Tag: Visual Analytics

Advanced Analytics | Analytics | Data Visualization
Carlos Pinheiro 0
Vehicle Routing Problem - A beer distribution example in Asheville

The Vehicle Routing Problem (VRP) algorithm aims to find optimal routes for one or multiple vehicles visiting a set of locations and delivering a specific amount of goods demanded by these locations. Problems related to the distribution of goods, normally between warehouses and customers or stores, are generally considered vehicle routing problems. For this article's example, let’s consider a real (and awesome) brewery that needs to deliver beer kegs to different bars and restaurants throughout multiple locations.

Joon-Hyung Koh 0
누구나 손쉽게 사용 가능한 AI 기반의 시각화 분석

시각화 분석을 위해서는 빅데이터를 활용할 수 있어야 하며, 시각화 및 고급 분석, 셀프 서비스, 리포팅 기능을 갖춰야 합니다. 아울러 데이터 핸들링, 분석, 리포트 생성에 이르는 전 과정에서 인사이트를 확보하고자 하는 모든 이들이 자유롭게 사용할 수 있어야 합니다. SAS AI 기반의 시각화 솔루션은 완전 초보자도 자동 추천과 자동 예측 기능을 사용하여

Advanced Analytics | Analytics | Artificial Intelligence | Data for Good | Data Visualization | Machine Learning
Carlos Pinheiro 0
Mobility tracing: Helping local authorities in the fight against COVID-19

The current state of policy enforcement during an infectious disease pandemic is mostly reactive. Public health officials track changes in active cases, identify hot-spots and enforce containment policies primarily based on geographic proximity. By combining telecommunications data -- which we turn into mobility information -- with public health data of

Advanced Analytics | Data Visualization | Programming Tips
Xavier Bizoux 0
Continuous Integration/Continuous Delivery – Using Python and REST APIs for SAS Visual Analytics reports

With increasing interest in Continuous Integration/Continuous Delivery (CI/CD), many SAS Users want to know what can be done for Visual Analytics reports. In this article, I will explain how to use Python and SAS Viya REST APIs to extract a report from a SAS Viya environment and import it into another environment.

Customer Intelligence | Data Visualization
Andrew Christian 0
How to utilize Customer Lifetime Value with SAS Visual Analytics

Some business models will segment the worth of their customers into categories that will often give different levels of service to the more “higher worth” customers. The metric most often used for that is called Customer Lifetime Value (CLV). CLV is simply a balance sheet look at the total cost spent versus the total revenue earned over a customer’s projected tenure or “life.”

SAS Administrators
Gerry Nelson 0
Where are my Viya files?

When working with files like SAS programs, images, documents, logs, etc., we are used to accessing them in operating system directories. In Viya, many of these files are not stored on the file-system. Let's look at where and how files are stored in Viya, and how to manage them.

Analytics | Data Visualization | Machine Learning | SAS Events
Gregor Herrmann 0
Aus der Praxis: 5 Erkenntnisse zum Thema Data Mining und Machine Learning

Beim diesjährigen SAS Forum Deutschland in Bonn boten Sascha Schubert und ich einige Hands-on-Sessions zu Data Science und Analytics an. Nichts Neues, denken Sie wahrscheinlich. Aber mir sind einige Veränderungen zu vorherigen Events aufgefallen, die meiner Ansicht nach auf einen größeren Umbruch in der analytischen Landschaft verweisen. Hier also meine

Advanced Analytics | SAS Administrators
Gerry Nelson 0
LDAP basics for the SAS Viya administrator

ln SAS Viya, deployments identities are managed by the environments configured identity provider. In Visual SAS Viya deployments the identity provider must be an LDAP (Lightweight Directory Access Protocol)  server. Initial setup of a SAS Viya Deployment requires configuration to support reading the identity information (users and groups) from LDAP. SAS Viya 3.3

Analytics | Data Management | Data Visualization
Georgia Mariani 0
6 examples of data management, reporting and analytics in higher education

Today in higher education, savvy users expect to have the information they need to make data-informed decisions at their fingertips. As such, leaders in institutional research (IR) are under pressure to provide these users with accurate data, reports and analyses. IR has been tasked with transforming data and reports in

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