Role of Meta Search Engines in Web- Based Information System: Fundamentals and Challenges
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Internet Training: the Basics. INSTITUTION Temple Univ., Philadelphia, PA
DOCUMENT RESUME ED 424 360 CE 077 241 AUTHOR Gallo, Gail; Wechowski, Chester P. TITLE Internet Training: The Basics. INSTITUTION Temple Univ., Philadelphia, PA. Center for Vocational Education Professional Personnel Development. PUB DATE 1998-04-00 NOTE 27p. PUB TYPE Guides Non-Classroom (055) EDRS PRICE MF01/PCO2 Plus Postage. DESCRIPTORS Adult Education; Educational Needs; *Information Sources; *Online Systems; Postsecondary Education; Secondary Education; Teaching Methods; Vocational Education Teachers; *World Wide Web IDENTIFIERS Web Sites ABSTRACT This paper outlines the basic information teachers need to . know to use the World Wide Web for research and communication, using Netscape 3.04. Topics covered include the following: what is the World Wide Web?; what is a browser?; accessing the Web; moving around a web document; the Uniform Resource Locator (URL); Bookmarks; saving and printing a web page; Netscape's options preferences menu; searching the Web; and interesting places (websites of interest to vocational educators) .Two appendixes contain the following: "Infoseek: How Do I Search? 'Ruby Slippers'" and Guidelines for Completing Abstracts. (KC) ******************************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * ***********************************************************************i******** t. The Center for Vocational Education \I/ Professional Personnel Development/ \ INTERNET TRAINING: THE BASICS Developed by Gail Gallo Project Director Chester P. Wichowski Temple University April, 1998 ./1.S. DEPARTMENT OFEDUCATION /ffice of Educational Research and Improvement PERMISSION TO REPRODUCE AND E UCATIONAL RESOURCES INFORMATION DISSEMINATE THIS MATERIAL HAS CENTER (ERIC) BEEN GRANTED BY This document has been reproduced as received from the person or organization originating it. 0 Minor changes have been made to improve reproduction quality. -
Oracle Data Sheet- Secure Enterprise Search
ORACLE SECURE ENTERPRISE SEARCH 11g DATA SHEET ORACLE SECURE ENTERPRISE SEARCH VERSION 11G R2 KEY FEATURES Oracle Secure Enterprise Search 11g (SES), a standalone product RELEASE 11.2.2.2 HIGHLIGHTS from Oracle, enables a high quality, secure search across all Facet Navigation enterprise information assets. Key SES features include: Push-based Content Indexing Multi-tier Install Search Result Tagging The ability to search and locate public, private and shared Unified Microsoft Sharepoint content across intranet web content, databases, files on local Connector, certified for MOSS 2010 version disk or file-servers, IMAP email, document repositories, New search result „hard sort‟ applications, and portals option Auto Suggestions “As You Type” Excellent search quality, with the most relevant items for a query Sitemap.org Support spanning diverse sources being shown first FACET NAVIGATION Full GUI support for facet Sub-second query performance creation, manipulation, and browsing. No programming required. Facet API support is Highly secure crawling, indexing, and searching also provided Facet navigation is secure. Integration with Desktop Search tools Facet values and counts are computed using only those documents that the search Ease of administration and maintenance – a „no-DBA‟ approach user is authorized to see Hierarchical- and range facets to Search. Date, number and string types Update facets without re- Information Uplift for the Intranet crawling As a result of search engines on the Internet, the power of effective search PUSH-BASED CONTENT technologies has become clear to everyone. Using the World Wide Web, consumers INDEXING have become their own information retrieval experts. But search within enterprises Allows customers to push differs radically from public Internet search. -
Dining Hall,” “Cafeteria,” and “Campus Food Service” • Be Specific As You Learn More – E.G
THE INTERNET Conducting Internet Research Computer Applications I Martin Santos Jorge Cab Objectives • After completing this section, students will be able to: • Understand the internet • Identify the different tools for research • Use and cite references from the internet Lecturers: Martin Santos/Jorge Cab (S.P.J.C.) 2 Vocabulary List • Internet (the Net): a global connection of millions of computer networks • Browser: software that helps a user access web sites (Internet Explorer and Netscape) • Server: a computer that runs special software and sends information over the Internet when requested • World Wide Web (the Web or www.): multimedia portion of the Internet consisting of text, graphics, audio and video • URL: stands for Uniform Resource Locator. It is the website's “address” or what the user types in to make the connection • Web site: a “virtual” place on the Internet with a unique URL • Virtual: “mental” replica of something - you can’t “touch” it – need a “tool” to get to it • Web page: a place on a web site where specific information is located • Home page: main page of a web site and first page to load when a site is accessed • Hyperlink: “clickable” text or graphics – takes you from one place to another – usually underlined and shows a hand shaped icon • Hypertext: capability to “link” or “jump” to other references or cross references by clicking • Cyberspace: “electronic” universe where information from one computer connects with another • Upload: process of transferring information to a page/site on the internet • Download: process of transferring information to a computer • Search engine: a site that scans the contents of other web sites to create a large index of information • Domain (top level): code located in the URL representing the type of organization (i.e., .gov (government), .edu (education), .mil (military), .org (organization – non-profit), .com (commercial – a business – for profit) • Internet Service Provider (ISP): a company with direct connection to the Internet that grants subscribers access to various Internet services. -
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. -
Dogpile.Com First to Combine Search Results from MSN Search with Google, Yahoo and Ask Jeeves
Dogpile.com First to Combine Search Results From MSN Search with Google, Yahoo and Ask Jeeves With the Addition of MSN Search, Dogpile.com Users Can Efficiently Find More of the Web's Most Relevant Search Results in One Place BELLEVUE, Wash. – August 2, 2005 – Dogpile.com today announced that search results from MSN Search are now available on the Web's leading metasearch engine. By becoming the first to combine results from the four leading search sites—MSN Search, Google, Yahoo and Ask Jeeves—Dogpile.com gives consumers the most comprehensive view of the Web and helps them efficiently retrieve the most relevant results. The addition of MSN Search to Dogpile.com further extends Dogpile.com's differentiation from any single search engine. Most people believe search results across all four engines are the same, when, in fact, the vast majority of the results from each engine are different. According to a new study, researchers at the University of Pittsburgh and the Pennsylvania State University evaluated 12,570 random queries run on MSN Search, Google, Yahoo and Ask Jeeves. They found only 1.1 percent of the first page results are the same across all four engines. The full results of the study can be found at http://CompareSearchEngines.dogpile.com/whitepaper. "By bringing together the best results from the top engines on Dogpile.com, consumers can be confident they are receiving the most relevant results," said Brian Bowman, vice president of marketing and product management for InfoSpace Search & Directory. Dogpile.com has built a tool that allows consumers to compare the results of the leading engines for themselves. -
Evaluation of Web-Based Search Engines Using User-Effort Measures
Evaluation of Web-Based Search Engines Using User-Effort Measures Muh-Chyun Tang and Ying Sun 4 Huntington St. School of Information, Communication and Library Studies Rutgers University, New Brunswick, NJ 08901, U.S.A. [email protected] [email protected] Abstract This paper presents a study of the applicability of three user-effort-sensitive evaluation measures —“first 20 full precision,” “search length,” and “rank correlation”—on four Web-based search engines (Google, AltaVista, Excite and Metacrawler). The authors argue that these measures are better alternatives than precision and recall in Web search situations because of their emphasis on the quality of ranking. Eight sets of search topics were collected from four Ph.D. students in four different disciplines (biochemistry, industrial engineering, economics, and urban planning). Each participant was asked to provide two topics along with the corresponding query terms. Their relevance and credibility judgment of the Web pages were then used to compare the performance of the search engines using these three measures. The results show consistency among these three ranking evaluation measures, more so between “first 20 full precision” and search length than between rank correlation and the other two measures. Possible reasons for rank correlation’s disagreement with the other two measures are discussed. Possible future research to improve these measures is also addressed. Introduction The explosive growth of information on the World Wide Web poses a challenge to traditional information retrieval (IR) research. Other than the sheer amount of information, some structural factors make searching for relevant and quality information on the Web a formidable task. -
Magnify Search Security and Administration Release 8.2 Version 04
Magnify Search Security and Administration Release 8.2 Version 04 April 08, 2019 Active Technologies, EDA, EDA/SQL, FIDEL, FOCUS, Information Builders, the Information Builders logo, iWay, iWay Software, Parlay, PC/FOCUS, RStat, Table Talk, Web390, WebFOCUS, WebFOCUS Active Technologies, and WebFOCUS Magnify are registered trademarks, and DataMigrator and Hyperstage are trademarks of Information Builders, Inc. Adobe, the Adobe logo, Acrobat, Adobe Reader, Flash, Adobe Flash Builder, Flex, and PostScript are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States and/or other countries. Due to the nature of this material, this document refers to numerous hardware and software products by their trademarks. In most, if not all cases, these designations are claimed as trademarks or registered trademarks by their respective companies. It is not this publisher's intent to use any of these names generically. The reader is therefore cautioned to investigate all claimed trademark rights before using any of these names other than to refer to the product described. Copyright © 2019, by Information Builders, Inc. and iWay Software. All rights reserved. Patent Pending. This manual, or parts thereof, may not be reproduced in any form without the written permission of Information Builders, Inc. Contents Preface ......................................................................... 7 Conventions ......................................................................... 7 Related Publications ................................................................. -
Enterprise Search Technology Using Solr and Cloud Padmavathy Ravikumar Governors State University
Governors State University OPUS Open Portal to University Scholarship All Capstone Projects Student Capstone Projects Spring 2015 Enterprise Search Technology Using Solr and Cloud Padmavathy Ravikumar Governors State University Follow this and additional works at: http://opus.govst.edu/capstones Part of the Databases and Information Systems Commons Recommended Citation Ravikumar, Padmavathy, "Enterprise Search Technology Using Solr and Cloud" (2015). All Capstone Projects. 91. http://opus.govst.edu/capstones/91 For more information about the academic degree, extended learning, and certificate programs of Governors State University, go to http://www.govst.edu/Academics/Degree_Programs_and_Certifications/ Visit the Governors State Computer Science Department This Project Summary is brought to you for free and open access by the Student Capstone Projects at OPUS Open Portal to University Scholarship. It has been accepted for inclusion in All Capstone Projects by an authorized administrator of OPUS Open Portal to University Scholarship. For more information, please contact [email protected]. ENTERPRISE SEARCH TECHNOLOGY USING SOLR AND CLOUD By Padmavathy Ravikumar Masters Project Submitted in partial fulfillment of the requirements For the Degree of Master of Science, With a Major in Computer Science Governors State University University Park, IL 60484 Fall 2014 ENTERPRISE SEARCH TECHNOLOGY USING SOLR AND CLOUD 2 Abstract Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database in9tegration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. -
How to Choose a Search Engine Or Directory
How to Choose a Search Engine or Directory Fields & File Types If you want to search for... Choose... Audio/Music AllTheWeb | AltaVista | Dogpile | Fazzle | FindSounds.com | Lycos Music Downloads | Lycos Multimedia Search | Singingfish Date last modified AllTheWeb Advanced Search | AltaVista Advanced Web Search | Exalead Advanced Search | Google Advanced Search | HotBot Advanced Search | Teoma Advanced Search | Yahoo Advanced Web Search Domain/Site/URL AllTheWeb Advanced Search | AltaVista Advanced Web Search | AOL Advanced Search | Google Advanced Search | Lycos Advanced Search | MSN Search Search Builder | SearchEdu.com | Teoma Advanced Search | Yahoo Advanced Web Search File Format AllTheWeb Advanced Web Search | AltaVista Advanced Web Search | AOL Advanced Search | Exalead Advanced Search | Yahoo Advanced Web Search Geographic location Exalead Advanced Search | HotBot Advanced Search | Lycos Advanced Search | MSN Search Search Builder | Teoma Advanced Search | Yahoo Advanced Web Search Images AllTheWeb | AltaVista | The Amazing Picture Machine | Ditto | Dogpile | Fazzle | Google Image Search | IceRocket | Ixquick | Mamma | Picsearch Language AllTheWeb Advanced Web Search | AOL Advanced Search | Exalead Advanced Search | Google Language Tools | HotBot Advanced Search | iBoogie Advanced Web Search | Lycos Advanced Search | MSN Search Search Builder | Teoma Advanced Search | Yahoo Advanced Web Search Multimedia & video All TheWeb | AltaVista | Dogpile | Fazzle | IceRocket | Singingfish | Yahoo Video Search Page Title/URL AOL Advanced -
Analysis of Query Keywords of Sports-Related Queries Using Visualization and Clustering
Analysis of Query Keywords of Sports-Related Queries Using Visualization and Clustering Jin Zhang and Dietmar Wolfram School of Information Studies, University of Wisconsin—Milwaukee, Milwaukee, WI 53201. E-mail: {jzhang, dwolfram}@uwm.edu Peiling Wang School of Information Sciences, College of Communication and Information, University of Tennessee at Knoxville, Knoxville, TN 37996–0341. E-mail: [email protected] The authors investigated 11 sports-related query key- the user’s request includes the client Internet Protocol (IP) words extracted from a public search engine query log address, request date/time, page requested, HTTP code, bytes to better understand sports-related information seeking served, user agent, referrer, and so on. The data are kept in on the Internet. After the query log contents were cleaned and query data were parsed, popular sports-related key- a standard format in a transaction log file (Hallam-Baker & words were identified, along with frequently co-occurring Behlendorf, 2008). query terms associated with the identified keywords. Although a transaction log comprises rich data, including Relationships among each sports-related focus keyword browsing times and traversal paths, it is the queries directly and its related keywords were characterized and grouped submitted by users that have attracted the most research using multidimensional scaling (MDS) in combination with traditional hierarchical clustering methods. The two attention. Query data contain keywords that reflect users’ approaches were synthesized in a visual context by high- wide-ranging information needs. Query logs have been ana- lighting the results of the hierarchical clustering analysis lyzed from a variety of sources with different emphases and in the visual MDS configuration. -
A Meta Search Engine Based on a New Result Merging Strategy
Usearch: A Meta Search Engine based on a New Result Merging Strategy Tarek Alloui1, Imane Boussebough2, Allaoua Chaoui1, Ahmed Zakaria Nouar3 and Mohamed Chaouki Chettah3 1MISC Laboratory, Department of Computer Science and its Applications, Faculty of NTIC, University Constantine 2 Abdelhamid Mehri, Constantine, Algeria 2LIRE Laboratory, Department of Software Technology and Information Systems, Faculty of NTIC, University Constantine 2 Abdelhamid Mehri, Constantine, Algeria 3Department of Computer Science and its Applications, Faculty of NTIC, University Constantine 2, Abdelhamid Mehri, Constantine, Algeria Keywords: Meta Search Engine, Ranking, Merging, Score Function, Web Information Retrieval. Abstract: Meta Search Engines are finding tools developed for improving the search performance by submitting user queries to multiple search engines and combining the different search results in a unified ranked list. The effectiveness of a Meta search engine is closely related to the result merging strategy it employs. But nowadays, the main issue in the conception of such systems is the merging strategy of the returned results. With only the user query as relevant information about his information needs, it’s hard to use it to find the best ranking of the merged results. We present in this paper a new strategy of merging multiple search engine results using only the user query as a relevance criterion. We propose a new score function combining the similarity between user query and retrieved results and the users’ satisfaction toward used search engines. The proposed Meta search engine can be used for merging search results of any set of search engines. 1 INTRODUCTION advantages of MSEs are their abilities to combine the coverage of multiple search engines and to reach Nowadays, the World Wide Web is considered as the deep Web. -
BI SEARCH and TEXT ANALYTICS New Additions to the BI Technology Stack
SECOND QUARTER 2007 TDWI BEST PRACTICES REPORT BI SEARCH AND TEXT ANALYTICS New Additions to the BI Technology Stack By Philip Russom TTDWI_RRQ207.inddDWI_RRQ207.indd cc11 33/26/07/26/07 111:12:391:12:39 AAMM Research Sponsors Business Objects Cognos Endeca FAST Hyperion Solutions Corporation Sybase, Inc. TTDWI_RRQ207.inddDWI_RRQ207.indd cc22 33/26/07/26/07 111:12:421:12:42 AAMM SECOND QUARTER 2007 TDWI BEST PRACTICES REPORT BI SEARCH AND TEXT ANALYTICS New Additions to the BI Technology Stack By Philip Russom Table of Contents Research Methodology and Demographics . 3 Introduction to BI Search and Text Analytics . 4 Defining BI Search . 5 Defining Text Analytics . 5 The State of BI Search and Text Analytics . 6 Quantifying the Data Continuum . 7 New Data Warehouse Sources from the Data Continuum . 9 Ramifications of Increasing Unstructured Data Sources . .11 Best Practices in BI Search . 12 Potential Benefits of BI Search . 12 Concerns over BI Search . 13 The Scope of BI Search . 14 Use Cases for BI Search . 15 Searching for Reports in a Single BI Platform Searching for Reports in Multiple BI Platforms Searching Report Metadata versus Other Report Content Searching for Report Sections Searching non-BI Content along with Reports BI Search as a Subset of Enterprise Search Searching for Structured Data BI Search and the Future of BI . 18 Best Practices in Text Analytics . 19 Potential Benefits of Text Analytics . 19 Entity Extraction . 20 Use Cases for Text Analytics . 22 Entity Extraction as the Foundation of Text Analytics Entity Clustering and Taxonomy Generation as Advanced Text Analytics Text Analytics Coupled with Predictive Analytics Text Analytics Applied to Semi-structured Data Processing Unstructured Data in a DBMS Text Analytics and the Future of BI .