Tag: group

Feed: SAS Blogs. Author: Paul Gittins. Until recently I’d not appreciated the flurry of hackathons that have occurred over the past few weeks, many with the admirable aim to identify and improve measures to combat the effects of COVID-19 or to better enable organisations (public and private) to cope with its fallout. How can we understand more about the spread of infectious disease? Notable public examples include: At the same time, some commercial organisations are doing much the same thing to address immediate challenges, such as: How to better forecast demand for product lines over the coming months, reflecting the ... Read More

Feed: R-bloggers. Author: Richard Iannone. [This article was first published on RStudio Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. We are extremely excited to have our first release of the gt packageavailable in CRAN! The name gt is short for “grammar of tables” andthe goal of gt is similar to that of ggplot2, serving to notjust to make it easy to make specific tables, but to describe a set ofunderlying components ... Read More

Feed: Planet MySQL; Author: MySQL Performance Blog; In this blog, I want to evaluate Group Replication Scaling capabilities in cases when we increase the number of nodes and increase user connections.For testing, I will deploy multi-node bare metal servers, where each node and client are dedicated to an individual server and connected between themselves by a 10Gb network.Also, I will use 3-nodes and 5-nodes Group Replication setup.Hardware specifications: System | Supermicro; SYS-F619P2-RTN; v0123456789 (Other) Service Tag | S292592X0110239C Platform | Linux Release | Ubuntu 18.04.4 LTS (bionic) Kernel | 5.3.0-42-generic Architecture | CPU = 64-bit, OS = 64-bit Threading | ... Read More

Feed: SAS Blogs. Author: Alyssa Grube. Over the last few weeks, the world has experienced unprecedented events. The current situation with COVID-19 continues to develop rapidly, and it’s requiring parents to think outside the box to solve problems – from home. With their new, sometimes invasive co-workers. Their kids. Parents don’t cease to be parents when their laptops are open, and kids don’t cease to be kids when Mom or Dad have back to back calls. Daily life has been turned upside down and with school and daycare closures, parents are trying to find ways to keep their kids healthy ... Read More

Feed: Actian. Author: Pradeep Bhanot. There are times when financial markets trend up. There are times that markets trend down.  And then there are times that the markets go crazy and the only thing predictable is volatility. When markets encounter these volatile periods, financial services companies become highly reliant on data analytics to determine where the real market forces are coming from and determine how best to react. There are five key analytics capabilities that financial services companies need to perform effectively in a volatile market environment.Connected Data The more data you have available to analyze, the more accuracy you ... Read More

Feed: R-bloggers. Author: Unknown. [This article was first published on Deeply Trivial, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. For the letter G, I’d like to introduce a very useful function: group_by. This function lets you group data by one or more variables. By itself, it may not seem very useful, but it’s great when you start manipulating and summarizing data. That’s because many of the functions applied to data after you ... Read More

Feed: SAS Blogs. Author: SAS Taiwan. 擬定分析主題後，在正式進入分析之前，分析材料的準備是資料探勘中極重要的工作環節，分析者須針對欲研究主題列示一份的變數清單，應依分析目的與假設、研究對象與範圍等列式出研究主題可能的相關變數。 在大型的分析專案，初階資料準備過程，容易產出動輒上百個分析者認為可能相關的變數，這些變數有些可能重要，也可能大部分都無相關，需要進一步一一與目標變數進行分析與檢定，才能確認變數的重要性，描述性統計分析或透過圖形檢視的資料瀏覽程序是一種方法，但對於大量分析變數投入時，則會相對耗時。 針對大量資料的變數篩選，EM則提供 Variable Selection node 快速的鑑別哪些變數對預測模型是具有解釋能力的，協助進行變數化簡的資料處理工作，經過 Variable Selection node 的變數檢定程序，與目標變數無相關、相關性小或變數解釋能力小，變數角色會被設為拒絕(Reject)，被設為拒絕的變數仍會繼續帶入後續節點，但是不會帶入預測分析裡進行模型建置。 Variable Selection node 透過統計檢定的方法來快速找出解釋變數與目標變數間的關係，提供模型變數預篩選功能。 同時可用以進行分析假設的驗證，確認真實資料是否與分析假設相符，若與假設相符，則可進一步得知其重要程度，反之，可能確認該變數與目標變數無關。 但實務上有時我們會對假設認為應該顯著但分析結果卻拒絕的變數，重新進行資料檢視或進行適當的資料處理來改善它的模型解勢力，特別是業務經驗的重要規則，透過檢定卻產生不顯著的結果時，可能是變數資料結構的問題，建議可透過資料轉換，變數切等、或分群方法來強化變數的解釋能力。 因此，變數篩選節點也可以用來檢視變數資料的狀況，提供變數是否需要進行資料處理的參考資訊。 圖一、Variable Selection node參數列 R-Square 各項主要參數選項的說明如下： ● 最大投入變數門檻值設定 (Maximum Variable Number) —設定投入變數的最大門檻值，預設值為3000。 ● 最小相關係數平方值 (Minimum R-Square) —預設值為0.005，若小於預設值則變數角色會被設定為拒絕(Reject)。 ● R2增益門檻設定 (Stop R-Square) —除了利用R2預設值進行變數篩選外，亦可以透過R2增益門檻設定，進行篩選調整，若投入變數的R2增益值低於預設值，該變數的變數角色亦會被設為拒絕，預設值為 0.0005. ● AOV16數值資料類別化 (Use AOV16 Variables) — 將數值資料類別化，透過Use AOV16 Variables 參數設定，將連續變數切分成16等分，AOV16 variables可以處理與目標變數的非線性關係，預設值為No. ● 類別變數水準值化簡 (Use Group Variables) — 運用 Use Group Variables 參數設定進行類別變數水準值化簡，依據投入變數與目標變數間的關係進行最適化簡，預設值為Yes. Chi-Square 各項主要參數選項的說明如下： ● 最小卡方門檻值設定 (Minimum Chi-Square) — 設定類別變數的最小卡方門檻值，預設值為3.84，因為P( chi-square統計量 > 3.84 ) = 0.05，也就是p-value設定在0.05，若分析變數卡方檢定結果其值小於3.84時，該變數的變數角色會被設為拒絕。 ● 連續變數類別化 (Number of Bins) — 將連續變數進行資料級距切割，已進行卡方分析，預設值為50。 Variable Selection node 變數篩選方法 Variable Selection node 變數篩選方法主要透過R2或卡方值的計算結果進行變數過濾。如果目標變數屬名目或順序尺度的變數，EM會產生一個 二元虛擬 的目標變數，而不是使用原來的順序或名義目標變量。在逐步回歸分析設定較低的進入門檻和排除門檻以避免排除可能適合的潛在影響變數。 SAS ... Read More

Feed: AWS Architecture Blog. IntroductionMany cloud-native organizations building modern applications have adopted a microservice architecture because of its flexibility, performance, and scalability. Even customers with legacy and monolithic application stacks are embarking on an application modernization journey and opting for this type of architecture. A microservice architecture allows applications to be composed of several loosely coupled discreet services that are independently deployable, scalable, and maintainable. These applications can comprise a large number of microservices, which often span multiple business units within an organization. These customers typically have a multi-account AWS environment with each AWS account belonging to an individual business ... Read More

Feed: Planet PostgreSQL. April 7, 2020PostgreSQL Parallel query is a big help for large amount of data retrieval. On my project using PostgreSQL 12.2, I found that the feature is enabled in plain SQL but disabled inside PLPGSQL or even SQL function. For years, I rely on user defined function for some considerations. First, to hide sql structure details from front end logic so that (as long as function declaration does not change) the change in internal query statements does not require front-end code rewriting. Second, it is easier to grant execution of function to group or login role while ... Read More

Feed: Teradata Blog. After the 2020 Spring Festival (Chinese New Year), many people returned to work, part of the country’s annual travel rush. In order to prevent confirmed cases of COVID-19 from increasing, the government of China issued strong control policies and measures. Countless community-level officials, police officers, and community volunteers continued working, guarding everyone's safety.China has a population of 1.4 billion people and approximately 450 million trips took place during this holiday. It became important to track the trip information of passengers who newly traveled in or out of areas affected by the epidemic in order to reduce potential ... Read More