PPT-Performance Metrics and Performance Engineering

Steve Chenoweth RHIT Above They look ready to perform but why are they sitting in the audience seats What is performance Its both of How fast and Capacity how many Usually a combination . Survey . Results. With help from Marty . Klubeck. at Notre Dame and Brenda . Osuna. at USC. Who are we?. Brown. Carnegie Mellon. Columbia . Cornell . CU. -Boulder . Duke . University . Georgetown . V. S. Somayazulu (v.srinivasa.somayazulu@intel.com). Hassnaa Moustafa (. hassnaa.moustafa@intel.com). Problem Statement. Video Quality Perception . is critical for end-user . QoE. . Topic has been discussed in WG, but no conclusion yet on including this in . Statisitcs. API. draft-huang-xrblock-rtcweb-rtcp-xr-metrics-02. Rachel Huang (rachel.huang@huawei.com). Varun Singh(. varun@comnet.tkk.fi. ). Roni Even (. roni.even@mail01.huawei.com. ). Dan Romascanu (dromasca@avaya.com). Visible impressions: . . Above the fold impressions plus scroll-downs. Benefit. :. . E. valuate media buy and empower rate negotiations. Above the fold. Below the fold. Visibility . Metrics. . Number of recorded impressions=500,000. Nick Mattar. Director of Marketing. Detroit Regional Chamber. “The marketing of products or services using digital channels to reach consumers, including channels that do NOT require the use of the internet.”. Loss Episode Metrics for IPPM. Nick Duffield, . Al Morton. , AT&T. Joel Sommers, Colgate University. IETF 79, Beijing, 11/8/2010. Agenda. Changes since IETF 77. One page summary of draft. Q&A from last IETF. Don McGreal. don.mcgreal@improvingenterprises.com. @. donmcgreal. . linkedin.com. /in/. donmcgreal. Agenda. Types of Metrics. Problems with Metrics. Metrics in Software Development. Evidence-Based Management. Survey . Results. With help from Marty . Klubeck. at Notre Dame and Brenda . Osuna. at USC. Who are we?. Brown. Carnegie Mellon. Columbia . Cornell . CU. -Boulder . Duke . University . Georgetown . Robert N Hatton. A2ST Solutions Architect. 256-319-8403. (w) . robert.n.hatton@saic.com. 31 July – 2 August 2017. Overview. Trainers across all industries are keenly focused on delivering . engaging . 1. LESSON OUTCOME:. This lesson provides an overview of doctrinal responsibilities, philosophies, and objectives for . applying HR Metrics in they day to day operations. . At the conclusion of this block of instruction, students will be able to . metrics. Group level. Density. Components. Isolates. Cliques. Centralization. Degree. Closeness. Betweenness. Factions. Core-periphery. Node level. Centrality. Degree. Indegree. Outdegree. Closeness. Rouven Krebs (SAP AG). , Christof Momm (SAP AG), Samuel Kounev (KIT). SPEC RG Cloud. , . May . 2012. Isolation . and Shared Resources. provides. Service Provider. High overhead, low utilization .  need to share. Performance Metrics for Graph Mining Tasks 1 Outline Introduction to Performance Metrics Supervised Learning Performance Metrics Unsupervised Learning Performance Metrics Optimizing Metrics Statistical Significance Techniques for Automatic Seizure Detection. V. Shah. I. Obeid. J. Picone. Y. Roy. G. Ekladious. R. Iskander. Abstract. Standardized databases and evaluation metrics accelerate research and technology development by enabling direct comparisons of research results..