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Timeline 1994 July Company Incorporated 1995 July Amazon
Timeline 1994 July Company Incorporated 1995 July Amazon.com Sells First Book, “Fluid Concepts & Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought” 1996 July Launches Amazon.com Associates Program 1997 May Announces IPO, Begins Trading on NASDAQ Under “AMZN” September Introduces 1-ClickTM Shopping November Opens Fulfillment Center in New Castle, Delaware 1998 February Launches Amazon.com Advantage Program April Acquires Internet Movie Database June Opens Music Store October Launches First International Sites, Amazon.co.uk (UK) and Amazon.de (Germany) November Opens DVD/Video Store 1999 January Opens Fulfillment Center in Fernley, Nevada March Launches Amazon.com Auctions April Opens Fulfillment Center in Coffeyville, Kansas May Opens Fulfillment Centers in Campbellsville and Lexington, Kentucky June Acquires Alexa Internet July Opens Consumer Electronics, and Toys & Games Stores September Launches zShops October Opens Customer Service Center in Tacoma, Washington Acquires Tool Crib of the North’s Online and Catalog Sales Division November Opens Home Improvement, Software, Video Games and Gift Ideas Stores December Jeff Bezos Named TIME Magazine “Person Of The Year” 2000 January Opens Customer Service Center in Huntington, West Virginia May Opens Kitchen Store August Announces Toys “R” Us Alliance Launches Amazon.fr (France) October Opens Camera & Photo Store November Launches Amazon.co.jp (Japan) Launches Marketplace Introduces First Free Super Saver Shipping Offer (Orders Over $100) 2001 April Announces Borders Group Alliance August Introduces In-Store Pick Up September Announces Target Stores Alliance October Introduces Look Inside The BookTM 2002 June Launches Amazon.ca (Canada) July Launches Amazon Web Services August Lowers Free Super Saver Shipping Threshold to $25 September Opens Office Products Store November Opens Apparel & Accessories Store 2003 April Announces National Basketball Association Alliance June Launches Amazon Services, Inc. -
Getting the Most out of Information Systems: a Manager's Guide (V
Getting the Most Out of Information Systems A Manager's Guide v. 1.0 This is the book Getting the Most Out of Information Systems: A Manager's Guide (v. 1.0). This book is licensed under a Creative Commons by-nc-sa 3.0 (http://creativecommons.org/licenses/by-nc-sa/ 3.0/) license. See the license for more details, but that basically means you can share this book as long as you credit the author (but see below), don't make money from it, and do make it available to everyone else under the same terms. This book was accessible as of December 29, 2012, and it was downloaded then by Andy Schmitz (http://lardbucket.org) in an effort to preserve the availability of this book. Normally, the author and publisher would be credited here. However, the publisher has asked for the customary Creative Commons attribution to the original publisher, authors, title, and book URI to be removed. Additionally, per the publisher's request, their name has been removed in some passages. More information is available on this project's attribution page (http://2012books.lardbucket.org/attribution.html?utm_source=header). For more information on the source of this book, or why it is available for free, please see the project's home page (http://2012books.lardbucket.org/). You can browse or download additional books there. ii Table of Contents About the Author .................................................................................................................. 1 Acknowledgments................................................................................................................ -
A Study on Users' Discovery Process of Amazon Echo
A STUDY ON USERS’ DISCOVERY PROCESS OF AMAZON ECHO DOT’S CAPABILITIES by Lingyi Zhang A Thesis Submitted to the Faculty of Purdue University In Partial Fulfillment of the Requirements for the degree of Master of Science Department of Computer Graphics Technology West Lafayette, Indiana May 2019 THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF COMMITTEE APPROVAL Dr. Paul Parsons, Chair Department of Computer Graphics Technology Dr. Austin L Toombs Department of Computer Graphics Technology Dr. Mihaela Vorvoreanu Department of Computer Graphics Technology Approved by: Dr. Nicoletta Adamo-Villani Graduate Program Co-Chair Dr. Colin Gray Graduate Program Co-Chair Head of the Graduate Program III ACKNOWLEDGMENTS I would like to thank my committee for helping me refine the thesis topic, giving me suggestions on methods and all the other supports. It has been through a long way since my very first research question. I could not complete this thesis without their help. Thank you. IV TABLE OF CONTENTS LIST OF TABLES ........................................................................................................................ VI LIST OF FIGURES ..................................................................................................................... VII ABSTRACT ............................................................................................................................... VIII INTRODUCTION .................................................................................................. 1 1.1 Statement of Problem -
Performance at Scale with Amazon Elasticache
Performance at Scale with Amazon ElastiCache July 2019 Notices Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved. Contents Introduction .......................................................................................................................... 1 ElastiCache Overview ......................................................................................................... 2 Alternatives to ElastiCache ................................................................................................. 2 Memcached vs. Redis ......................................................................................................... 3 ElastiCache for Memcached ............................................................................................... 5 Architecture with ElastiCache for Memcached ............................................................... -
A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk
A Data-Driven Analysis of Workers’ Earnings on Amazon Mechanical Turk Kotaro Hara1,2, Abi Adams3, Kristy Milland4,5, Saiph Savage6, Chris Callison-Burch7, Jeffrey P. Bigham1 1Carnegie Mellon University, 2Singapore Management University, 3University of Oxford 4McMaster University, 5TurkerNation.com, 6West Virginia University, 7University of Pennsylvania [email protected] ABSTRACT temporarily out-of-work engineers to work [1,4,39,46,65]. A growing number of people are working as part of on-line crowd work. Crowd work is often thought to be low wage Yet, despite the potential for crowdsourcing platforms to work. However, we know little about the wage distribution extend the scope of the labor market, many are concerned in practice and what causes low/high earnings in this that workers on crowdsourcing markets are treated unfairly setting. We recorded 2,676 workers performing 3.8 million [19,38,39,42,47,59]. Concerns about low earnings on crowd tasks on Amazon Mechanical Turk. Our task-level analysis work platforms have been voiced repeatedly. Past research revealed that workers earned a median hourly wage of only has found evidence that workers typically earn a fraction of ~$2/h, and only 4% earned more than $7.25/h. While the the U.S. minimum wage [34,35,37–39,49] and many average requester pays more than $11/h, lower-paying workers report not being paid for adequately completed requesters post much more work. Our wage calculations are tasks [38,51]. This is problematic as income generation is influenced by how unpaid work is accounted for, e.g., time the primary motivation of workers [4,13,46,49]. -
Amazon Elasticache Deep Dive Powering Modern Applications with Low Latency and High Throughput
Amazon ElastiCache Deep Dive Powering modern applications with low latency and high throughput Michael Labib Sr. Manager, Non-Relational Databases © 2020, Amazon Web Services, Inc. or its Affiliates. Agenda • Introduction to Amazon ElastiCache • Redis Topologies & Features • ElastiCache Use Cases • Monitoring, Sizing & Best Practices © 2020, Amazon Web Services, Inc. or its Affiliates. Introduction to Amazon ElastiCache © 2020, Amazon Web Services, Inc. or its Affiliates. Purpose-built databases © 2020, Amazon Web Services, Inc. or its Affiliates. Purpose-built databases © 2020, Amazon Web Services, Inc. or its Affiliates. Modern real-time applications require Performance, Scale & Availability Users 1M+ Data volume Terabytes—petabytes Locality Global Performance Microsecond latency Request rate Millions per second Access Mobile, IoT, devices Scale Up-out-in E-Commerce Media Social Online Shared economy Economics Pay-as-you-go streaming media gaming Developer access Open API © 2020, Amazon Web Services, Inc. or its Affiliates. Amazon ElastiCache – Fully Managed Service Redis & Extreme Secure Easily scales to Memcached compatible performance and reliable massive workloads Fully compatible with In-memory data store Network isolation, encryption Scale writes and open source Redis and cache for microsecond at rest/transit, HIPAA, PCI, reads with sharding and Memcached response times FedRAMP, multi AZ, and and replicas automatic failover © 2020, Amazon Web Services, Inc. or its Affiliates. What is Redis? Initially released in 2009, Redis provides: • Complex data structures: Strings, Lists, Sets, Sorted Sets, Hash Maps, HyperLogLog, Geospatial, and Streams • High-availability through replication • Scalability through online sharding • Persistence via snapshot / restore • Multi-key atomic operations A high-speed, in-memory, non-Relational data store. • LUA scripting Customers love that Redis is easy to use. -
Amazon Mechanical Turk Developer Guide API Version 2017-01-17 Amazon Mechanical Turk Developer Guide
Amazon Mechanical Turk Developer Guide API Version 2017-01-17 Amazon Mechanical Turk Developer Guide Amazon Mechanical Turk: Developer Guide Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Amazon Mechanical Turk Developer Guide Table of Contents What is Amazon Mechanical Turk? ........................................................................................................ 1 Mechanical Turk marketplace ....................................................................................................... 1 Marketplace rules ............................................................................................................... 2 The sandbox marketplace .................................................................................................... 2 Tasks that work well on Mechanical Turk ...................................................................................... 3 Tasks can be completed within a web browser ....................................................................... 3 Work can be broken into distinct, bite-sized tasks ................................................................. -
Dspace Cover Page
Human-powered sorts and joins The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Adam Marcus, Eugene Wu, David Karger, Samuel Madden, and Robert Miller. 2011. Human-powered sorts and joins. Proc. VLDB Endow. 5, 1 (September 2011), 13-24. As Published http://www.vldb.org/pvldb/vol5.html Publisher VLDB Endowment Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/73192 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike 3.0 Detailed Terms http://creativecommons.org/licenses/by-nc-sa/3.0/ Human-powered Sorts and Joins Adam Marcus Eugene Wu David Karger Samuel Madden Robert Miller {marcua,sirrice,karger,madden,rcm}@csail.mit.edu ABSTRACT There are several reasons that systems like MTurk are of interest Crowdsourcing marketplaces like Amazon’s Mechanical Turk (MTurk) database researchers. First, MTurk workflow developers often imple- make it possible to task people with small jobs, such as labeling im- ment tasks that involve familiar database operations such as filtering, ages or looking up phone numbers, via a programmatic interface. sorting, and joining datasets. For example, it is common for MTurk MTurk tasks for processing datasets with humans are currently de- workflows to filter datasets to find images or audio on a specific sub- signed with significant reimplementation of common workflows and ject, or rank such data based on workers’ subjective opinion. Pro- ad-hoc selection of parameters such as price to pay per task. We de- grammers currently waste considerable effort re-implementing these scribe how we have integrated crowds into a declarative workflow operations because reusable implementations do not exist. -
Amazon Case Study(10739114)
Amazon Case Study Anas Al Azmeh University of Amsterdam VU of Amsterdam Abstract. Amazon business as a website portal for goods and chattels growth in the past two decades to reach one of the best ICT organization. The curve of Amazon revenues increased sharply and most of the largest companies start to build its software using Amazon help after releasing Amazon Web Services (AWS). An explanation in this case study about the adaptive cycle that Amazon went through and the new stage of Amazon business. Keywords. Amazon Web Services, Adaptive cycle, Virtual organization Introduction Achieving one of the highest revenue business is quite impossible with these great companies in the market. Amazon has fascinated the world with the struggle to list its name in the best companies in the world. Amazon has been founded in July 1994[1] as a small company in United States, this small company has changed the view of market within short period. A brief of Amazon story of success and its development cycle which allows it to have the greatest growth in the market are descripted in this case study. 1. Amazon Strategy Amazon strategy depend on creating new portals and become the pioneer of the market. The market always follow the pioneer and most of the companies that deny to change disappear, as a consequence of not providing the customer needs. According to the History and Timeline of Amazon [2] Amazon influenced the market view. In 1997, Amazon introduced the 1-click shopping. After that, Amazon start to change the focus of its local market local to international one by lunching the first international sites in United Kingdom and Germany. -
Amazon Mechanical Turk Requester UI Guide Amazon Mechanical Turk Requester UI Guide
Amazon Mechanical Turk Requester UI Guide Amazon Mechanical Turk Requester UI Guide Amazon Mechanical Turk: Requester UI Guide Copyright © 2014 Amazon Web Services, Inc. and/or its affiliates. All rights reserved. The following are trademarks of Amazon Web Services, Inc.: Amazon, Amazon Web Services Design, AWS, Amazon CloudFront, Cloudfront, Amazon DevPay, DynamoDB, ElastiCache, Amazon EC2, Amazon Elastic Compute Cloud, Amazon Glacier, Kindle, Kindle Fire, AWS Marketplace Design, Mechanical Turk, Amazon Redshift, Amazon Route 53, Amazon S3, Amazon VPC. In addition, Amazon.com graphics, logos, page headers, button icons, scripts, and service names are trademarks, or trade dress of Amazon in the U.S. and/or other countries. Amazon©s trademarks and trade dress may not be used in connection with any product or service that is not Amazon©s, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Amazon Mechanical Turk Requester UI Guide Table of Contents Welcome ..................................................................................................................................... 1 How Do I...? ......................................................................................................................... 1 Introduction to Mechanical Turk ....................................................................................................... -
The Age of Amazon: Maximizing the B2C Marketing Opportunity
The Age of Amazon: Maximizing the B2C Marketing Opportunity In partnership with: The Age of Amazon: Maximizing the B2C Marketing Opportunity, published by ClickZ Intelligence in partnership with Catalyst, a GroupM and WPP company, is based on a survey of more than 250 North America-based business-to-consumer (B2C) marketers (across all sectors), and numerous in- depth interviews with those who are actively using Amazon as a marketing and sales platform for their own brands or on behalf of their clients. The report is aimed at all B2C-focused companies marketing and selling to consumers across a range of sectors, but is particularly relevant for those working in the consumer-packaged goods (CPG1) arena. In conjunction with our survey of marketers, we also carried out an online survey of 1,600 U.S. consumers, looking at usage of Amazon and comparing research and buying behavior for eight categories of retail goods. The marketer survey was conducted in July 2017, while the consumer survey was carried out in August 2017. 1For those reading outside the U.S., CPG is the same as FMCG (fast-moving consumer goods) 2 Contents Evolution of the customer journey drives CPG reorganization 8 The continued disruption of retail, and fusing of digital and offline 11 Influence of ecommerce grows across range of categories 14 The rise of Amazon as a media and marketing player 18 Use of Amazon Marketing Services 21 How companies are budgeting for Amazon 24 Ownership of Amazon strategy and programs 28 Seizing the Alexa opportunity 33 12 key recommendations for optimizing your Amazon strategy and programs 36 3 Executive Summary mazon’s star is in the ascendancy, with and only 15% agree that they are using Amazon business and mainstream media fixated Marketing Services to its full potential. -
Privacy Experiences on Amazon Mechanical Turk
113 “Our Privacy Needs to be Protected at All Costs”: Crowd Workers’ Privacy Experiences on Amazon Mechanical Turk HUICHUAN XIA, Syracuse University YANG WANG, Syracuse University YUN HUANG, Syracuse University ANUJ SHAH, Carnegie Mellon University Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) are widely used by organizations, researchers, and individuals to outsource a broad range of tasks to crowd workers. Prior research has shown that crowdsourcing can pose privacy risks (e.g., de-anonymization) to crowd workers. However, little is known about the specific privacy issues crowd workers have experienced and how they perceive the state ofprivacy in crowdsourcing. In this paper, we present results from an online survey of 435 MTurk crowd workers from the US, India, and other countries and areas. Our respondents reported different types of privacy concerns (e.g., data aggregation, profiling, scams), experiences of privacy losses (e.g., phishing, malware, stalking, targeted ads), and privacy expectations on MTurk (e.g., screening tasks). Respondents from multiple countries and areas reported experiences with the same privacy issues, suggesting that these problems may be endemic to the whole MTurk platform. We discuss challenges, high-level principles and concrete suggestions in protecting crowd workers’ privacy on MTurk and in crowdsourcing more broadly. CCS Concepts: • Information systems → Crowdsourcing; • Security and privacy → Human and societal aspects of security and privacy; • Human-centered computing → Computer supported cooperative work; Additional Key Words and Phrases: Crowdsourcing; Privacy; Amazon Mechanical Turk (MTurk) ACM Reference format: Huichuan Xia, Yang Wang, Yun Huang, and Anuj Shah. 2017. “Our Privacy Needs to be Protected at All Costs”: Crowd Workers’ Privacy Experiences on Amazon Mechanical Turk.