Storm-Crawler: Real-Time Web Crawling on Apache Storm
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Study of Web Crawler and Its Different Types
IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 1, Ver. VI (Feb. 2014), PP 01-05 www.iosrjournals.org Study of Web Crawler and its Different Types Trupti V. Udapure1, Ravindra D. Kale2, Rajesh C. Dharmik3 1M.E. (Wireless Communication and Computing) student, CSE Department, G.H. Raisoni Institute of Engineering and Technology for Women, Nagpur, India 2Asst Prof., CSE Department, G.H. Raisoni Institute of Engineering and Technology for Women, Nagpur, India, 3Asso.Prof. & Head, IT Department, Yeshwantrao Chavan College of Engineering, Nagpur, India, Abstract : Due to the current size of the Web and its dynamic nature, building an efficient search mechanism is very important. A vast number of web pages are continually being added every day, and information is constantly changing. Search engines are used to extract valuable Information from the internet. Web crawlers are the principal part of search engine, is a computer program or software that browses the World Wide Web in a methodical, automated manner or in an orderly fashion. It is an essential method for collecting data on, and keeping in touch with the rapidly increasing Internet. This Paper briefly reviews the concepts of web crawler, its architecture and its various types. Keyword: Crawling techniques, Web Crawler, Search engine, WWW I. Introduction In modern life use of internet is growing in rapid way. The World Wide Web provides a vast source of information of almost all type. Now a day’s people use search engines every now and then, large volumes of data can be explored easily through search engines, to extract valuable information from web. -
Security Log Analysis Using Hadoop Harikrishna Annangi Harikrishna Annangi, [email protected]
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by St. Cloud State University St. Cloud State University theRepository at St. Cloud State Culminating Projects in Information Assurance Department of Information Systems 3-2017 Security Log Analysis Using Hadoop Harikrishna Annangi Harikrishna Annangi, [email protected] Follow this and additional works at: https://repository.stcloudstate.edu/msia_etds Recommended Citation Annangi, Harikrishna, "Security Log Analysis Using Hadoop" (2017). Culminating Projects in Information Assurance. 19. https://repository.stcloudstate.edu/msia_etds/19 This Starred Paper is brought to you for free and open access by the Department of Information Systems at theRepository at St. Cloud State. It has been accepted for inclusion in Culminating Projects in Information Assurance by an authorized administrator of theRepository at St. Cloud State. For more information, please contact [email protected]. Security Log Analysis Using Hadoop by Harikrishna Annangi A Starred Paper Submitted to the Graduate Faculty of St. Cloud State University in Partial Fulfillment of the Requirements for the Degree of Master of Science in Information Assurance April, 2016 Starred Paper Committee: Dr. Dennis Guster, Chairperson Dr. Susantha Herath Dr. Sneh Kalia 2 Abstract Hadoop is used as a general-purpose storage and analysis platform for big data by industries. Commercial Hadoop support is available from large enterprises, like EMC, IBM, Microsoft and Oracle and Hadoop companies like Cloudera, Hortonworks, and Map Reduce. Hadoop is a scheme written in Java that allows distributed processes of large data sets across clusters of computers using programming models. A Hadoop frame work application works in an environment that provides storage and computation across clusters of computers. -
Building a Scalable Index and a Web Search Engine for Music on the Internet Using Open Source Software
Department of Information Science and Technology Building a Scalable Index and a Web Search Engine for Music on the Internet using Open Source software André Parreira Ricardo Thesis submitted in partial fulfillment of the requirements for the degree of Master in Computer Science and Business Management Advisor: Professor Carlos Serrão, Assistant Professor, ISCTE-IUL September, 2010 Acknowledgments I should say that I feel grateful for doing a thesis linked to music, an art which I love and esteem so much. Therefore, I would like to take a moment to thank all the persons who made my accomplishment possible and hence this is also part of their deed too. To my family, first for having instigated in me the curiosity to read, to know, to think and go further. And secondly for allowing me to continue my studies, providing the environment and the financial means to make it possible. To my classmate André Guerreiro, I would like to thank the invaluable brainstorming, the patience and the help through our college years. To my friend Isabel Silva, who gave me a precious help in the final revision of this document. Everyone in ADETTI-IUL for the time and the attention they gave me. Especially the people over Caixa Mágica, because I truly value the expertise transmitted, which was useful to my thesis and I am sure will also help me during my professional course. To my teacher and MSc. advisor, Professor Carlos Serrão, for embracing my will to master in this area and for being always available to help me when I needed some advice. -
Natural Language Processing Technique for Information Extraction and Analysis
International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Volume 2, Issue 8, August 2015, PP 32-40 ISSN 2349-4840 (Print) & ISSN 2349-4859 (Online) www.arcjournals.org Natural Language Processing Technique for Information Extraction and Analysis T. Sri Sravya1, T. Sudha2, M. Soumya Harika3 1 M.Tech (C.S.E) Sri Padmavati Mahila Visvavidyalayam (Women’s University), School of Engineering and Technology, Tirupati. [email protected] 2 Head (I/C) of C.S.E & IT Sri Padmavati Mahila Visvavidyalayam (Women’s University), School of Engineering and Technology, Tirupati. [email protected] 3 M. Tech C.S.E, Assistant Professor, Sri Padmavati Mahila Visvavidyalayam (Women’s University), School of Engineering and Technology, Tirupati. [email protected] Abstract: In the current internet era, there are a large number of systems and sensors which generate data continuously and inform users about their status and the status of devices and surroundings they monitor. Examples include web cameras at traffic intersections, key government installations etc., seismic activity measurement sensors, tsunami early warning systems and many others. A Natural Language Processing based activity, the current project is aimed at extracting entities from data collected from various sources such as social media, internet news articles and other websites and integrating this data into contextual information, providing visualization of this data on a map and further performing co-reference analysis to establish linkage amongst the entities. Keywords: Apache Nutch, Solr, crawling, indexing 1. INTRODUCTION In today’s harsh global business arena, the pace of events has increased rapidly, with technological innovations occurring at ever-increasing speed and considerably shorter life cycles. -
Distributed Web Crawling Using Network Coordinates
Distributed Web Crawling Using Network Coordinates Barnaby Malet Department of Computing Imperial College London [email protected] Supervisor: Peter Pietzuch Second Marker: Emil Lupu June 16, 2009 2 Abstract In this report we will outline the relevant background research, the design, the implementation and the evaluation of a distributed web crawler. Our system is innovative in that it assigns Euclidean coordinates to crawlers and web servers such that the distances in the space give an accurate prediction of download times. We will demonstrate that our method gives the crawler the ability to adapt and compensate for changes in the underlying network topology, and in doing so can achieve significant decreases in download times when compared with other approaches. 3 4 Acknowledgements Firstly, I would like to thank Peter Pietzuch for the help that he has given me throughout the course of the project as well as showing me support when things did not go to plan. Secondly, I would like to thank Johnathan Ledlie for helping me with some aspects of the implemen- tation involving the Pyxida library. I would also like to thank the PlanetLab support team for giving me extensive help in dealing with complaints from web masters. Finally, I would like to thank Emil Lupu for providing me with feedback about my Outsourcing Report. 5 6 Contents 1 Introduction 9 2 Background 13 2.1 Web Crawling . 13 2.1.1 Web Crawler Architecture . 14 2.1.2 Issues in Web Crawling . 16 2.1.3 Discussion . 17 2.2 Crawler Assignment Strategies . 17 2.2.1 Hash Based . -
Panacea D4.1
SEVENTH FRAMEWORK PROGRAMME THEME 3 Information and communication Technologies PANACEA Project Grant Agreement no.: 248064 Platform for Automatic, Normalized Annotation and Cost-Effective Acquisition of Language Resources for Human Language Technologies D4.1 Technologies and tools for corpus creation, normalization and annotation Dissemination Level: Public Delivery Date: July 16, 2010 Status – Version: Final Author(s) and Affiliation: Prokopis Prokopidis, Vassilis Papavassiliou (ILSP), Pavel Pecina (DCU), Laura Rimel, Thierry Poibeau (UCAM), Roberto Bartolini, Tommaso Caselli, Francesca Frontini (ILC- CNR), Vera Aleksic, Gregor Thurmair (Linguatec), Marc Poch Riera, Núria Bel (UPF), Olivier Hamon (ELDA) Technologies and tools for corpus creation, normalization and annotation Table of contents 1 Introduction ........................................................................................................................... 3 2 Terminology .......................................................................................................................... 3 3 Corpus Acquisition Component ............................................................................................ 3 3.1 Task description ........................................................................................................... 4 3.2 State of the art .............................................................................................................. 4 3.3 Existing tools .............................................................................................................. -
Building a Search Engine for the Cuban Web
Building a Search Engine for the Cuban Web Jorge Luis Betancourt Search/Crawl Engineer NOVEMBER 16-18, 2016 • SEVILLE, SPAIN Who am I 01 Jorge Luis Betancourt González Search/Crawl Engineer Apache Nutch Committer & PMC Apache Solr/ES enthusiast 2 Agenda • Introduction & motivation • Technologies used • Customizations • Conclusions and future work 3 Introduction / Motivation Cuba Internet Intranet Global search engines can’t access documents hosted the Cuban Intranet 4 Writing your own web search engine from scratch? or … 5 Common search engine features 1 Web search: HTML & documents (PDF, DOC) • highlighting • suggestions • filters (facets) • autocorrection 2 Image search (size, format, color, objects) • thumbnails • show metadata • filters (facets) • match text with images 3 News search (alerting, notifications) • near real time • email, push, SMS 6 How to fulfill these requirements? store query At the core a search engine: stores some information a retrieve this information when a question is received 7 Open Source to the rescue … crawler 1 Index Server 2 web interface 3 8 Apache Nutch Nutch is a well matured, production ready “ Web crawler. Enables fine grained configuration, relying on Apache Hadoop™ data structures, which are great for batch processing. 9 Apache Nutch • Highly scalable • Highly extensible • Pluggable parsing protocols, storage, indexing, scoring, • Active community • Apache License 10 Apache Solr TOTAL DOWNLOADS 8M+ MONTHLY DOWNLOADS 250,000+ • Apache License • Great community • Highly modular • Stability / Scalability -
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} -
Chapter 2 Introduction to Big Data Technology
Chapter 2 Introduction to Big data Technology Bilal Abu-Salih1, Pornpit Wongthongtham2 Dengya Zhu3 , Kit Yan Chan3 , Amit Rudra3 1The University of Jordan 2 The University of Western Australia 3 Curtin University Abstract: Big data is no more “all just hype” but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. from the 3V’s to up 10V’s of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units and components. Finally, we provide some big data online resources for reference. Keywords Big data, 3V of Big data, Cloud Computing, Data Lake, Enterprise Data Centre, PaaS, IaaS, SaaS, Hadoop, Spark, HBase, Information retrieval, Solr 2.1 Introduction The ability to exploit the ever-growing amounts of business-related data will al- low to comprehend what is emerging in the world. In this context, Big Data is one of the current major buzzwords [1]. Big Data (BD) is the technical term used in reference to the vast quantity of heterogeneous datasets which are created and spread rapidly, and for which the conventional techniques used to process, analyse, retrieve, store and visualise such massive sets of data are now unsuitable and inad- equate. This can be seen in many areas such as sensor-generated data, social media, uploading and downloading of digital media. -
Towards a Distributed Web Search Engine
Towards a Distributed Web Search Engine Ricardo Baeza-Yates Yahoo! Research Barcelona, Spain Joint work with Barla Cambazoglu, Aristides Gionis, Flavio Junqueira, Mauricio Marín, Vanessa Murdock (Yahoo! Research) and many other people Web Search Web Context 4 Web Search • This is one of the most complex data engineering challenges today: – Distributed in nature – Large volume of data – Highly concurrent service – Users expect very good & fast answers • Current solution: Replicated centralized system 5 WR Logical Architecture Web Crawlers 6 A Typical Web Search Engine • Caching – result cache – posting list cache – document cache • Replication – multiple clusters – improve throughput • Parallel query processing – partitioned index • document-based • term-based – Online query processing Search Engine Architectures • Architectures differ in – number of data centers – assignment of users to data centers – assignment of index to data centers System Size • 20 billion Web pages implies at least 100Tb of text • The index in RAM implies at least a cluster of 10,000 PCs • Assume we can answer 1,000 queries/sec • 350 million queries a day imply 4,000 queries/sec • Decide that the peak load plus a fault tolerance margin is 3 • This implies a replication factor of 12 giving 120,000 PCs • Total deployment cost of over 100 million US$ plus maintenance cost • In 201x, being conservative, we would need over 1 million computers! 10 Questions • Should we use a centralized system? • Can we have a (cheaper) distributed search system in spite of network latency? -
Code Smell Prediction Employing Machine Learning Meets Emerging Java Language Constructs"
Appendix to the paper "Code smell prediction employing machine learning meets emerging Java language constructs" Hanna Grodzicka, Michał Kawa, Zofia Łakomiak, Arkadiusz Ziobrowski, Lech Madeyski (B) The Appendix includes two tables containing the dataset used in the paper "Code smell prediction employing machine learning meets emerging Java lan- guage constructs". The first table contains information about 792 projects selected for R package reproducer [Madeyski and Kitchenham(2019)]. Projects were the base dataset for cre- ating the dataset used in the study (Table I). The second table contains information about 281 projects filtered by Java version from build tool Maven (Table II) which were directly used in the paper. TABLE I: Base projects used to create the new dataset # Orgasation Project name GitHub link Commit hash Build tool Java version 1 adobe aem-core-wcm- www.github.com/adobe/ 1d1f1d70844c9e07cd694f028e87f85d926aba94 other or lack of unknown components aem-core-wcm-components 2 adobe S3Mock www.github.com/adobe/ 5aa299c2b6d0f0fd00f8d03fda560502270afb82 MAVEN 8 S3Mock 3 alexa alexa-skills- www.github.com/alexa/ bf1e9ccc50d1f3f8408f887f70197ee288fd4bd9 MAVEN 8 kit-sdk-for- alexa-skills-kit-sdk- java for-java 4 alibaba ARouter www.github.com/alibaba/ 93b328569bbdbf75e4aa87f0ecf48c69600591b2 GRADLE unknown ARouter 5 alibaba atlas www.github.com/alibaba/ e8c7b3f1ff14b2a1df64321c6992b796cae7d732 GRADLE unknown atlas 6 alibaba canal www.github.com/alibaba/ 08167c95c767fd3c9879584c0230820a8476a7a7 MAVEN 7 canal 7 alibaba cobar www.github.com/alibaba/ -
Apache Solr 3 1 Cookbook.Pdf
Apache Solr 3.1 Cookbook Over 100 recipes to discover new ways to work with Apache's Enterprise Search Server Rafał Kuć BIRMINGHAM - MUMBAI Apache Solr 3.1 Cookbook Copyright © 2011 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: July 2011 Production Reference: 1140711 Published by Packt Publishing Ltd. 32 Lincoln Road Olton Birmingham, B27 6PA, UK. ISBN 978-1-849512-18-3 www.packtpub.com Cover Image by Rakesh Shejwal ([email protected]) Credits Author Project Coordinator Rafał Kuć Srimoyee Ghoshal Reviewers Proofreader Ravindra Bharathi Samantha Lyon Juan Grande Indexer Acquisition Editor Hemangini Bari Sarah Cullington Production Coordinators Development Editor Adline Swetha Jesuthas Meeta Rajani Kruthika Bangera Technical Editors Cover Work Manasi Poonthottam Kruthika Bangera Sakina Kaydawala Copy Editors Kriti Sharma Leonard D'Silva About the Author Rafał Kuć is a born team leader and software developer.