PAC 2020 Santorin - Stijn Schepers

Join us to learn how to tune your web performance by combining synthetic, real-user, and competitive benchmarking metrics to give you the most complete dataset needed to optimize your site – and beat your competitors. You will learn: -Choosing the right tool for the job -Using competitive benchmarking data -Mine key performance analytics that matter -Putting performance in the context of your business

This document provides recommendations for Splunk platform, dashboard studio, connected experience, and real-world customer use case recordings and readings. It lists specific recording IDs and titles that cover new features in the Splunk cloud platform and data ingestion, data visualization with dashboard studio, using anomaly detection with dashboard studio, enabling mobile access, and examples of how McLaren and a home training rig use Splunk.

Join Raytheon hiring teams that support open needs across North Texas! Attached is an open req list of positions we are looking to fill locally and across the world - stop by and talk to our teams! Thursday, June 21st, 3:00pm-7:00pm Courtyard Marriott 210 E Stacy Rd Allen, TX 75002

The document discusses three major problems in verification: specifying properties to check, specifying the environment, and computational complexity. It then presents several approaches to addressing these problems, including using coverage metrics tailored to detection ability, sequential equivalence checking to avoid testbenches, and "perspective-based verification" using minimal abstract models focused on specific property classes. This allows verification earlier in design when changes are more tractable and catches bugs before implementation.

This document discusses how to prepare a website for holidays and major events by focusing on performance. It recommends taking a continuous improvement approach of analyzing site usage data, testing for performance issues, and monitoring site performance during events. Key steps include studying past events to understand customer impacts, projecting future usage, contingency planning, and building a feedback loop between development, product management, and engineering. The goal is to adopt a culture where performance is a key feature and the site is always being prepared through continuous delivery, instrumentation, and addressing issues before they affect customers.

VerbalizeIt, a human-powered translation platform for businesses, was selected to appear on the popular Shark Tank TV show. Launching a completely revamped website, and recognizing the opportunity to convert six million viewers into customers, VerbalizeIt turned to SOASTA for cloud testing to ensure that their technology held up under the heavy spike in traffic. In this webinar, Kunal Sarda, COO of VerbalizeIt, will be discussing: VerbalizeIt’s road to Shark Tank and SOASTA How quickly they were able to test for the anticipated increase in Website Traffic Samples of user scenarios and tests conducted How web performance bottlenecks were uncovered and fixed Don’t miss this important webinar on performance testing

This document discusses key measurements for testers, including precision vs accuracy, goals for testing (SMART goals), the GQM methodology for defining test goals and questions, and various metrics for evaluating projects, products, and releases such as defect rates and trends. It provides examples of defining test plans and resources needed, tracking reported vs resolved defects, and criteria for determining when a release is ready.

Load testing approaches of the past support application delivery of the past. Times have changed. Today’s leading companies do more testing in less time with higher coverage of their web and mobile applications, everyday. In this webinar you’ll learn: - Why user experience is king - How to do front-to-back performance testing for mobile and web apps - How to deploy web and mobile load tests with global scale and distribution - Live production testing enabled with real-time analysis and control - How real user monitoring drives test creation and guides production testing The time is now to move your testing from the past to the present! Join us for tips and tricks to get you there.

Slides from the July 31st, 2013 webinar "Preparing for Enterprise Continuous Delivery - 5 Critical Steps" by XebiaLabs

Real-time Anomaly Detection for Real-time Data Needs: Much of the world’s data is becoming streaming, time-series data, where anomalies give significant information in often-critical situations. Examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. Are there algorithms up for the challenge? Which are the most capable? The Numenta Anomaly Detection Benchmark (NAB) attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics. These characteristics are formalized in NAB, using a custom scoring algorithm to evaluate the detectors on a benchmark dataset with labeled, real-world time-series data. We present these components, and describe the end-to-end scoring process. We give results and analyses for several algorithms to illustrate NAB in action. The goal for NAB is to provide a standard, open-source framework for which we can compare and evaluate different algorithms for detecting anomalies in streaming data.

These are the slides of my JavaOne presentation. The abstract goes like this: How do companies developing business-critical Java enterprise Web applications increase releases from 40 to 300 per year and still remain confident about a spike of 1,800 percent in traffic during key events such as Super Bowl Sunday or Cyber Monday? It takes a fundamental change in culture. Although DevOps is often seen as a mechanism for taming the chaos, adopting an agile methodology across all teams is only the first step. This session explores best practices for continuous delivery with higher quality for improving collaboration between teams by consolidating tools and for reducing overhead to fix issues. It shows how to build a performance-focused culture with tools such as Hudson, Jenkins, Chef, Puppet, Selenium, and Compuware APM/dynaTrace

This presentation was given at StarWest 2013 in Anaheim, CA and also broadcasted through the Virtual Conference. It shows how important it is to focus on performance throughout continuous delivery in order to avoid the most common performance problem patterns that still cause applications to crash and engineers spending their weekends and nights in a firefighting/war room situation

The FDA is advising use of data standards as early as possible in the study lifecycle. As a result, Data Management centers are using the Study Data Tabulation Model (SDTM) to drive operations from First Patient In till Database Lock. Many tools on the market allow for the creation of SDTM datasets via intuitive user interfaces. However, targeted tools are needed to manage nightly jobs taking care of data source downloads (eCRF, ePRO, Lab, etc), data uploads in a staging database, converting to SDTM and running edit checks before the Clinical Data Manager arrives in the morning.

Accelerating Web and Mobile Testing for Continuous Delivery Automated load and performance testing of your web and mobile apps can ensure quality throughout the application lifecycle. Automated and continuous testing can increase the speed and accuracy of application readiness, and eliminate time-consuming, error-prone manual processes. In this webinar, led by SOASTA experts, you will learn: • How to create a continuous load and performance testing framework • How to trigger testing every time code changes are delivered • How to use TouchTest for mobile apps functional testing • How to use CloudTest for load testing

ESC was founded in 1969 and is the #1 supplier of CEMS software. It has the largest installed base, monitoring over 2,200 units at 600 plants. ESC provides extensive software and services to help customers manage emissions monitoring and reporting requirements. This includes StackVision software, engineering support, training, and responsive customer support. Upcoming events were highlighted to help customers stay up to date on regulations and learn how to optimize their emissions data management and reporting.

Presented at SplunkLive! Munich 2018, gaining insight on both the experience, and the "why" behind the experience.

Presented at SplunkLive! Frankfurt 2018: Monitoring App Experience...And the App Splunk and APM Demo/Customer Stories Key Takeaways

This document discusses using Splunk to gain insights into end user experience and the factors that influence experience. Splunk provides a platform approach to monitor applications across the full technology stack from networks to databases. It can ingest data from various sources, including APM tools, and provide visibility into both instrumented and non-instrumented applications and environments. Splunk also offers predictive analytics capabilities and allows various stakeholders like operations and business teams to access and analyze data. The document demonstrates how Splunk can help organizations improve user experience, application performance, and collaboration between teams.

The document discusses improving web performance at Telefonica Digital through establishing practices like continuous performance integration, monitoring real user behavior, addressing non-functional requirements earlier, and establishing a performance testing culture. It notes current problems like a lack of tools, performance testing late in the process, and negative user feedback. The future involves integrating performance tests earlier, automating reports, and using real user monitoring for faster feedback.

The document discusses performance engineering at Blackboard, including defining key concepts like performance, scalability, and the application performance index (Apdex). It outlines Blackboard's performance engineering process and methodology, including using tools like LoadRunner for testing and establishing performance archetype ratios to measure scalability. Planned performance engineering projects for 2007 are also mentioned, such as virtualization testing and monitoring initiatives.

The document discusses application portfolio management in mining and presents a classification reference model mapping vendor solutions to business capabilities. It begins by covering application portfolio management and the need for an industry reference model given the specialized nature of mining applications. An application classification model is then developed by researching 91 vendors and 323 applications. The model maps applications across 5 levels based on their purpose. Finally, the document explores how the reference model can be used for customer rationalization, analyzing vendor solution spread, and vendor comparison.

Visual Studio provides integrated tools to support DevOps practices like continuous integration, delivery, deployment and monitoring across the development and production environments. It allows teams to plan, develop, test and release applications while optimizing resources, managing technical debt, and gaining insights from evidence in production to refine future work.

CA Application Performance Management (CA APM) 10 brings three all new patent pending features to change the way you triage and diagnose problems in your apps: a task-based perspectives view, an all new timeline that clearly shows the impact of change and differential analysis to reduce noise in automatic alerting. Learn about these features and how they will dramatically streamline your time to resolution. For more information, please visit http://cainc.to/Nv2VOe

The document discusses how to automate performance testing in DevOps. It outlines an automated analysis workflow involving defining metrics, comparing metrics to thresholds and baselines, pattern analysis, and test results. It also discusses script automation, reducing false positives, and integrating different types of performance tests like load, stress, and spike tests. The goal is to automate performance testing to support the rapid delivery cycles of DevOps.

The document provides a resume for Kavitha Srinivasan summarizing her career objective, professional experience, technical skills, education, strengths and work experience. She has over 3 years of experience in application support and maintenance. Her technical skills include languages like C, C#, Java and technologies like SQL Server, Oracle, .NET and she is currently pursuing an MCA degree. She has worked on projects for clients like Royal Bank of Scotland, Agilent Technologies and Walt Disney Parks providing support, maintenance and development services.

This document provides an overview of asset performance management (APM) for oil and gas assets. It discusses how APM can improve efficiency by facilitating better data flow across departments. The document also outlines how APM can be applied at different stages including design, operations, and maintenance. It describes tools like reliability centered maintenance, risk-based inspection, and digital twins that are part of APM. The goal of APM is to optimize asset performance over the entire lifecycle and increase profits.

The document discusses various software testing concepts and terms. It contains 10 short questions with explanations of stress testing, cyclomatic complexity, object oriented testing, regression testing, loop testing vs path testing, client server environment, graph based testing, security testing benefits, characteristics of real-time systems, and benefits of data flow testing. It also includes 4 longer questions about designing test cases, discussing factors for testing a real-time system, testing in a multiplatform environment, and explaining graph based testing in detail.

The document discusses various metrics that can be used to measure different aspects of software quality. It describes McCall's quality factors triangle which identifies key attributes like correctness, reliability, efficiency etc. It then discusses different types of metrics like function-based metrics which measure functionality, design metrics which measure complexity, and class-oriented metrics which measure characteristics of object-oriented design like coupling and cohesion. The document provides examples of metrics that can measure code, interfaces, testing and more.

The document is a curriculum vitae for Amit Fatehchand Jain. It summarizes his professional experience, skills, education, and certifications. He has over 7 years of experience in automation testing and manual web testing. He is certified in principles of life insurance and foundation level testing. His skills include SQL, various programming languages, testing tools like UFT and QC. He has worked on projects for Tata Consultancy Services, Principal Financial Group, and Wipro Technologies testing applications in banking, finance, and insurance domains.

Collection of my ideas on #broadband Services #cxtransformation. I feel #dataandanalytics will definitely help in close loop systems, not only in #networks but also the business processes. #customerexperience is the key for ISPs & CSPs for retention and loyal customer base. The current network are improving the data set availability by using #telemetry #USP and #netconf, but still lot more standardisation is needed in this area, iOAM can be great protocol to implement. The Linux Foundation is also there in data analysis and AI, really thankful to them for democratisation of technology. #PNDA #ACUMOS #aiforeveryone #dataanalytics #closedloop #broadbandnetworks #ftth #NLP #predictiveanalytics #prescriptiveanalytics #analytics #analyticsplatform

Proceedings of the 2015 Industrial and Systems Engineering Research Conference S. Cetinkaya and J. K. Ryan, eds. Use of Symbolic Regression for Lean Six Sigma Projects Daniel Moreno-Sanchez, MSc. Jacobo Tijerina-Aguilera, MSc. Universidad de Monterrey San Pedro Garza Garcia, NL 66238, Mexico Arlethe Yari Aguilar-Villarreal, MEng. Universidad Autonoma de Nuevo Leon San Nicolas de los Garza, NL 66451, Mexico Abstract Lean Six Sigma projects and the quality engineering profession have to deal with an extensive selection of tools most of them requiring specialized training. The increased availability of standard statistical software motivates the use of advanced data science techniques to identify relationships between potential causes and project metrics. In these circumstances, Symbolic Regression has received increased attention from researchers and practitioners to uncover the intrinsic relationships hidden within complex data without requiring specialized training for its implementation. The objective of this paper is to evaluate the advantages and drawbacks of using computer assisted Symbolic Regression within the Analyze phase of a Lean Six Sigma project. An application of this approach in a service industry project is also presented. Keywords Symbolic Regression, Data Science, Lean Six Sigma 1. Introduction Lean Six Sigma (LSS) has become a well-known hybrid methodology for quality and productivity improvement in organizations. Its wide adoption in several industries has shaped Process Innovation and Operational Excellence initiatives, enabling LSS to become a main topic in quality practitioner sites of interest [1], recognized Six Sigma (SS) certification body of knowledge contents [2], and professional society conferences [3]. However LSS projects and the quality engineering profession have to deal with an extensive selection of tools most of them requiring specialized training. To assist LSS practitioners it is common to categorize tools based on the traditional DMAIC model which stands for Define, Measure, Analyze, Improve, and Control phases. Table 1 presents an overview of the main tools that are commonly used in each phase of a LSS project, allowing team members to progressively develop an understanding between realizing each phase’s intent and how the selected tools can contribute to that purpose. This paper focuses on the Analyze phase where tools for statistical model building are most likely to be selected. The increased availability of standard statistical software motivates the use of advanced data science techniques to identify relationships between potential causes and project metrics. In these circumstances Symbolic Regression (SR) has received increased attention from researchers and practitioners even though SR is still in an early stage of commercial availability. The objective of this paper is to evaluate the advantages and drawbacks o ...

This document describes a solution accelerator for monitoring overall equipment effectiveness (OEE) and key performance indicators (KPIs) across multiple manufacturing factories in near real-time. It discusses how the Databricks lakehouse platform can be used to ingest sensor and operational technology data from devices, clean and structure the data, integrate it with data from ERP systems, calculate OEE and other metrics through streaming aggregations, and surface the outcomes through dashboards. The solution implements a data architecture pattern called medallion to incrementally move data from raw to aggregated layers for analysis.

This presentation by Deevid De Meyer outlines how Brainjar uses human-centric design and explainability to create machine learning systems that work together with humans to improve efficiency while reducing error rate.