企業競爭力。第二，在資源稀少性的部份是以生產技術與經營管理制度兩個方面來做策略管理。生產技術包含了疫苗冷鏈運輸與貨運電子化作業;經營管理制度則是加強飛航安全與實施員工無薪(專案)事假。第三，在資源不可模仿性方面，航空業在疫情防護與設備的策略為員工個人防護裝備的日常供應與全球各場站生物辨識系統的普及化。員工培訓策略方面則是提供員工的充電再訓練計劃。最後，資源不可替代性部份則是針對企業品牌形象的維護。這包含了疫苗運送、環保企業、企業社會責任等良好形象的創造與運用。

Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing

為了解決Cargo website的問題，作者Akidau, Tyler/ Chernyak, Slava/ Lax, Reuven 這樣論述：

Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scienti

sts, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way.Expanded from Tyler Akidau's popular blog posts "Streaming 101" and "Streaming 102", this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of

processing real-time data streams. You'll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax.You'll explore: How streaming and batch data processing patterns compareThe core principles and concepts behind robust out-of-order data processingHow w

atermarks track progress and completeness in infinite datasetsHow exactly-once data processing techniques ensure correctnessHow the concepts of streams and tables form the foundations of both batch and streaming data processingThe practical motivations behind a powerful persistent state mechanism, d

riven by a real-world exampleHow time-varying relations provide a link between stream processing and the world of SQL and relational algebra Tyler Akidau is a senior staff software engineer at Google, where he is the technical lead for the Data Processing Languages & Systems group, responsible for

Google’s Apache Beam efforts, Google Cloud Dataflow, and internal data processing tools like Google Flume, MapReduce, and MillWheel. His also a founding member of the Apache Beam PMC. Though deeply passionate and vocal about the capabilities and importance of stream processing, he is also a firm be

liever in batch and streaming as two sides of the same coin, with the real endgame for data processing systems the seamless merging between the two. He is the author of the 2015 Dataflow Model paper and the Streaming 101 and Streaming 102 articles on the O’Reilly website. His preferred mode of trans

portation is by cargo bike, with his two young daughters in tow.Slava Chernyak is a senior software engineer at Google Seattle. Slava spent over five years working on Google’s internal massive-scale streaming data processing systems and has since become involved with designing and building Windmill,

Google Cloud Dataflow’s next-generation streaming backend, from the ground up. Slava is passionate about making massive-scale stream processing available and useful to a broader audience. When he is not working on streaming systems, Slava is out enjoying the natural beauty of the Pacific Northwest.

Reuven Lax is a senior staff software engineer at Google Seattle, and has spent the past nine years helping to shape Google’s data processing and analysis strategy. For much of that time he has focused on Google’s low-latency, streaming data processing efforts, first as a long-time member and lead o

f the MillWheel team, and more recently founding and leading the team responsible for Windmill, the next-generation stream processing engine powering Google Cloud Dataflow. He’s very excited to bring Google’s data-processing experience to the world at large, and proud to have been a part of publishi

ng both the MillWheel paper in 2013 and the Dataflow Model paper in 2015. When not at work, Reuven enjoys swing dancing, rock climbing, and exploring new parts of the world.

以SOR模型探討智能旅店之內容行銷對入住意願之影響

為了解決Cargo website的問題，作者江宜臻 這樣論述：

觀光旅宿業的蓬勃發展可以從70年代後期發現，當時有許多熱門的民宿與飯店崛起。近幾年來也有不少新型態的旅店爆紅，像是膠囊旅館、智能旅店等，更有Airbnb、Agoda等共享平台竄起，使消費者有更多選擇。在眾多旅店中，結合先進科技與未來感的旅店非智能旅店莫屬，機器人手臂、智慧門鎖等智能設備服務都讓消費者在住宿時增添一份新奇感。在未實體進入智能旅店前，消費者是透過那些內容資訊來決定是否入住呢？在瀏覽網頁中的內容資訊時，又會產生怎樣的情緒反應？諸多內容行銷資訊中，一定有特別吸引消費者的因素，讓消費者願意透過內容資訊就決定消費並入住。本研究藉由SOR模型與內容行銷因素來設計問卷，針對瀏覽過智能旅店之官

方網站或官方帳號內容的消費者進行問卷調查。最後共收回435份有效問卷，統整後再使用SmartPLS 3來執行問卷資料的統計分析。研究結果顯示，內容行銷之內容可信度、內容娛樂性與社會互動功能對功能價值與情緒價值呈正面顯著影響，功能價值與情緒價值對入住意願與內容分享也呈正面顯著影響，而功能價值與情緒價值於內容行銷要素對入住意願與內容分享間存在部分中介效果。建議未來智能旅店業者在要更新其官方網站或官方帳號之內容時，除了思考如何融入功能價值與情緒價值以外，可優先從滿意度與直接效果較高之內容娛樂性與社會互動功能著手，增加消費者對入住意願與內容分享之意願。