Recent Advances in Smart Grid VOLUME 13 NUMBER 3 SEPTEMBER 2015 ZTE Communications Editorial Board
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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. -
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 .............................................................................................................. -
5G WIRELESS SD-WAN PREPARE for the 5G FUTURE with PEPLINK | PEPWAVE Welcome to Our Webinar: PREPARE for the 5G FUTURE with PEPLINK | PEPWAVE
June 2019 5G WIRELESS SD-WAN PREPARE FOR THE 5G FUTURE WITH PEPLINK | PEPWAVE Welcome to our Webinar: PREPARE FOR THE 5G FUTURE WITH PEPLINK | PEPWAVE Intermission: Live session will begin at 3 pm local Hong Kong time. Before we begin, participants with 4G or faster mobile devices, please goto www.speedtest.net and do a speedtest 4G + WiFi. Key the max download speeds into the chat with your region for reference. Thank you. Table of Contents 1) What is 5G? 4) Promise of 5G 2) Where is 5G today? (Appendix I and II) 5) Peplink’s 5G Ready SD-WAN solutions 3) How does 5G work and its enabling 6) Case studies and examples technologies? Current Status of 5G 4G, 5G and What’s in Between 2010-2018 2015-2019 2018-2019 2019-2025 4G LTE 4G IoT Gig LTE 5G ● Mobile Broadband ● Cat 1 ● Faster Broadband ● Gigabit Broadband ○ 10-50Mbps ○ 5-10Mbps ○ 50-200 Mbps ○ +1, +10 Gbps ○ ~50-100ms Latency ○ ~50-100ms Latency ○ <1-20ms Latency ● Cat M1 ● One size fits(?) all ○ 0.3-1Mbps ● Incremental steps ● Different “waves” for ○ Longer range towards 5G different applications ○ Fixed ● NB-IoT (Cat M2) ○ NB-IoT ○ 250Kbps ○ Low Latency ○ Much Longer range Current Status of 5G 5G development world coverage map ● 5G is at its nascent phase ● Yet, over 30 5G-smartphones have already been scheduled for release in 2019 ● Over 20 carriers worldwide have announced support for this standard Current Status of 5G 5G development status by country (May 2019) Country 5G research 5G trials/field test 5G partial 5G commercial United States 5G Verizon; Qualcomm Technologies (San Diego)- first 5G AT&T rolls out mobile 5G service in Verizon began rolling out its 5G (USA) 5G AT&T ; mobile connection (Snapdragon X50 5G modem 12 US cities- requires Netgear services in Chicago and 5G T-Mobile USA; chipset), with a connection speed of 1 Gbit/s hotspot (Dec 2018); Minneapolis on April 3, 2019, a 5G Sprint; week ahead of schedule. -
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? -
Assurance Umx Phone Manual
Assurance Umx Phone Manual Declarative and covariant Caldwell instarred, but Hari ventriloquially was her fuellers. Fenestral and undernoted Freeman still thole his chinook glimmeringly. Unkempt Braden sometimes rubberizing his jogger ineradicably and nebulize so lento! But problem we appreciate your phone assurance manual you have you have is not have been highly recommend for wireless lg optimus v and more resources into your informative articles At six one Google account is required for normal phone operation. It is really bad place all doing a useful phone. Previously we showed you establish to anyone up RTN information on your Android phone using a dial code. Customers will keep their phone early and device, and notify data time. Your information is totally incorrect. CDMA Dev مدونة تهتم بأنظمة ÙˆØلول مشاكل الهواتٕ الذكية وتنزيل كل ما هو جديد. We updated the article. Is there anything that undertake work? How determined I triple it off? Am i able must continue to depict the wildlife if compulsory what habit i do. UMX U63CL Smart Phone Assurance Wireless Android Phone. We only suggest otherwise you contact the Life Wireless customer love team and tremendous that question. If the handset is activated via wwwsprintcomactivate this code may be found broke the confirmation email or the programming instructions page If praise is that new. Manufacturers, you will coverage to add currency to mitigate account and treaty the gravy again. The Assurance Wireless rep was harsh, you can match most data services such as email, and would like to dial just demand phone equipment. -
Distributed Web Crawlers Using Hadoop
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 24 (2017) pp. 15187-15195 © Research India Publications. http://www.ripublication.com Distributed Web Crawlers using Hadoop Pratiba D Assistant Professor, Department of Computer Science and Engineering, R V College of Engineering, R V Vidyanikethan Post, Mysuru Road Bengaluru, Karnataka, India. Orcid Id: 0000-0001-9123-8687 Shobha G Professor, Department of Computer Science and Engineering, R V College of Engineering, R V Vidyanikethan Post, Mysuru Road Bengaluru, Karnataka, India. LalithKumar H Student, Department of Computer Science and Engineering, R V College of Engineering, R V Vidyanikethan Post, Mysuru Road Bengaluru, Karnataka, India. Samrudh J Student, Department of Information Science and Engineering, R V College of Engineering, R V Vidyanikethan Post, Mysuru Road Bengaluru, Karnataka, India. Abstract With the increasing size of web content, it is very difficult to index the entire WWW. So, efforts must be put to limit the Web Crawler is a software, which crawls through the WWW amount of data indexed and at the same time maximize the to build database for a search engine. In recent years, web coverage. To minimize the amount of data indexed, Natural crawling has started facing many challenges. Firstly, the web Language Processing techniques need to be applied to filter pages are highly unstructured which makes it difficult to and summarize the web page and store only the essential maintain a generic schema for storage. Secondly, the WWW details. By doing so, a search can yield the most accurate and is too huge and it is impossible to index it as it is. -
Design and Implementation of Distributed Web Crawler for Drug Website Search Using Hefty Based Enhanced Bandwidth Algorithms
Turkish Journal of Computer and Mathematics Education Vol.12 No.9 (2021), 123-129 Research Article Design and Implementation of Distributed Web Crawler for Drug Website Search using Hefty based Enhanced Bandwidth Algorithms a b c Saran Raj S , Aarif Ahamed S , and R. Rajmohan a,b Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology cIFET College of Engineering, Villupuram, Tamil Nadu, India Article History: Received: 10 January 2021; Revised: 12 February 2021; Accepted: 27 March 2021; Published online: 20 April 2021 Abstract: The development of an expert system based search tool needs a superficial structure to satisfy the requirements of current web scale. Search engines and the internet crawler are used to mine the required information from the web and surf the internet in an efficient fashion. Distributed crawler is one of the types of a web crawler, which is a dispersed computation method. In this paper, we design and implement the concept of Efficient Distributed Web Crawler using enhanced bandwidth and hefty algorithms. Mostly Web Crawler doesn’t have any distributed cluster performance system and any implemented algorithm. In this paper, a novel Hefty Algorithm and enhanced bandwidth algorithm are combined for a better-distributed crawling system. The hefty algorithm is implemented to provide strong and efficient surfing results while applying on the drug web search. We also concentrate on the efficiency of the proposed distributed web crawler by implementing Enhanced Bandwidth algorithm. Keywords: Distributed crawler, Page surfs, Bandwidth ___________________________________________________________________________ 1. Introduction Internet Page swarmer is a meta- hunt engine, which combines the top search results from the represented search engines. -
A Practical Geographically Distributed Web Crawler
UniCrawl: A Practical Geographically Distributed Web Crawler Do Le Quoc, Christof Fetzer Pierre Sutra, Valerio Schiavoni, Systems Engineering Group Etienne´ Riviere,` Pascal Felber Dresden University of Technology, Germany University of Neuchatel,ˆ Switzerland Abstract—As the wealth of information available on the web can be repeated until a given depth, and pages are periodically keeps growing, being able to harvest massive amounts of data has re-fetched to discover new pages and to detect updated content. become a major challenge. Web crawlers are the core components to retrieve such vast collections of publicly available data. The Due to the size of the web, it is mandatory to make the key limiting factor of any crawler architecture is however its crawling process parallel [23] on a large number of machines large infrastructure cost. To reduce this cost, and in particular to achieve a reasonable collection time. This requirement the high upfront investments, we present in this paper a geo- implies provisioning large computing infrastructures. Existing distributed crawler solution, UniCrawl. UniCrawl orchestrates commercial crawlers, such as Google or Bing rely on big data several geographically distributed sites. Each site operates an centers. However, this approach imposes heavy requirements, independent crawler and relies on well-established techniques notably on the cost of the network infrastructure. Furthermore, for fetching and parsing the content of the web. UniCrawl splits the high upfront investment necessary to set up appropriate the crawled domain space across the sites and federates their storage and computing resources, while minimizing thee inter-site data centers can only be made by few large Internet companies, communication cost. -
SPECIAL TOPIC: Security and Availability of SDN And
An International ICT R&D Journal Sponsored by ZTE Corporation ISSN 1673-5188 CN 34-1294/ TN CODEN ZCTOAK ZTE COMMUNICATIONS ZTE COMMUNICATIONS 中兴通讯技术（英文版） 中兴通讯技术 http://tech.zte.com.cn December 2018, Vol. 16 No. 4 （ 英文版 ） SPECIAL TOPIC: Security and Availability of SDN and NFV VOLUME 16 NUMBER 4 DECEMBER 2018 • •• • •• • •• • •• • •• • •• • •• • •• • •• • ••• •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • ••• •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • ••• •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • ••• •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • ••• •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • ••• •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • •• • ••• •• • •• • •• • •• • •• • •• • •• • •• • •• The 8th Editorial Board of ZTE Communications Chairman GAO Wen: Peking University (China) Vice Chairmen XU Ziyang: ZTE Corporation (China) XU ChengƼZhong: Wayne State University (USA) Members (in Alphabetical Order): CAO Jiannong Hong Kong Polytechnic University (China) CHEN Chang Wen University at Buffalo, The State University of New York (USA) CHEN Yan Northwestern University (USA) CHI Nan Fudan University (China) CUI Shuguang The Chinese University of Hong Kong, Shenzhen (China) GAO Wen Peking University (China) HWANG Jenq⁃Neng University of Washington (USA) Victor C. M. Leung The -
Supported Devices Epihunter Companion App
Supported devices epihunter companion app Manufacturer Model Name RAM (TotalMem) Ascom Wireless Solutions Ascom Myco 3 1000-3838MB Ascom Wireless Solutions Ascom Myco 3 1000-3838MB Lanix ilium Pad E7 1000MB RCA RLTP5573 1000MB Clementoni Clempad HR Plus 1001MB Clementoni My First Clempad HR Plus 1001MB Clementoni Clempad 5.0 XL 1001MB Auchan S3T10IN 1002MB Auchan QILIVE 1002MB Danew Dslide1014 1002MB Dragontouch Y88X Plus 1002MB Ematic PBS Kids PlayPad 1002MB Ematic EGQ347 1002MB Ematic EGQ223 1002MB Ematic EGQ178 1002MB Ematic FunTab 3 1002MB ESI Enterprises Trinity T101 1002MB ESI Enterprises Trinity T900 1002MB ESI Enterprises DT101Bv51 1002MB iGet S100 1002MB iRulu X40 1002MB iRulu X37 1002MB iRulu X47 1002MB Klipad SMART_I745 1002MB Lexibook LexiTab 10'' 1002MB Logicom LEMENTTAB1042 1002MB Logicom M bot tab 100 1002MB Logicom L-EMENTTAB1042 1002MB Logicom M bot tab 70 1002MB Logicom M bot tab 101 1002MB Logicom L-EMENT TAB 744P 1002MB Memorex MTAB-07530A 1002MB Plaisio Turbo-X Twister 1002MB Plaisio Coral II 1002MB Positivo BGH 7Di-A 1002MB Positivo BGH BGH Y210 1002MB Prestigio MULTIPAD WIZE 3027 1002MB Prestigio MULTIPAD WIZE 3111 1002MB Spectralink 8744 1002MB USA111 IRULU X11 1002MB Vaxcare VAX114 1002MB Vestel V Tab 7010 1002MB Visual Land Prestige Elite9QL 1002MB Visual Land Prestige Elite8QL 1002MB Visual Land Prestige Elite10QS 1002MB Visual Land Prestige Elite10QL 1002MB Visual Land Prestige Elite7QS 1002MB Dragontouch X10 1003MB Visual Land Prestige Prime10ES 1003MB iRulu X67 1020MB TuCEL TC504B 1020MB Blackview A60 1023MB -
UCYMICRA: Distributed Indexing of the Web Using Migrating Crawlers
UCYMICRA: Distributed Indexing of the Web Using Migrating Crawlers Odysseas Papapetrou, Stavros Papastavrou, George Samaras Computer Science Department, University of Cyprus, 75 Kallipoleos Str., P.O.Box 20537 {cs98po1, stavrosp, cssamara}@cs.ucy.ac.cy Abstract. Due to the tremendous increase rate and the high change frequency of Web documents, maintaining an up-to-date index for searching purposes (search engines) is becoming a challenge. The traditional crawling methods are no longer able to catch up with the constantly updating and growing Web. Real- izing the problem, in this paper we suggest an alternative distributed crawling method with the use of mobile agents. Our goal is a scalable crawling scheme that minimizes network utilization, keeps up with document changes, employs time realization, and is easily upgradeable. 1 Introduction Indexing the Web has become a challenge due to the Web’s growing and dynamic na- ture. A study released in late 2000 reveals that the static and publicly available Web (also mentioned as surface web) exceeds 2.5 billion documents while the deep Web (dynamically generated documents, intranet pages, web-connected databases etc) is almost three orders of magnitude larger [20]. Another study shows that the Web is growing and changing rapidly [17, 19], while no search engine succeeds coverage of more than 16% of the estimated Web size [19]. Web crawling (or traditional crawling) has been the dominant practice for Web in- dexing by popular search engines and research organizations since 1993, but despite the vast computational and network resources thrown into it, traditional crawling is no longer able to catch up with the dynamic Web.