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Open Source Used in Influx1.8 Influx 1.9
Open Source Used In Influx1.8 Influx 1.9 Cisco Systems, Inc. www.cisco.com Cisco has more than 200 offices worldwide. Addresses, phone numbers, and fax numbers are listed on the Cisco website at www.cisco.com/go/offices. Text Part Number: 78EE117C99-1178791953 Open Source Used In Influx1.8 Influx 1.9 1 This document contains licenses and notices for open source software used in this product. With respect to the free/open source software listed in this document, if you have any questions or wish to receive a copy of any source code to which you may be entitled under the applicable free/open source license(s) (such as the GNU Lesser/General Public License), please contact us at [email protected]. In your requests please include the following reference number 78EE117C99-1178791953 Contents 1.1 golang-protobuf-extensions v1.0.1 1.1.1 Available under license 1.2 prometheus-client v0.2.0 1.2.1 Available under license 1.3 gopkg.in-asn1-ber v1.0.0-20170511165959-379148ca0225 1.3.1 Available under license 1.4 influxdata-raft-boltdb v0.0.0-20210323121340-465fcd3eb4d8 1.4.1 Available under license 1.5 fwd v1.1.1 1.5.1 Available under license 1.6 jaeger-client-go v2.23.0+incompatible 1.6.1 Available under license 1.7 golang-genproto v0.0.0-20210122163508-8081c04a3579 1.7.1 Available under license 1.8 influxdata-roaring v0.4.13-0.20180809181101-fc520f41fab6 1.8.1 Available under license 1.9 influxdata-flux v0.113.0 1.9.1 Available under license 1.10 apache-arrow-go-arrow v0.0.0-20200923215132-ac86123a3f01 1.10.1 Available under -
Buildbot Documentation Release 1.6.0
Buildbot Documentation Release 1.6.0 Brian Warner Nov 17, 2018 Contents 1 Buildbot Tutorial 3 1.1 First Run.................................................3 1.2 First Buildbot run with Docker......................................6 1.3 A Quick Tour...............................................9 1.4 Further Reading............................................. 17 2 Buildbot Manual 23 2.1 Introduction............................................... 23 2.2 Installation................................................ 29 2.3 Concepts................................................. 41 2.4 Secret Management........................................... 50 2.5 Configuration............................................... 53 2.6 Customization.............................................. 251 2.7 Command-line Tool........................................... 278 2.8 Resources................................................. 289 2.9 Optimization............................................... 289 2.10 Plugin Infrastructure in Buildbot..................................... 289 2.11 Deployment............................................... 290 2.12 Upgrading................................................ 292 3 Buildbot Development 305 3.1 Development Quick-start......................................... 305 3.2 General Documents........................................... 307 3.3 APIs................................................... 391 3.4 Python3 compatibility.......................................... 484 3.5 Classes................................................. -
Gerrit J.J. Van Den Burg, Phd London, UK | Email: [email protected] | Web: Gertjanvandenburg.Com
Gerrit J.J. van den Burg, PhD London, UK | Email: [email protected] | Web: gertjanvandenburg.com Summary I am a research scientist with a PhD in machine learning and 8+ years of experience in academic research. I am currently looking to transition to industry to work on complex, large-scale problems that can have a positive real-world impact. I have extensive experience with machine learning modeling, algorithm design, and software engineering in Python, C, and R. My goal is to use my expertise and technical skills to address ongoing research challenges in machine learning and AI. Research Experience Postdoctoral Researcher — The Alan Turing Institute, UK 2018–2021 • Introduced a memorization score for probabilistic deep generative models and showed that neural networks can remember part of their input data, which has important implications for data privacy • Created a method for structure detection in textual data files that improved on the Python builtin method by 21%. Developed this into a Python package that has received over 600,000 downloads • Developed a robust Bayesian matrix factorization algorithm for time series modeling and forecasting that improved imputation error up to 60% while maintaining competitive runtime • Established the first benchmark dataset for change point detection on general real-world time series and determined the best performing methods, with consequences for research and practice • Collaborated with other researchers to design a unified interface for AI-powered data cleaning tools Doctoral Researcher -
QUARTERLY CHECK-IN Technology (Services) TECH GOAL QUADRANT
QUARTERLY CHECK-IN Technology (Services) TECH GOAL QUADRANT C Features that we build to improve our technology A Foundation level goals offering B Features we build for others D Modernization, renewal and tech debt goals The goals in each team pack are annotated using this scheme illustrate the broad trends in our priorities Agenda ● CTO Team ● Research and Data ● Design Research ● Performance ● Release Engineering ● Security ● Technical Operations Photos (left to right) Technology (Services) CTO July 2017 quarterly check-in All content is © Wikimedia Foundation & available under CC BY-SA 4.0, unless noted otherwise. CTO Team ● Victoria Coleman - Chief Technology Officer ● Joel Aufrecht - Program Manager (Technology) ● Lani Goto - Project Assistant ● Megan Neisler - Senior Project Coordinator ● Sarah Rodlund - Senior Project Coordinator ● Kevin Smith - Program Manager (Engineering) Photos (left to right) CHECK IN TEAM/DEPT PROGRAM WIKIMEDIA FOUNDATION July 2017 CTO 4.5 [LINK] ANNUAL PLAN GOAL: expand and strengthen our technical communities What is your objective / Who are you working with? What impact / deliverables are you expecting? workflow? Program 4: Technical LAST QUARTER community building (none) Outcome 5: Organize Wikimedia Developer Summit NEXT QUARTER Objective 1: Developer Technical Collaboration Decide on event location, dates, theme, deadlines, etc. Summit web page and publicize the information published four months before the event (B) STATUS: OBJECTIVE IN PROGRESS Technology (Services) Research and Data July, 2017 quarterly -
VES Home Welcome to the VNF Event Stream (VES) Project Home
VES Home Welcome to the VNF Event Stream (VES) Project Home This project was approved May 31, 2016 based upon the VNF Event Stream project proposal. In the meantime the project evolved and VES is not only used by VNF but also by PNF (physical network functions). However, the term "VES" is established and will be kept. Next to OPNFV and ONAP also O-RAN, O-RAN-SC and 3GPP are using VES. The term "xNF" refers to the combination of virtual network functions and physical network functions. Project description: Objective: This project will develop OPNFV platform support for VNF and PNF event streams, in a common model and format intended for use by Service Providers (SPs), e.g. in managing xNF health and lifecycle. The project’s goal is to enable a significant reduction in the effort to develop and integrate xNF telemetry-related data into automated xNF management systems, by promoting convergence toward a common event stream format and collection system. The VES doc source, code, and tests are available at: OPNFV github (generally updated with 30 minutes of merged commits) OPNFV gitweb To clone from the OPNFV repo, see the instructions at the Gerrit project page Powerpoint intro to the project: OPNVF VES.pptx. A demo of the project (vHello_VES Demo) was first presented at OpenStack Barcelona (2016), and updated for the OPNFV Summit 2017 (VES ONAP demo - see below for more info). The following diagram illustrates the concept and scope for the VES project, which includes: From ONAP a Common Event Data Model for the “VNF Event Stream”, with report "domains" covering e.g. -
CBOR (RFC 7049) Concise Binary Object Representation
CBOR (RFC 7049) Concise Binary Object Representation Carsten Bormann, 2015-11-01 1 CBOR: Agenda • What is it, and when might I want it? • How does it work? • How do I work with it? 2 CBOR: Agenda • What is it, and when might I want it? • How does it work? • How do I work with it? 3 Slide stolen from Douglas Crockford History of Data Formats • Ad Hoc • Database Model • Document Model • Programming Language Model Box notation TLV 5 XML XSD 6 Slide stolen from Douglas Crockford JSON • JavaScript Object Notation • Minimal • Textual • Subset of JavaScript Values • Strings • Numbers • Booleans • Objects • Arrays • null Array ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"] [ [0, -1, 0], [1, 0, 0], [0, 0, 1] ] Object { "name": "Jack B. Nimble", "at large": true, "grade": "A", "format": { "type": "rect", "width": 1920, "height": 1080, "interlace": false, "framerate": 24 } } Object Map { "name": "Jack B. Nimble", "at large": true, "grade": "A", "format": { "type": "rect", "width": 1920, "height": 1080, "interlace": false, "framerate": 24 } } JSON limitations • No binary data (byte strings) • Numbers are in decimal, some parsing required • Format requires copying: • Escaping for strings • Base64 for binary • No extensibility (e.g., date format?) • Interoperability issues • I-JSON further reduces functionality (RFC 7493) 12 BSON and friends • Lots of “binary JSON” proposals • Often optimized for data at rest, not protocol use (BSON ➔ MongoDB) • Most are more complex than JSON 13 Why a new binary object format? • Different design goals from current formats – stated up front in the document • Extremely small code size – for work on constrained node networks • Reasonably compact data size – but no compression or even bit-fiddling • Useful to any protocol or application that likes the design goals 14 Concise Binary Object Representation (CBOR) 15 “Sea Boar” “Sea Boar” 16 Design goals (1 of 2) 1. -
Metrics for Gerrit Code Reviews
SPLST'15 Metrics for Gerrit code reviews Samuel Lehtonen and Timo Poranen University of Tampere, School of Information Sciences, Tampere, Finland [email protected],[email protected] Abstract. Code reviews are a widely accepted best practice in mod- ern software development. To enable easier and more agile code reviews, tools like Gerrit have been developed. Gerrit provides a framework for conducting reviews online, with no need for meetings or mailing lists. However, even with the help of tools like Gerrit, following and monitoring the review process becomes increasingly hard, when tens or even hun- dreds of code changes are uploaded daily. To make monitoring the review process easier, we propose a set of metrics to be used with Gerrit code review. The focus is on providing an insight to velocity and quality of code reviews, by measuring different review activities based on data, au- tomatically extracted from Gerrit. When automated, the measurements enable easy monitoring of code reviews, which help in establishing new best practices and improved review process. Keywords: Code quality; Code reviews; Gerrit; Metrics; 1 Introduction Code reviews are a widely used quality assurance practice in software engineer- ing, where developers read and assess each other's code before it is integrated into the codebase or deployed into production. Main motivations for reviews are to detect software defects and to improve code quality while sharing knowledge among developers. Reviews were originally introduced by Fagan [4] already in 1970's. The original, formal type of code inspections are still used in many com- panies, but has been often replaced with more modern types of reviews, where the review is not tied to place or time. -
Fashion Terminology Today Describe Your Heritage Collections with an Eye on the Future
Fashion Terminology Today Describe your heritage collections with an eye on the future Ykje Wildenborg MoMu – Fashion Museum of the Province of Antwerp, Belgium Europeana Fashion, Modemuze Abstract: This article was written for ‘non-techy people’, or people with a basic knowledge of information technology, interested in preparing their fashion heritage metadata for publication online. Publishing fashion heritage on the web brings about the undisputed need for a shared vocabulary, especially when merged. This is not only a question of multilingualism. Between collections and even within collections different words have been used to describe, for example, the same types of objects, materials or techniques. In professional language: the data often is “unclean”. Linked Data is the name of a development in information technology that could prove useful for fashion collecting institutions. It means that the descriptions of collections, in a computer readable format, have a structure that is extremely easy for the device to read. As alien as it may sound, Linked Data practices are already used by the data departments of larger museums, companies and governmental institutions around the world. It eliminates the need for translation or actual changing of the content of databases. It only concerns ‘labeling’ of terms in databases with an identifier. With this in mind, MoMu, the fashion museum of Antwerp, Belgium, is carrying out a termi- nology project in Flanders and the Netherlands, in order to motivate institutions to accomplish the task of labeling their terms. This article concludes with some of the experiences of this adventure, but firstly elucidates the context of the situation. -
Decentralized Identifier WG F2F Sessions
Decentralized Identifier WG F2F Sessions Day 1: January 29, 2020 Chairs: Brent Zundel, Dan Burnett Location: Microsoft Schiphol 1 Welcome! ● Logistics ● W3C WG IPR Policy ● Agenda ● IRC and Scribes ● Introductions & Dinner 2 Logistics ● Location: “Spaces”, 6th floor of Microsoft Schiphol ● WiFi: SSID Publiek_theOutlook, pwd Hello2020 ● Dial-in information: +1-617-324-0000, Meeting ID ● Restrooms: End of the hall, turn right ● Meeting time: 8 am - 5 pm, Jan. 29-31 ● Breaks: 10:30-11 am, 12:30-1:30 pm, 2:30-3 pm ● DID WG Agenda: https://tinyurl.com/didwg-ams2020-agenda (HTML) ● Live slides: https://tinyurl.com/didwg-ams2020-slides (Google Slides) ● Dinner Details: See the “Dinner Tonight” slide at the end of each day 3 W3C WG IPR Policy ● This group abides by the W3C patent policy https://www.w3.org/Consortium/Patent-Policy-20040205 ● Only people and companies listed at https://www.w3.org/2004/01/pp-impl/117488/status are allowed to make substantive contributions to the specs ● Code of Conduct https://www.w3.org/Consortium/cepc/ 4 Today’s agenda 8:00 Breakfast 8:30 Welcome, Introductions, and Logistics Chairs 9:00 Level setting Chairs 9:30 Security issues Brent 10:15 DID and IoT Sam Smith 10:45 Break 11:00 Multiple Encodings/Different Syntaxes: what might we want to support Markus 11:30 Different encodings: model incompatibilities Manu 12:00 Abstract data modeling options Dan Burnett 12:30 Lunch (brief “Why Are We Here?” presentation) Christopher Allen 13:30 DID Doc Extensibility via Registries Mike 14:00 DID Doc Extensibility via JSON-LD Manu -
Interoperability for the Semantic Web: a Loosely Coupled Mediation Approach Antoine Zimmermann
Interoperability for the Semantic Web: A Loosely Coupled Mediation Approach Antoine Zimmermann To cite this version: Antoine Zimmermann. Interoperability for the Semantic Web: A Loosely Coupled Mediation Ap- proach. Modeling and Simulation. Université Jean Monnet, 2021. tel-03163932 HAL Id: tel-03163932 https://tel.archives-ouvertes.fr/tel-03163932 Submitted on 9 Mar 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. These` d'habilitation Universite´ Jean Monnet Ecole´ doctorale 488 Sciences, Ing´enierieet Sant´e(SIS) Interop´erabilit´epour le Web s´emantique : une approche par m´ediationfaiblement coupl´ee Soutenue publiquement par : Antoine Zimmermann MINES Saint-Etienne´ Membres du jury : Olivier Boissier Professeur, MINES Saint-Etienne´ Tuteur de recherche Pierre-Antoine Champin Ma^ıtrede conf´erenceHDR, Universit´eClaude-Bernard Examinateur J´er^ome Euzenat Directeur de recherche, Inria Examinateur Fabien Gandon Directeur de recherche, Inria Rapporteur Andreas Harth Professeur, Friedrich-Alexander-Universit¨atErlangen-N¨urnberg Rapporteur -
Healing the Fragmentation to Realise the Full Potential of The
HealingHealing thethe fragmentationfragmentation toto realiserealise thethe fullfull potentialpotential of thethe IoTIoT Dr.Dr. DaveDave Raggett,Raggett, W3C/ERCIMW3C/ERCIM email:email: [email protected]@w3.org 2525 JuneJune 20202020 ThisThis talktalk isis supportedsupported byby thethe CreateCreate--IoTIoT CoordinationCoordination andand SupportSupport ActionAction withwith fundingfunding fromfrom thethe EuropeanEuropean CommissionCommission Investments in IoT are at risk! Eric Siow, Director, Open Web Platform Standards and Ecosystem Strategies at Intel: • IoT is a little over 10 years old • Hype has been much greater than present reality • IoT is “biting off more that it can chew” • Trying to address too many markets • Involves too many and mostly uncoordinated SDOs and SIGs 2/14 Key IoT Challenges Facing Smart Cities • Lack of coalescence around a set of complementary standards • Hinders scalability, interoperability and evolution • Need to simplify: prioritise and define requirements • Regional regulatory differences adding to the confusion • Diverse requirements impede scalability of the market • Need regulatory agencies to participate and help with standardisation requirements • Lack of interoperability wastes up to 40% of IoT value1 • Cities and technology partners may waste up to $321 billion by 20252 1. https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world 2. https://machinaresearch.com/news/smart-cities-could-waste-usd341-billion-by-2025-on-non-standardized-iot-deployments/ -
Full-Graph-Limited-Mvn-Deps.Pdf
org.jboss.cl.jboss-cl-2.0.9.GA org.jboss.cl.jboss-cl-parent-2.2.1.GA org.jboss.cl.jboss-classloader-N/A org.jboss.cl.jboss-classloading-vfs-N/A org.jboss.cl.jboss-classloading-N/A org.primefaces.extensions.master-pom-1.0.0 org.sonatype.mercury.mercury-mp3-1.0-alpha-1 org.primefaces.themes.overcast-${primefaces.theme.version} org.primefaces.themes.dark-hive-${primefaces.theme.version}org.primefaces.themes.humanity-${primefaces.theme.version}org.primefaces.themes.le-frog-${primefaces.theme.version} org.primefaces.themes.south-street-${primefaces.theme.version}org.primefaces.themes.sunny-${primefaces.theme.version}org.primefaces.themes.hot-sneaks-${primefaces.theme.version}org.primefaces.themes.cupertino-${primefaces.theme.version} org.primefaces.themes.trontastic-${primefaces.theme.version}org.primefaces.themes.excite-bike-${primefaces.theme.version} org.apache.maven.mercury.mercury-external-N/A org.primefaces.themes.redmond-${primefaces.theme.version}org.primefaces.themes.afterwork-${primefaces.theme.version}org.primefaces.themes.glass-x-${primefaces.theme.version}org.primefaces.themes.home-${primefaces.theme.version} org.primefaces.themes.black-tie-${primefaces.theme.version}org.primefaces.themes.eggplant-${primefaces.theme.version} org.apache.maven.mercury.mercury-repo-remote-m2-N/Aorg.apache.maven.mercury.mercury-md-sat-N/A org.primefaces.themes.ui-lightness-${primefaces.theme.version}org.primefaces.themes.midnight-${primefaces.theme.version}org.primefaces.themes.mint-choc-${primefaces.theme.version}org.primefaces.themes.afternoon-${primefaces.theme.version}org.primefaces.themes.dot-luv-${primefaces.theme.version}org.primefaces.themes.smoothness-${primefaces.theme.version}org.primefaces.themes.swanky-purse-${primefaces.theme.version}