Live Web Search Experiments for the Rest of Us
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Awareness and Utilization of Search Engines for Information Retrieval by Students of National Open University of Nigeria in Enugu Study Centre Library
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Library Philosophy and Practice (e-journal) Libraries at University of Nebraska-Lincoln 2020 Awareness and Utilization of Search Engines for Information Retrieval by Students of National Open University of Nigeria in Enugu Study Centre Library SUNDAY JUDE ONUH National Open University of Nigeria, [email protected] OGOEGBUNAM LOVETH EKWUEME National Open University of Nigeria, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/libphilprac Part of the Library and Information Science Commons ONUH, SUNDAY JUDE and EKWUEME, OGOEGBUNAM LOVETH, "Awareness and Utilization of Search Engines for Information Retrieval by Students of National Open University of Nigeria in Enugu Study Centre Library" (2020). Library Philosophy and Practice (e-journal). 4650. https://digitalcommons.unl.edu/libphilprac/4650 Awareness and Utilization of Search Engines for Information Retrieval by Students of National Open University of Nigeria in Enugu Study Centre Library By Jude Sunday Onuh Enugu Study Centre Library National Open University of Nigeria [email protected] & Loveth Ogoegbunam Ekwueme Department of Library and Information Science National Open University of Nigeria [email protected] Abstract This study dwelt on awareness and utilization of search engines for information retrieval by students of National Open University of Nigeria (NOUN) Enugu Study centre. Descriptive survey research was adopted for the study. Two research questions were drawn from the two purposes that guided the study. The population consists of 5855 undergraduate students of NOUN Enugu Study Centre. A sample size of 293 students was used as 5% of the entire population. -
Search Techniques: Web Based Search MODULE - 5B INFORMATION RETRIEVAL SYSTEM
Search Techniques: Web Based Search MODULE - 5B INFORMATION RETRIEVAL SYSTEM 18 Notes SEARCH TECHNIQUES: WEB BASED SEARCH 18.1 INTRODUCTION Internet has become the biggest repository of information in the world. It can be considered as a global library where variety of information in different languages and formats is stored in digital form. The volume of information on web is enormous and it has become near to impossible to estimate its size. Because of its size and storing mechanism, finding relevant and precise information has become a difficult task. For searching information from this vast repository, we use search engines. There are thousands of search engines available on internet. For example, if you visit http://www.thesearchen ginelist.com/, you will find a classified list of search engines. This list is category-wise and includes all-purpose search engines in various fields like accounting, blogs, books, legal, medical, etc. In Lesson 17, you studied basic concepts of search techniques. Here, you will learn various aspects of searching information on web. 18.2 OBJECTIVES After studying this lesson, you will be able to: explain purpose of simple and advanced search techniques; develop search string using Boolean logic on a given topic; illustrate search string with the help of a diagram; give examples of simple search and advanced search on internet; identify various Search Engines, viz. Google, Yahoo, Google Scholar; LIBRARY AND INFORMATION SCIENCE 367 MODULE - 5B Search Techniques: Web Based Search INFORMATION RETRIEVAL SYSTEM identify Search Engines on internet in different vernacular languages; illustrate search in specific categories, viz. maps, images; and modify search strings to get precise results. -
Clearing Commonly Used Browser's Internet Cache Desktops
Clearing Commonly Used Browser’s Internet Cache Clearing Commonly Used Browser’s Internet Cache For information on how to clear the cache of commonly used internet browsers, please see below. NOTE: If your browser is not listed, please refer to the documentation that came with your device or the browser software on how to clear its cache. REMINDER: To protect your privacy and prevent unauthorized use of your System ID, 1) sign out of the Self-Service application, 2) clear the browser’s cache, and 3) close all web browser windows. Desktops Internet Explorer 7 1. From the Tools menu, select Internet Options. 2. Under "Browsing history", click Delete. 3. To delete your cache, click Delete files. 4. Click Close, and then click OK to exit. 5. After the cache is cleared, click the X to close the browser window and confirm ALL browser windows are closed. Internet Explorer 9, 10, and 11 1. Select Tools (via the Gear icon) 2. Select Safety 3. Select Delete Browsing History… NOTE: You can also access this menu by holding Ctrl + Shift + Delete 4. Make sure to uncheck Preserve Favorites websites data and check both Temporary Internet Files and Cookies then click Delete. Microsoft Edge 1. Click the Hub icon. 2. Click the History icon. 3. Click the link labeled Clear all History. 4. Check the boxes for each item you want to clear. 5. Click the Clear button. The message “All Clear!” will appear. Android To clear cache: 1. Start your browser. 2. Tap Menu, and then tap More. 3. Select Settings. -
Taskstream Technical & System Faqs
Taskstream Technical & System FAQs 71 WEST 23RD STREET, NEW YORK, NY 10010 · T 1.800.311.5656 · e [email protected] Taskstream FAQs Taskstream FAQs Click the links in each question or description to navigate to the answer: System Requirements FAQs . What Internet browsers can I use to access Taskstream? . Can I use Taskstream on a Mac or a PC? on a desktop or laptop computer? . Does Taskstream work on iPad, iPhone or Android-based devices? . Do I need additional plugins or add-ons to use Taskstream? . Can I access Taskstream offline? . Does Taskstream require JavaScript to be enabled? . Does Taskstream require cookies to be enabled? . Does Taskstream use pop-ups? . How can I import and export data within Taskstream? Technical FAQs . Can I have Taskstream open in more than one window at a time? . Is Taskstream's site accessible to users with disabilities? . What is a Comma Separated Value (CSV) file? . How do I print my work? . How do I format my text? . How do I add special characters, such as math symbols or special punctuation, to my text? . What is WebMarker File Markup? . What is Turnitin® Originality Reporting? . Can I change the size of the Taskstream text/fonts? Troubleshooting . When I click on a button nothing occurs. Some of the features within the Taskstream site are missing. When I try to download/view a file attachment, nothing happens. I am not receiving emails from Taskstream. I uploaded a file on my Mac, but the attached file does not open. My file is too large to upload. Taskstream is reporting that I have entered unsafe text. -
Document Selection in a Distributed Search Engine Architecture Ibrahim
Document Selection in a Distributed Search Engine Architecture 1Ibrahim AlShourbaji, 2Samaher Al-Janabi and 3Ahmed Patel 1Computer Network Department, Computer Science and Information System College, Jazan University, Jazan 82822-6649, Saudi Arabia 2Department of Information Networks, Faculty of Information Technology,University of Babylon, Babylon, Hilla 00964, Iraq 3Visiting Professor School of Computing and Information Systems, Faculty of Science, Engineering and Computing, Kingston University, Kingston upon Thames KT1 2EE, United Kingdom Abstract Distributed Search Engine Architecture (DSEA) hosts numerous independent topic-specific search engines and selects a subset of the databases to search within the architecture. The objective of this approach is to reduce the amount of space needed to perform a search by querying only a subset of the total data available. In order to manipulate data across many databases, it is most efficient to identify a smaller subset of databases that would be most likely to return the data of specific interest that can then be examined in greater detail. The selection index has been most commonly used as a method for choosing the most applicable databases as it captures broad information about each database and its indexed documents. Employing this type of database allows the researcher to find information more quickly, not only with less cost, but it also minimizes the potential for biases. This paper investigates the effectiveness of different databases selected within the framework and scope of the distributed search engine architecture. The purpose of the study is to improve the quality of distributed information retrieval. Keywords: web search, distributed search engine, document selection, information retrieval, Collection Retrival Inference network 1. -
Information Technology: Applications DLIS408
Information Technology: Applications DLIS408 Edited by: Jovita Kaur INFORMATION TECHNOLOGY: APPLICATIONS Edited By Jovita Kaur Printed by LAXMI PUBLICATIONS (P) LTD. 113, Golden House, Daryaganj, New Delhi-110002 for Lovely Professional University Phagwara DLP-7765-079-INFO TECHNOLOGY APPLICATION C-4713/012/02 Typeset at: Shubham Composers, Delhi Printed at: Sanjay Printers & Publishers, Delhi SYLLABUS Information Technology: Applications Objectives: • To understand the applications of Information technology in organizations. • To appreciate how information technology can help to improve decision-making in organizations. • To appreciate how information technology is used to integrate the business disciplines. • To introduce students to business cases, so they learn to solve business problems with information technology. • To introduce students to the strategic applications of information technology. • To introduce students to the issues and problems involved in building complex systems and organizing information resources. • To introduce students to the social implications of information technology. • To introduce students to the management of information systems. S. No. Topics Library automation: Planning and implementation, Automation of housekeeping operations – Acquisition, 1. Cataloguing, Circulation, Serials control OPAC Library management. 2. Library software packages: RFID, LIBSYS, SOUL, WINISIS. 3. Databases: Types and generations, salient features of select bibliographic databases. 4. Communication technology: Fundamentals communication media and components. 5. Network media and types: LAN, MAN, WAN, Intranet. 6. Digital, Virtual and Hybrid libraries: Definition and scope. Recent development. 7. Library and Information Networks with special reference to India: DELNET, INFLIBNET, ERNET, NICNET. Internet—based resources and services Browsers, search engines, portals, gateways, electronic journals, mailing 8. list and scholarly discussion lists, bulletin board, computer conference and virtual seminars. -
A Comparative Analysis of the Search Feature Effectiveness of the Major English and Chinese Search Engines
The current issue and full text archive of this journal is available at www.emeraldinsight.com/1468-4527.htm Search feature A comparative analysis of the effectiveness search feature effectiveness of the major English and Chinese search engines 217 Refereed article received Jin Zhang 6 July 2011 School of Information Studies, University of Wisconsin Milwaukee, Approved for publication Milwaukee, Wisconsin, USA 5 May 2012 Wei Fei Suzhou Library, Suzhou, China, and Taowen Le Goddard School of Business and Economics, Weber State University, Ogden, Utah, USA Abstract Purpose – The purpose of this paper to investigate the effectiveness of selected search features in the major English and Chinese search engines and compare the search engines’ retrieval effectiveness. Design/approach/methodology – The search engines Google, Google China, and Baidu were selected for this study. Common search features such as title search, basic search, exact phrase search, PDF search, and URL search, were identified and used. Search results from using the five features in the search engines were collected and compared. One-way ANOVA and regression analysis were used to compare the retrieval effectiveness of the search engines. Findings – It was found that Google achieved the best retrieval performance with all five search features among the three search engines. Moreover Google achieved the best webpage ranking performance. Practical implications – The findings of this study improve the understanding of English and Chinese search engines and the differences between them in terms of search features, and can be used to assist users in choosing appropriate and effective search strategies when they search for information on the internet. -
Shaping the Web: Why the Politics of Search Engines Matters
TheInformation Society, 16:169 –185, 2000 Copyright c 2000T aylor& Francis 0197-2243/° 00$12.00+ .00 Shaping the Web: Whythe Politics ofSearch Engines Matters Lucas D.Introna LondonSchool of Economics,London, United Kingdom Helen Nissenbaum University Center for HumanV alues,Princeton University, Princeton,New Jersey,USA Enhancedby the technologyof the WorldWide W eb,it This articleargues that searchengines raise not merely technical has becomean integral part ofthe ever-expandingglobal issuesbut alsopolitical ones. Our studyof searchengines suggests media system, movingonto center stage ofmedia politics thatthey systematically exclude (in somecases by design and in alongsidetraditional broadcastmedia— television andra- some,accidentally ) certainsites and certaintypes of sitesin favor dio.Enthusiasts ofthe “newmedium” have heralded it as ofothers,systematically giving prominence to some at theexpense ademocratizingforce that will givevoice to diverse so- ofothers. We argue that such biases,which would leadto a nar- cial, economic,and cultural groups,to members ofsociety rowingof theWeb’ s functioningin society,run counterto thebasic notfrequentlyheard in the publicsphere. It will empower architectureof the Web aswell as tothevalues and idealsthat have the traditionally disempowered,giving them access both fueledwidespread support for its growth and development.We to typically unreachablenodes of powerand to previously considerways of addressingthe politics of searchengines, raising inaccessible troves ofinformation. doubts whether,in -
Merging Multiple Search Results Approach for Meta-Search Engines
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by D-Scholarship@Pitt MERGING MULTIPLE SEARCH RESULTS APPROACH FOR META-SEARCH ENGINES By Khaled Abd-El-Fatah Mohamed B.S-------- Cairo University, Egypt, 1995 M.A-------Cairo University, Egypt, 1999 M.A------- University of Pittsburgh 2001 Submitted to the Graduate Faculty of School of Information Sciences in Partial Fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2004 UNIVERSITY OF PITTSBURGH INFORMATION SCIENCES This dissertation was presented by Khaled Abd-El-Fatah Mohamed It was defended on Janauary 29, 2004 and approved by Chris Tomer, PhD, Associate Professor, DLIS Jose-Marie Griffiths, PhD, Professor, DLIS Don King, Research Professor, DLIS Amy Knapp, PhD, ULS Dissertation Director: Chris Tomer, PhD, Associate Professor MERGING MULTIPLE SEARCH RESULTS APPROACH FOR META-SEARCH ENGINES Khaled A. Mohamed, PhD University of Pittsburgh, 2004 Meta Search Engines are finding tools developed for enhancing the search performance by submitting user queries to multiple search engines and combining the search results in a unified ranked list. They utilized data fusion technique, which requires three major steps: databases selection, the results combination, and the results merging. This study tries to build a framework that can be used for merging the search results retrieved from any set of search engines. This framework based on answering three major questions: 1. How meta-search developers could define the optimal rank order for the selected engines. 2. How meta-search developers could choose the best search engines combination. 3. What is the optimal heuristic merging function that could be used for aggregating the rank order of the retrieved documents form incomparable search engines. -
Meta Search Engine with an Intelligent Interface for Information Retrieval on Multiple Domains
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.4, October 2011 META SEARCH ENGINE WITH AN INTELLIGENT INTERFACE FOR INFORMATION RETRIEVAL ON MULTIPLE DOMAINS D.Minnie1, S.Srinivasan2 1Department of Computer Science, Madras Christian College, Chennai, India [email protected] 2Department of Computer Science and Engineering, Anna University of Technology Madurai, Madurai, India [email protected] ABSTRACT This paper analyses the features of Web Search Engines, Vertical Search Engines, Meta Search Engines, and proposes a Meta Search Engine for searching and retrieving documents on Multiple Domains in the World Wide Web (WWW). A web search engine searches for information in WWW. A Vertical Search provides the user with results for queries on that domain. Meta Search Engines send the user’s search queries to various search engines and combine the search results. This paper introduces intelligent user interfaces for selecting domain, category and search engines for the proposed Multi-Domain Meta Search Engine. An intelligent User Interface is also designed to get the user query and to send it to appropriate search engines. Few algorithms are designed to combine results from various search engines and also to display the results. KEYWORDS Information Retrieval, Web Search Engine, Vertical Search Engine, Meta Search Engine. 1. INTRODUCTION AND RELATED WORK WWW is a huge repository of information. The complexity of accessing the web data has increased tremendously over the years. There is a need for efficient searching techniques to extract appropriate information from the web, as the users require correct and complex information from the web. A Web Search Engine is a search engine designed to search WWW for information about given search query and returns links to various documents in which the search query’s key words are found. -
Patent Application Publication (10) Pub. No.: US 2013/0091418 A1 Gonzalez (43) Pub
US 2013 009 1418A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2013/0091418 A1 Gonzalez (43) Pub. Date: Apr. 11, 2013 (54) CROSS-BROWSER TOOLBAR AND METHOD Publication Classification THEREOF (51) Int. Cl. (71) Applicant: Visicom Media Inc., Brossard (CA) G06F 7/2 (2006.01) (52) U.S. Cl. (72) Inventor: Miguel Enrique Cepero Gonzalez, CPC .................................... G06F 17/218 (2013.01) Brossard (CA) USPC .......................................................... 71.5/234 (57) ABSTRACT (73) Assignee: VISICOMMEDIA INC., Brossard A tool configured to build a cross-browser toolbar is pro (CA) vided. The tool comprises a processor; and a memory coupled to the processor and configured to store at least instructions for execution of a wizard program by the processor, wherein (21) Appl. No.: 13/690,236 the wizard program causes to: receive an input identifying at least user interface elements and event handlers respective of the user interface elements, wherein the input further identi (22) Filed: Nov.30, 2012 fies at least two different types of a web browser on which the toolbar can be executed; generate respective of the received O O input a toolbar render object, a script file, and at least one Related U.S. Application Data toolbar library for each type of web browser; and compile the (63) Continuation of application No. 12/270,421, filed on toolbar render object, the script file, and the least one toolbar Nov. 13, 2008, now Pat. No. 8,341,608. library into an installer file. 120-1 Installer Web File BrOWSer TOOlbar Builder installer File TOOlbar BrOWSer Libraries Patent Application Publication Apr. -
Downloads the Lists of Search Engines and Query Terms That Are Previously Defined As Part of an Experimental Design
Running Head: ALGORITHM AUDITING AT A LARGE-SCALE 1 Algorithm Auditing at a Large-Scale: Insights from Search Engine Audits Roberto Ulloa1, Mykola Makhortykh2 and Aleksandra Urman3 1 GESIS – Leibniz-Institute for the Social Sciences 2 University of Bern 3 University of Zurich Author Note We have no conflicts of interest to disclose. This is a preprint and working version of the paper. Data collections were sponsored from the SNF (100001CL_182630/1) and DFG (MA 2244/9-1) grant for the project “Reciprocal relations between populist radical-right attitudes and political information behaviour: A longitudinal study of attitude development in high- choice information environments” lead by Silke Adam (U of Bern) and Michaela Maier (U of Koblenz-Landau) and FKMB (the Friends of the Institute of Communication and Media Science at the University of Bern) grant “Algorithmic curation of (political) information and its biases” awarded to Mykola Makhortykh and Aleksandra Urman. Running Head: ALGORITHM AUDITING AT A LARGE-SCALE 2 Abstract Algorithm audits have increased in recent years due to a growing need to independently assess the performance of automatically curated services that process, filter and rank the large and dynamic amount of information available on the internet. Among several methodologies to perform such audits, virtual agents stand out because they offer the ability to perform systematic experiments, simulating human behaviour without the associated costs of recruiting participants. Motivated by the importance of research transparency and replicability of results, this paper focuses on the challenges of such an approach. It provides methodological details, recommendations, lessons learned and limitations that researchers should take into consideration when setting up experiments with virtual agents.