SQL online的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列包括價格和評價等資訊懶人包

Practical Fraud Prevention: Fraud and AML Analytics for Fintech and Ecommerce, Using SQL and Python

為了解決SQL online的問題，作者Saporta, Gilit,Maraney, Shoshana 這樣論述：

Gilit Saporta is an experienced fraud-fighting leader with well over a decade of experience in the field. Having begun her career at Fraud Sciences, the startup famous for pioneering innovative accuracy in online fraud prevention, during the course of her career she has led and trained fraud analyst

s at PayPal, Forter, and Simplex. She is currently a lecturer at and a content contributor to the Tel Aviv University MBA Fintech Fraud Course, is on the steering committee of the annual CyberWeek FraudCon Conference, co-hosts Fraud Fighters’ Israeli meetups and leads the Israeli branch of RiskSalon

. She is an experienced conference speaker and has contributed to many fraud, risk, and payments publications.Shoshana Maraney is an experienced writer, with more than a decade of experience in expressing complex concepts, technologies, and techniques in comprehensible and relatable language. She ha

s worked in the fraud prevention industry for more than five years, at first Forter and now Identiq. She is a content committee member of the Tel Aviv University CyberWeek FraudCon Conference, and has created numerous presentations for risk and payments conferences around the world. She has composed

many articles for fraud, risk, and payments publications. Her degree is from Cambridge University.

SQL online進入發燒排行的影片

基於非揮發性記憶儲存系統壽命提升之高效能 索引管理策略

為了解決SQL online的問題，作者唐 吉 這樣論述：

近年來非揮發性記憶體已逐漸成熟，而其良好的特性（如：高儲存密度、低靜態功耗等）為大數據儲存系統開闢了新的可能性。然而，非揮發性記憶體的耐久度與傳統記憶體相比十分有限，甚至持續惡化，因此利用磨損平衡技術來增加非揮發性記憶體的壽命已成為非揮發性存儲系統設計中的一個主要問題。更糟的是，由於嵌入式系統的資料管理系統通常使用索引方案來維護小數據，這使得非揮發性記憶體的耐久度問題更加嚴重；也就是說非揮發性記憶體的壽命在嵌入式系統中很快就會耗盡。因此，許多先前的文獻著重於重新思考基於非揮發性記憶體的系統上的索引和資料管理方案。而先前多數的研究主要集中在減少記憶體和儲存裝置中的寫入次數及降低寫入放大問題。不

幸的是，僅考慮寫入次數並無法有效延長非揮發性記憶體的使用壽命，因為這樣的解決方案無法在設備中均勻分配寫入流量。基於這樣的觀察，本研究為非揮發性記憶體和固態硬碟系統提出了兩種替代索引方案，分別為waB+tree 以及waLSM-tree。其中waB+tree 的設計考慮了B+-tree 結構內每個節點的更新頻率，從而將寫入流量均勻地分散到非揮發性記憶體的單元中。另一方面，waLSM 則考慮LSM 樹中的每個級別之間的不同數據溫度，進而在整個固態硬碟中均勻擦除所有塊。根據我們的實驗結果，本研究所提出的索引方案可有效提升裝置的耐久度，進而延長裝置的壽命

SQL Server on Kubernetes: Designing and Building a Modern Data Platform

為了解決SQL online的問題，作者Nocentino, Anthony E.,Weissman, Ben 這樣論述：

​Anthony E. Nocentino is the Founder and President of Centino Systems as well as a Pluralsight author, a Microsoft Data Platform MVP, and an industry-recognized Kubernetes, SQL Server, and Linux expert. In his consulting practice, Anthony designs solutions, deploys the technology, and provides exper

tise on system performance, architecture, and security. He has bachelor’s and master’s degrees in computer science, with research publications in machine virtualization, high performance/low latency data access algorithms, and spatial database systems.Ben Weissman is the owner and founder of Solisyo

n, a consulting firm based in Germany and focused on business intelligence (BI), business analytics, and data warehousing. He is a Microsoft Data Platform MVP, the first German BimlHero, and has been working with SQL Server since SQL Server 6.5. Ben is also an MCSE, Charter Member of the Microsoft P

rofessional Program for Big Data, Artificial Intelligence and Data Science, and he is a Certified Data Vault Data Modeler. If he is not currently working with data, he is probably travelling to explore the world. You can find him online at @bweissman on Twitter.

應用集群分析於精準行銷之研究-以企業軟體為例

為了解決SQL online的問題，作者陳思妤 這樣論述：

隨著訂閱授權並交付軟體的 SaaS（Software as a Service，簡稱 SaaS）軟體即服務出現，預測模型的應用將可以為企業軟體業者提升競爭力。企業軟體業在目標客戶的預測上，常常面臨資料蒐集不易之困境。倘若能依循零售業的方式，利用資料庫中的顧客購買紀錄，作為預估未來市場的決策依據。本研究採用 RFM指標中三項指標進行顧客價值之兩階段集群分析，再運用 CART 決策樹將客戶進行分析，建構出預測模型，進而探討各集群間的差異性。透過透過 UCI 公開資料庫的某英國批發零售商銷售總筆數 530108 之交易資料，建立預測模型，分析該企業的顧客特徵值。根據結果，給予企業軟體業者、廣告業者

以及後續相關領域參考。茲將本研究重要發現分述如下：一、精準行銷與廣告策略為正相關，行銷目標在於消費者體驗上能更進階，同時降低廣告成本並創造更高的收益，最終進行付費購買。二、RFM 模型與兩階段集群分析將線上零售商客戶進行分群，從客戶變動的消費行為對其產生特徵值標籤後，將顧客分為「高消費型客戶」、「潛力型消費型客戶」、「流失型客戶」等三種類型。三、建立模型方面，使用「分類與回歸數」（Classification and Regression Tree，簡稱 CART）決策樹算法建構模型，結果發現決策樹的顯著度為 95 %，顯示決策樹能提供對應的解釋規則。