Comparison of Search Engines
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Market Research SD-5 Gathering Information About Commercial Products and Services
Market Research SD-5 Gathering Information About Commercial Products and Services DEFENSE STANDARDIZATION PROGRA M JANUARY 2008 Contents Foreword 1 The Market Research Other Considerations 32 Background 2 Process 13 Amount of Information Strategic Market Research to Gather 32 What Is Market Research? 2 (Market Surveillance) 14 Procurement Integrity Act 32 Why Do Market Research? 2 Identify the Market or Market Paperwork Reduction Act 33 Segment of Interest 14 When Is Market Research Cost of Market Research 34 Done? 5 Identify Sources of Market Information 16 Who Should Be Involved In Market Research? 7 Collect Relevant Market Other Information Information 17 Technical Specialist 8 Document the Results 18 on Market Research 35 User 9 Logistics Specialist 9 Tactical Market Research Appendix A 36 (Market Investigation) 19 Testing Specialist 9 Types of Information Summarize Strategic Market Available on the Internet Cost Analyst 10 Research 19 Legal Counsel 10 Formulate Requirements 20 Appendix B 39 Contracting Officer 10 Web-Based Information Identify Sources of Sources Information 21 Guiding Principles 11 Collect Product or Service Appendix C 47 Examples of Tactical Start Early 11 Information from Sources 22 Collect Information from Information Define and Document Product or Service Users 26 Requirements 11 Evaluate the Data 27 Refine as You Proceed 12 Document the Results 30 Tailor the Investigation 12 Repeat as Necessary 12 Communicate 12 Involve Users 12 Foreword The Department of Defense (DoD) relies extensively on the commercial market for the products and services it needs, whether those products and services are purely commercial, modified for DoD use from commercial products and services, or designed specifically for DoD. -
Web Search Tutoring for the Local Community
Web search for local communities in the Highlands of Scotland: A self-tutoring guide MODULE III Alternatives to Google: some other search tools worth a try © Copyright Hans Zell Publishing Consultants 2011 Glais Bheinn, Lochcarron, Ross-shire IV54 8YB, Scotland, UK Email: [email protected] Web: www.hanszell.co.uk Web search for local communities in the Highlands of Scotland: A self-tutoring guide MODULE I How to get the most out of Google Web search MODULE II A concise guide to Google products, services, applications, and other offerings MODULE III Alternatives to Google: some other search tools worth a try MODULE IV The best of the Web: a guide to some of the most information-rich resources on the Internet 2 Introduction Google is a marvellous Web search tool and is as good as they get at present, but it is certainly not the only one. Other top search engines include Ask.com (aka as Ask Jeeves), Bing (formerly called MSN Search), and Yahoo! (and see General purpose, product, and visual search engines below). According to data published by Experian Hitwise http://www.hitwise.com/us/datacenter/main/dashboard-23984.html in June 2011, Google still heavily dominates the market with a share of about 68%, while the market share of Yahoo and Microsoft’s Bing currently is something just under 14% for both; Ask.com is in fourth place with around 2.6%, and AOL Search in fifth place with about 1.4%. The picture is roughly the same if ranked by number of visits, although Bing does better than Yahoo in this category. -
NEXT GENERATION CATALOGUES: an ANALYSIS of USER SEARCH STRATEGIES and BEHAVIOR by FREDRICK KIWUWA LUGYA DISSERTATION Submitted I
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Illinois Digital Environment for Access to Learning and Scholarship Repository NEXT GENERATION CATALOGUES: AN ANALYSIS OF USER SEARCH STRATEGIES AND BEHAVIOR BY FREDRICK KIWUWA LUGYA DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Library and Information Science in the Graduate College of the University of Illinois at Urbana-Champaign, 2017 Urbana, Illinois Doctoral Committee: Associate Professor Kathryn La Barre, Chair and Director Assistant Professor Nicole A. Cooke Dr. Jennifer Emanuel Taylor, University of Illinois Chicago Associate Professor Carol Tilley ABSTRACT The movement from online catalogues to search and discovery systems has not addressed the goals of true resource discoverability. While catalogue user studies have focused on user search and discovery processes and experiences, and construction and manipulation of search queries, little insight is given to how searchers interact with search features of next generation catalogues. Better understanding of user experiences can help guide informed decisions when selecting and implementing new systems. In this study, fourteen graduate students completed a set of information seeking tasks using UIUC's VuFind installation. Observations of these interactions elicited insight into both search feature use and user understanding of the function of features. Participants used the basic search option for most searches. This is because users understand that basic search draws from a deep index that always gives results regardless of search terms; and because it is convenient, appearing at every level of the search, thus reducing effort and shortening search time. -
A Framework for Evaluating the Retrieval Effectiveness of Search Engines Dirk Lewandowski Hamburg University of Applied Sciences, Germany
1 A Framework for Evaluating the Retrieval Effectiveness of Search Engines Dirk Lewandowski Hamburg University of Applied Sciences, Germany This is a preprint of a book chapter to be published in: Jouis, Christophe: Next Generation Search Engine: Advanced Models for Information Retrieval. Hershey, PA: IGI Global, 2012 http://www.igi-global.com/book/next-generation-search-engines/59723 ABSTRACT This chapter presents a theoretical framework for evaluating next generation search engines. We focus on search engines whose results presentation is enriched with additional information and does not merely present the usual list of “10 blue links”, that is, of ten links to results, accompanied by a short description. While Web search is used as an example here, the framework can easily be applied to search engines in any other area. The framework not only addresses the results presentation, but also takes into account an extension of the general design of retrieval effectiveness tests. The chapter examines the ways in which this design might influence the results of such studies and how a reliable test is best designed. INTRODUCTION Information retrieval systems in general and specific search engines need to be evaluated during the development process, as well as when the system is running. A main objective of the evaluations is to improve the quality of the search results, although other reasons for evaluating search engines do exist (see Lewandowski & Höchstötter, 2008). A variety of quality factors can be applied to search engines. These can be grouped into four major areas (Lewandowski & Höchstötter, 2008): • Index Quality: This area of quality measurement indicates the important role that search engines’ databases play in retrieving relevant and comprehensive results. -
E-Book: an Introduction to Custom Search Engines for Websites
E-book: An Introduction to Custom Search Engines for Websites What are you looking for? Search www.sitesearch360.com 1 Contents An introduction to search engines........................................ 4 Your visitor’s search journey: the key elements................ 23 What is site search?................................................................ 6 Presentation — how does it look?................................................ 24 Why is site search important?............................................... 6 Processing — how does it work?.................................................. 28 Navigation vs. search................................................................. 7 Results — how does it deliver?.................................................... 32 How are people using search?............................................... 9 Implementation challenges.................................................... 36 Why are custom search engines the perfect choice?........ 12 JavaScript................................................................................ 36 Semantic search........................................................................ 12 Security.................................................................................. 37 Natural search language............................................................ 13 WordPress............................................................................... 37 Actionable analytics.................................................................. 14 Cloudflare and -
Cyber Intelligence Gathering – Cheat Sheet
Cyber Intelligence Gathering – Cheat Sheet Social Media Websites www.youtube.com www.twitter.com www.facebook.com www.linkedin.com www.flickr.com www.myspace.com http://plus.google.com https://www.pinterest.com Social Media Monitoring http://socialmention.com/ http://topsy.com/ http://www.twazzup.com http://www.icerocket.com People & Company Search http://checkusernames.com/ http://www.123people.com https://pipl.com http://www.pcworld.com/article/219593/how_to_do_an_online_background_check_for_free.html www.Pandia.com www.virtualchase.com www.people-search-engines.com www.peoplesearch.net www.virtualgumshoe.com www.searchsystems.net www.person.langenberg.com www.publicrecordfinder.com www.powerreporting.com www.nettrace.com www.deadlineonline.com www.peoplesearchlinks.com http://www.peekyou.com http://com.lullar.com www.infospace.com http://www.sec.gov/edgar/searchedgar/webusers.htm#.VRMr4Fy28b4 http://www.zoominfo.com Cyber 51 LLC 267 Kentlands Blvd. #800, Gaithersburg, Maryland, 20878, USA - Email: [email protected] CYBER 51 LLC PROVIDES THIS DOCUMENT FOR EDUCATIONAL PURPOSES ONLY. CYBER 51 IS NOT RESPONSIBLE FOR ANY LINKED PAGES FROM THIS DOCUMENT OR ANY WRONG DOING. Historical Websites and Archives Search www.screenshots.com www.archive.org Google Search: cache:www.website.com Network Detail Search http://www.mcit.com.hk/nqt.php http://www.yougetsignal.com Pictures Search Google Image Search Exif tool www.photobucket.com Email Search Email Search in speech marks Email Header Analysis Main Search Engines Google Yahoo Bing Gigablast Ask Baidu Google Search Google Image Search Google Trends Google Maps Google Earth Google Video Search Google Search (Advanced) Email search in speech marks Google search in speech marks site:twitter.com "Name" site:www.facebook.com "Name" AND "Location" intitle:”index of” “parent directory” john doe Cyber 51 LLC 267 Kentlands Blvd. -
Natural Language Processing for Online Applications Text Retrieval, Extraction and Categorization
Natural Language Processing for Online Applications Natural Language Processing Editor Prof. Ruslan Mitkov School of Humanities, Languages and Social Sciences University of Wolverhampton Stafford St. Wolverhampton WV1 1SB, United Kingdom Email: [email protected] Advisory Board Christian Boitet (University of Grenoble) Jn Carroll (University of Sussex, Brighton) Eugene Charniak (Brown University, Providence) Eduard Hovy (Information Sciences Institute, USC) Richard Kittredge (University of Montreal) Geoffrey Leech (Lancaster University) Carlos Martin-Vide (Rovira i Virgili Un., Tarragona) Andrei Mikheev (University of Edinburgh) Jn Nerbonne (University of Groningen) Nicolas Nicolov (IBM, T.J. Watson Research Center) Kemal Oflazer (Sabanci University) Allan Ramsey (UMIST, Manchester) Monique Rolbert (Université de Marseille) Richard Sproat (AT&T Labs Research, Florham Park) K-Y Su (Baviour Design Corp.) Isabelle Trancoso (INESC, Lisbon) Benjamin Tsou (City University of Hong Kong) Jun-ichi Tsujii (University of Tokyo) Evene Tzoukermann (Bell Laboratories, Murray Hill) Yorick Wilks (University of Sheffield) Volume 5 Natural Language Processing for Online Applications: Text Retrieval, Extraction and Categorization by Peter Jackson and Isabelle Moulinier Natural Language Processing for Online Applications Text Retrieval, Extraction and Categorization Peter Jackson Isabelle Moulinier omson Legal & Regulatory John Benjamins Publishing Company Amsterdam / iladelia TM The paper used in this publication meets the minimum requirements of American 8 National Standard for Information Sciences – Permanence of Paper for Printed Library Materials, ansi z39.48-1984. Library of Congress Cataloging-in-Publication Data Jackson, Peter, 1948- Natural language processing for online applications : text retrieval, extraction, and categorization / Peter Jackson, Isabelle Moulinier. p.cm.(Natural Language Processing, issn 1567–8202 ; v.5) Includes bibliographical references and index. -
Search Engines: Search Engines Are Tools Which You Can Use to Find Websites Which Have the Information You Are Looking For
About Search Engines: Search engines are tools which you can use to find websites which have the information you are looking for. The key to easily finding information with search engines is using the right search terms. Search engines like Google and Yahoo search enormous web indexes of internet sites and return lists of websites which are relevant to your search. There are many types of search engines and meta-search engines. Meta-search engines send your search to several other search engines and return results together from each. You can almost always get to a search engine on the Internet by going to www. Name of the Search Engine.com For example: www.yahoo.com will get you to the Yahoo search engine. See the search engine section in this handout for different search engines. Basic Search Engine strategies: 1. Decide if you should use a search engine. You might just want to browse by exploring resources with a directory or index, like the Yahoo directory or the Librarian’s Index to the Internet. 2. Don’t enter words that are too general or too specific. For example you remember a great chicken lasagna your friend made and you want to make it too. Your search would be: NOT: Italian food BUT: chicken lasagna recipe mushrooms spinach 3. Eliminate unnecessary words and prepositions. Words like it, but, on, a, among, around, at, might be unnecessary unless they are part of a proper name. For example: NOT: I am looking for a place to stay in Ashland BUT: Hotels Bed Breakfasts Ashland Oregon 4. -
Information Literacy
COURSE GUIDE LIS 101 INFORMATION LITERACY Course Team Okpala Angela Ebele PhD & Ailakhu, Ugonna Vivian PhD (Course Developers) - NOUN Okpala Angela Ebele Ph D. & Ailakhu, Ugonna Vivian PhD (Course Writers) - NOUN Okpala Angela Ebele Ph D. (Course Editor) - NOUN NATIONAL OPEN UNIVERSITY OF NIGERIA LIS 101 COURSE GUIDE © 2020 by NOUN Press National Open University of Nigeria Headquarters University Village Plot 91, Cadastral Zone Nnamdi Azikiwe Expressway Jabi, Abuja Lagos Office 14/16 Ahmadu Bello Way Victoria Island, Lagos e-mail: [email protected] URL: www.nou.edu.ng All rights reserved. No part of this book may be reproduced, in any form or by any means, without permission in writing from the publisher. Print: 2020 ISBN: 978-978-058-046-9 ii LIS 101 COURSE GUIDE CONTENTS PAGE Introduction ……………………………………………….. iv What you will learn in this course ………………………… iv Course Aims ………………………………………............. iv Course Objectives …………………………………………. iv Working through this course ………………………………. v Course Materials …………………………………………… v Study Units ………………………………………................ v Presentation Schedule ……………………………............... vi Assignment File …………………………………………… vi Assessment ………………………………………………… vi How to get the most from this course ……………………... vii Facilitation/tutor and Tutorials ……………………………. viii iii LIS 101 COURSE GUIDE INTRODUCTION Welcome to LIS 101: Information Literacy. This Course Guide is a brief description of what the course is about and the course material will give you the contents of what you are expected to learn in this course. It also contains some general guidelines on the amount of time you are expected to spend on each unit of this course to complete the course. There is a separate Assignment File which contains detailed information on tutor-marked assignments that you are expected to answer after each unit. -
Web Search Engine
Web Search Engine Bosubabu Sambana, MCA, M.Tech Assistant Professor, Dept of Computer Science & Engineering, Simhadhri Engineering College, Visakhapatnam, AP-531001, India. Abstract: This hypertext pool is dynamically changing due to this reason it is more difficult to find useful The World Wide Web (WWW) allows people to share information.In 1995, when the number of “usefully information or data from the large database searchable” Web pages was a few tens of millions, it repositories globally. We need to search the was widely believed that “indexing the whole of the information with specialized tools known generically Web” was already impractical or would soon become as search engines. There are many search engines so due to its exponential growth. A little more than a available today, where retrieving meaningful decade later, the GYM search engines—Google, information is difficult. However to overcome this problem of retrieving meaningful information Yahoo!, and Microsoft—are indexing almost a intelligently in common search engines, semantic thousand times as much data and between them web technologies are playing a major role. In this providing reliable sub second responses to around a paper we present a different implementation of billion queries a day in a plethora of languages. If this semantic search engine and the role of semantic were not enough, the major engines now provide much relatedness to provide relevant results. The concept higher quality answers. of Semantic Relatedness is connected with Wordnet which is a lexical database of words. We also For most searchers, these engines do a better job of made use of TF-IDF algorithm to calculate word ranking and presenting results, respond more quickly frequency in each and every webpage and to changes in interesting content, and more effectively Keyword Extraction in order to extract only useful eliminate dead links, duplicate pages, and off-topic keywords from a huge set of words. -
Evaluating the Suitability of Web Search Engines As Proxies for Knowledge Discovery from the Web
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 96 ( 2016 ) 169 – 178 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2016, 5-7 September 2016, York, United Kingdom Evaluating the suitability of Web search engines as proxies for knowledge discovery from the Web Laura Martínez-Sanahuja*, David Sánchez UNESCO Chair in Data Privacy, Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili Av.Països Catalans, 26, 43007 Tarragona, Catalonia, Spain Abstract Many researchers use the Web search engines’ hit count as an estimator of the Web information distribution in a variety of knowledge-based (linguistic) tasks. Even though many studies have been conducted on the retrieval effectiveness of Web search engines for Web users, few of them have evaluated them as research tools. In this study we analyse the currently available search engines and evaluate the suitability and accuracy of the hit counts they provide as estimators of the frequency/probability of textual entities. From the results of this study, we identify the search engines best suited to be used in linguistic research. © 20162016 TheThe Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of KES International. Peer-review under responsibility of KES International Keywords: Web search engines; hit count; information distribution; knowledge discovery; semantic similarity; expert systems. 1. Introduction Expert and knowledge-based systems rely on data to build the knowledge models required to perform inferences or answer questions. -
Resource Selection for Federated Search on The
Resource Selection for Federated Search on the Web Dong Nguyen1, Thomas Demeester2, Dolf Trieschnigg1,3, Djoerd Hiemstra1 1 University of Twente, The Netherlands 2 iMinds, Ghent University, Belgium 3 MyDataFactory, The Netherlands {d.nguyen, r.b.trieschnigg, d.hiemstra}@utwente.nl, [email protected] Abstract A publicly available dataset for federated search reflecting a real web environment has long been absent, making it difficult for researchers to test the validity of their federated search algorithms for the web setting. We present several experiments and analyses on resource selection on the web using a recently released test collection containing the results from more than a hundred real search engines, ranging from large general web search engines such as Google, Bing and Yahoo to small domain- specific engines. First, we experiment with estimating the size of uncooperative search engines on the web using query based sampling and propose a new method using the ClueWeb09 dataset. We find the size estimates to be highly effective in resource selection. Second, we show that an optimized federated search system based on smaller web search engines can be an alternative to a system using large web search engines. Third, we provide an empirical comparison of several popular resource selection methods and find that these methods are not readily suitable for resource selection on the web. Challenges include the sparse resource descriptions and extremely skewed sizes of collections. 1 Introduction Despite the obvious utility and necessity of using large general web search engines for navigating the internet, a large part of the web cannot be reached by these.