Mapreduce: Simplified Data Processing On
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Intro to Google for the Hill
Introduction to A company built on search Our mission Google’s mission is to organize the world’s information and make it universally accessible and useful. As a first step to fulfilling this mission, Google’s founders Larry Page and Sergey Brin developed a new approach to online search that took root in a Stanford University dorm room and quickly spread to information seekers around the globe. The Google search engine is an easy-to-use, free service that consistently returns relevant results in a fraction of a second. What we do Google is more than a search engine. We also offer Gmail, maps, personal blogging, and web-based word processing products to name just a few. YouTube, the popular online video service, is part of Google as well. Most of Google’s services are free, so how do we make money? Much of Google’s revenue comes through our AdWords advertising program, which allows businesses to place small “sponsored links” alongside our search results. Prices for these ads are set by competitive auctions for every search term where advertisers want their ads to appear. We don’t sell placement in the search results themselves, or allow people to pay for a higher ranking there. In addition, website managers and publishers take advantage of our AdSense advertising program to deliver ads on their sites. This program generates billions of dollars in revenue each year for hundreds of thousands of websites, and is a major source of funding for the free content available across the web. Google also offers enterprise versions of our consumer products for businesses, organizations, and government entities. -
Annual Report 2018
Pakistan Telecommunication Company Limited Company Telecommunication Pakistan PTCL PAKISTAN ANNUAL REPORT 2018 REPORT ANNUAL /ptcl.official /ptclofficial ANNUAL REPORT Pakistan Telecommunication /theptclcompany Company Limited www.ptcl.com.pk PTCL Headquarters, G-8/4, Islamabad, Pakistan Pakistan Telecommunication Company Limited ANNUAL REPORT 2018 Contents 01COMPANY REVIEW 03FINANCIAL STATEMENTS CONSOLIDATED Corporate Vision, Mission & Core Values 04 Auditors’ Report to the Members 129-135 Board of Directors 06-07 Consolidated Statement of Financial Position 136-137 Corporate Information 08 Consolidated Statement of Profit or Loss 138 The Management 10-11 Consolidated Statement of Comprehensive Income 139 Operating & Financial Highlights 12-16 Consolidated Statement of Cash Flows 140 Chairman’s Review 18-19 Consolidated Statement of Changes in Equity 141 Group CEO’s Message 20-23 Notes to and Forming Part of the Consolidated Financial Statements 142-213 Directors’ Report 26-45 47-46 ہ 2018 Composition of Board’s Sub-Committees 48 Attendance of PTCL Board Members 49 Statement of Compliance with CCG 50-52 Auditors’ Review Report to the Members 53-54 NIC Peshawar 55-58 02STATEMENTS FINANCIAL Auditors’ Report to the Members 61-67 Statement of Financial Position 68-69 04ANNEXES Statement of Profit or Loss 70 Pattern of Shareholding 217-222 Statement of Comprehensive Income 71 Notice of 24th Annual General Meeting 223-226 Statement of Cash Flows 72 Form of Proxy 227 Statement of Changes in Equity 73 229 Notes to and Forming Part of the Financial Statements 74-125 ANNUAL REPORT 2018 Vision Mission To be the leading and most To be the partner of choice for our admired Telecom and ICT provider customers, to develop our people in and for Pakistan. -
Mapreduce and Beyond
MapReduce and Beyond Steve Ko 1 Trivia Quiz: What’s Common? Data-intensive compung with MapReduce! 2 What is MapReduce? • A system for processing large amounts of data • Introduced by Google in 2004 • Inspired by map & reduce in Lisp • OpenSource implementaMon: Hadoop by Yahoo! • Used by many, many companies – A9.com, AOL, Facebook, The New York Times, Last.fm, Baidu.com, Joost, Veoh, etc. 3 Background: Map & Reduce in Lisp • Sum of squares of a list (in Lisp) • (map square ‘(1 2 3 4)) – Output: (1 4 9 16) [processes each record individually] 1 2 3 4 f f f f 1 4 9 16 4 Background: Map & Reduce in Lisp • Sum of squares of a list (in Lisp) • (reduce + ‘(1 4 9 16)) – (+ 16 (+ 9 (+ 4 1) ) ) – Output: 30 [processes set of all records in a batch] 4 9 16 f f f returned iniMal 1 5 14 30 5 Background: Map & Reduce in Lisp • Map – processes each record individually • Reduce – processes (combines) set of all records in a batch 6 What Google People Have NoMced • Keyword search Map – Find a keyword in each web page individually, and if it is found, return the URL of the web page Reduce – Combine all results (URLs) and return it • Count of the # of occurrences of each word Map – Count the # of occurrences in each web page individually, and return the list of <word, #> Reduce – For each word, sum up (combine) the count • NoMce the similariMes? 7 What Google People Have NoMced • Lots of storage + compute cycles nearby • Opportunity – Files are distributed already! (GFS) – A machine can processes its own web pages (map) CPU CPU CPU CPU CPU CPU CPU CPU -
Google Apps Premier Edition: Easy, Collaborative Workgroup Communication with Gmail and Google Calendar
Google Apps Premier Edition: easy, collaborative workgroup communication with Gmail and Google Calendar Messaging overview Google Apps Premier Edition messaging tools include email, calendar and instant messaging solutions that help employees communicate and stay connected, wherever and whenever they work. These web-based services can be securely accessed from any browser, work on mobile devices like BlackBerry and iPhone, and integrate with other popular email systems like Microsoft Outlook, Apple Mail, and more. What’s more, Google Apps’ SAML-based Single Sign-On (SSO) capability integrates seamlessly with existing enterprise security and authentication services. Google Apps deliver productivity and reduce IT workload with a hosted, 99.9% uptime solution that gets teams working together fast. Gmail Get control of spam Advanced filters keep spam from employees’ inboxes so they can focus on messages that matter, and IT admins can focus on other initiatives. Keep all your email 25 GB of storage per user means that inbox quotas and deletion schedules are a thing of the past. Integrated instant messaging Connect with contacts instantly without launching a separate application or leaving your inbox. No software required. Built-in voice and video chat Voice and video conversations, integrated into Gmail, make it easy to connect face-to-face with co-workers around the world. Find messages instantly Powerful Google search technology is built into Gmail, turning your inbox into your own private and secure Google search engine for email. Protect and secure sensitive information Additional spam filtering from Postini provides employees with an additional layer of protection and policy-enforced encryption between domains using standard TLS protocols. -
Improving Efficiency of Map Reduce Paradigm with ANFIS for Big Data (IJSTE/ Volume 1 / Issue 12 / 015)
IJSTE - International Journal of Science Technology & Engineering | Volume 1 | Issue 12 | June 2015 ISSN (online): 2349-784X Improving Efficiency of Map Reduce Paradigm with ANFIS for Big Data Gor Vatsal H. Prof. Vatika Tayal Department of Computer Science and Engineering Department of Computer Science and Engineering NarNarayan Shashtri Institute of Technology Jetalpur , NarNarayan Shashtri Institute of Technology Jetalpur , Ahmedabad , India Ahmedabad , India Abstract As all we know that map reduce paradigm is became synonyms for computing big data problems like processing, generating and/or deducing large scale of data sets. Hadoop is a well know framework for these types of problems. The problems for solving big data related problems are varies from their size , their nature either they are repetitive or not etc., so depending upon that various solutions or way have been suggested for different types of situations and problems. Here a hybrid approach is used which combines map reduce paradigm with anfis which is aimed to boost up such problems which are likely to repeat whole map reduce process multiple times. Keywords: Big Data, fuzzy Neural Network, ANFIS, Map Reduce, Hadoop ________________________________________________________________________________________________________ I. INTRODUCTION Initially, to solve problem various problems related to large crawled documents, web requests logs, row data , etc a computational processing model is suggested by jeffrey Dean and Sanjay Ghemawat is Map Reduce in 2004[1]. MapReduce programming model is inspired by map and reduce primitives which are available in Lips and many other functional languages. It is like a De Facto standard and widely used for solving big data processing and related various operations. -
The Google File System (GFS)
The Google File System (GFS), as described by Ghemwat, Gobioff, and Leung in 2003, provided the architecture for scalable, fault-tolerant data management within the context of Google. These architectural choices, however, resulted in sub-optimal performance in regard to data trustworthiness (security) and simplicity. Additionally, the application- specific nature of GFS limits the general scalability of the system outside of the specific design considerations within the context of Google. SYSTEM DESIGN: The authors enumerate their specific considerations as: (1) commodity components with high expectation of failure, (2) a system optimized to handle relatively large files, particularly multi-GB files, (3) most writes to the system are concurrent append operations, rather than internally modifying the extant files, and (4) a high rate of sustained bandwidth (Section 1, 2.1). Utilizing these considerations, this paper analyzes the success of GFS’s design in achieving a fault-tolerant, scalable system while also considering the faults of the system with regards to data-trustworthiness and simplicity. Data-trustworthiness: In designing the GFS system, the authors made the conscious decision not prioritize data trustworthiness by allowing applications to access ‘stale’, or not up-to-date, data. In particular, although the system does inform applications of the chunk-server version number, the designers of GFS encouraged user applications to cache chunkserver information, allowing stale data accesses (Section 2.7.2, 4.5). Although possibly troubling -
Notes on Chromebooks and Neverware Cloudready Chromium
Chromebooks Are For Seniors - Ron Brown - APCUG VTC - 8-19-17 https://youtu.be/4uszFPNL-SU http://cb4s.net/ Are Chromebooks more secure than laptops? Google’s security features in ChromeOS When Google set about designing ChromeOS it had the distinct advantage of being able to see the problems that Windows, macOS, and even Linux had struggled with when it came to security. With this in mind it implemented five key features that make ChromeOS a formidable system for hackers to crack. Automatic Updates As new threats become known, it’s vital that patches are applied quickly to thwart them. Google has an excellent track record on this, as not only does it release fixes on a very regular basis, but with Chromebooks guaranteed OS updates for seven years after release, the majority of users are running the most up to date version anyway. his can be an issue on other platforms, where differing combinations of OS versions and hardware can delay patches. Sandboxing If something does go wrong, and malware gets onto a Chromebook, there’s not much damage it can do. Each tab in ChromeOS acts as a separate entity with a restricted environment or ‘sandbox’. This means that only the affected tab is vulnerable, and that it is very difficult for the infection to spread to other areas of the machine. In Windows and macOS the malware is usually installed somewhere on the system itself, which makes it a threat with a much wider scope. There are ways to restrict this of course, with anti-virus software, regular system scans, and not running as an administrator. -
A Review on GOOGLE File System
International Journal of Computer Science Trends and Technology (IJCST) – Volume 4 Issue 4, Jul - Aug 2016 RESEARCH ARTICLE OPEN ACCESS A Review on Google File System Richa Pandey [1], S.P Sah [2] Department of Computer Science Graphic Era Hill University Uttarakhand – India ABSTRACT Google is an American multinational technology company specializing in Internet-related services and products. A Google file system help in managing the large amount of data which is spread in various databases. A good Google file system is that which have the capability to handle the fault, the replication of data, make the data efficient, memory storage. The large data which is big data must be in the form that it can be managed easily. Many applications like Gmail, Facebook etc. have the file systems which organize the data in relevant format. In conclusion the paper introduces new approaches in distributed file system like spreading file’s data across storage, single master and appends writes etc. Keywords:- GFS, NFS, AFS, HDFS I. INTRODUCTION III. KEY IDEAS The Google file system is designed in such a way that 3.1. Design and Architecture: GFS cluster consist the data which is spread in database must be saved in of single master and multiple chunk servers used by a arrange manner. multiple clients. Since files to be stored in GFS are The arrangement of data is in the way that it can large, processing and transferring such huge files can reduce the overhead load on the server, the consume a lot of bandwidth. To efficiently utilize availability must be increased, throughput should be bandwidth files are divided into large 64 MB size highly aggregated and much more services to make chunks which are identified by unique 64-bit chunk the available data more accurate and reliable. -
Redalyc.Acceptability Engineering: the Study of User Acceptance Of€Innovative€Technologies
Journal of Applied Research and Technology ISSN: 1665-6423 [email protected] Centro de Ciencias Aplicadas y Desarrollo Tecnológico México Kim, Hee-Cheol Acceptability engineering: the study of user acceptance of innovative technologies Journal of Applied Research and Technology, vol. 13, núm. 2, 2015, pp. 230-237 Centro de Ciencias Aplicadas y Desarrollo Tecnológico Distrito Federal, México Available in: http://www.redalyc.org/articulo.oa?id=47439895008 How to cite Complete issue Scientific Information System More information about this article Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Journal's homepage in redalyc.org Non-profit academic project, developed under the open access initiative Disponible en www.sciencedirect.com Journal of Applied Research and Technology Journal of Applied Research and Technology 13 (2015) 230-237 www.jart.ccadet.unam.mx Original Acceptability engineering: the study of user acceptance of innovative technologies Hee-Cheol Kim Department of Computer Engineering, u-Healthcare & Anti-aging Research Center, Inje University, Gimhae, Gyeong-Nam, Korea Received 19 April 2014; accepted 18 August 2014 Abstract The discipline of human-computer interaction (HCI) has been vital in developing understandings of users, usability, and the design of user- centered computer systems. However, it does not provide a satisfactory explanation of user perspectives on the specialized but important domain of innovative technologies, instead focusing more on mature technologies. In particular, the success of innovative technologies requires attention to be focused on early adopters of the technology and enthusiasts, rather than general end-users. Therefore, user acceptance should be considered more important than usability and convenience. -
Learn How to Use Google Reviews at Your Hotel
Learn How to Use Google Reviews at your Hotel Guide Managing Guest Satisfaction Surveys: Best Practices Index Introduction 2 Can you explain Google’s rating system? 3 What’s different about the new Google Maps? 5 Do reviews affect my hotel’s search ranking? 6 How can we increase the number of Google reviews? 7 Can I respond to Google reviews? 8 Managing Guest Satisfaction1 Surveys: Best Practices Introduction Let’s be honest, Google user reviews aren’t very helpful And then there’s the near-ubiquitous “+1” button, a way when compared to reviews on other review sites. for Google+ users to endorse a business, web page, They’re sparse, random and mostly anonymous. You photo or post. can’t sort them, filtering options are minimal, and the rating system is a moving target. These products are increasingly integrated, allowing traveler planners to view rates, availability, location, But that’s all changing. photos and reviews without leaving the Google ecosystem. Reviews and ratings appear to play an increasingly critical role in Google’s master plan for world domination This all makes Google reviews difficult to ignore—for in online travel planning. They now show prominently in travelers and hotels. So what do hotels need to know? In Search, Maps, Local, Google+, Hotel Finder and the this final instalment in ReviewPro’s popular Google For new Carousel—and on desktops, mobile search and Hotels series, we answer questions from webinar mobile applications. attendees related to Google reviews. Managing Guest Satisfaction2 Surveys: Best Practices Can you Explain Google’s Rating System? (I) Registered Google users can rate a business by visiting its Google+ 360° Guest Local page and clicking the Write a Review icon. -
Apigee X Migration Offering
Apigee X Migration Offering Overview Today, enterprises on their digital transformation journeys are striving for “Digital Excellence” to meet new digital demands. To achieve this, they are looking to accelerate their journeys to the cloud and revamp their API strategies. Businesses are looking to build APIs that can operate anywhere to provide new and seamless cus- tomer experiences quickly and securely. In February 2021, Google announced the launch of the new version of the cloud API management platform Apigee called Apigee X. It will provide enterprises with a high performing, reliable, and global digital transformation platform that drives success with digital excellence. Apigee X inte- grates deeply with Google Cloud Platform offerings to provide improved performance, scalability, controls and AI powered automation & security that clients need to provide un-parallel customer experiences. Partnerships Fresh Gravity is an official partner of Google Cloud and has deep experience in implementing GCP products like Apigee/Hybrid, Anthos, GKE, Cloud Run, Cloud CDN, Appsheet, BigQuery, Cloud Armor and others. Apigee X Value Proposition Apigee X provides several benefits to clients for them to consider migrating from their existing Apigee Edge platform, whether on-premise or on the cloud, to better manage their APIs. Enhanced customer experience through global reach, better performance, scalability and predictability • Global reach for multi-region setup, distributed caching, scaling, and peak traffic support • Managed autoscaling for runtime instance ingress as well as environments independently based on API traffic • AI-powered automation and ML capabilities help to autonomously identify anomalies, predict traffic for peak seasons, and ensure APIs adhere to compliance requirements. -
F1 Query: Declarative Querying at Scale
F1 Query: Declarative Querying at Scale Bart Samwel John Cieslewicz Ben Handy Jason Govig Petros Venetis Chanjun Yang Keith Peters Jeff Shute Daniel Tenedorio Himani Apte Felix Weigel David Wilhite Jiacheng Yang Jun Xu Jiexing Li Zhan Yuan Craig Chasseur Qiang Zeng Ian Rae Anurag Biyani Andrew Harn Yang Xia Andrey Gubichev Amr El-Helw Orri Erling Zhepeng Yan Mohan Yang Yiqun Wei Thanh Do Colin Zheng Goetz Graefe Somayeh Sardashti Ahmed M. Aly Divy Agrawal Ashish Gupta Shiv Venkataraman Google LLC [email protected] ABSTRACT 1. INTRODUCTION F1 Query is a stand-alone, federated query processing platform The data processing and analysis use cases in large organiza- that executes SQL queries against data stored in different file- tions like Google exhibit diverse requirements in data sizes, la- based formats as well as different storage systems at Google (e.g., tency, data sources and sinks, freshness, and the need for custom Bigtable, Spanner, Google Spreadsheets, etc.). F1 Query elimi- business logic. As a result, many data processing systems focus on nates the need to maintain the traditional distinction between dif- a particular slice of this requirements space, for instance on either ferent types of data processing workloads by simultaneously sup- transactional-style queries, medium-sized OLAP queries, or huge porting: (i) OLTP-style point queries that affect only a few records; Extract-Transform-Load (ETL) pipelines. Some systems are highly (ii) low-latency OLAP querying of large amounts of data; and (iii) extensible, while others are not. Some systems function mostly as a large ETL pipelines. F1 Query has also significantly reduced the closed silo, while others can easily pull in data from other sources.