Towards the Ontology Web Search Engine
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DEWS: a Decentralized Engine for Web Search
DEWS: A Decentralized Engine for Web Search Reaz Ahmed, Rakibul Haque, Md. Faizul Bari, Raouf Boutaba David R. Cheriton School of Computer Science, University of Waterloo [r5ahmed|m9haque|mfbari|rboutaba]@uwaterloo.ca Bertrand Mathieu Orange Labs, Lannion, France [email protected] (Technical Report: CS-2012-17) Abstract The way we explore the Web is largely governed by centrally controlled, clustered search engines, which is not healthy for our freedom in the Internet. A better solution is to enable the Web to index itself in a decentralized manner. In this work we propose a decentralized Web search mechanism, named DEWS, which will enable the existing webservers to collaborate with each other to form a distributed index of the Web. DEWS can rank the search results based on query keyword relevance and relative importance of websites. DEWS also supports approximate matching of query keywords and incremental retrieval of search results in a decentralized manner. We use the standard LETOR 3.0 dataset to validate the DEWS protocol. Simulation results show that the ranking accuracy of DEWS is very close to the centralized case, while network overhead for collaborative search and indexing is logarithmic on network size. Simulation results also show that DEWS is resilient to changes in the available pool of indexing webservers and works efficiently even in presence of heavy query load. 1 Introduction Internet is the largest repository of documents that man kind has ever created. Voluntary contributions from millions of Internet users around the globe, and decentralized, autonomous hosting infrastructure are the sole factors propelling the continuous growth of the Internet. -
PDF-Xchange Viewer
PDF-XChange Viewer © 2001-2011 Tracker Software Products Ltd North/South America, Australia, Asia: Tracker Software Products (Canada) Ltd., PO Box 79 Chemainus, BC V0R 1K0, Canada Sales & Admin Tel: Canada (+00) 1-250-324-1621 Fax: Canada (+00) 1-250-324-1623 European Office: 7 Beech Gardens Crawley Down., RH10 4JB Sussex, United Kingdom Sales Tel: +44 (0) 20 8555 1122 Fax: +001 250-324-1623 http://www.tracker-software.com [email protected] Support: [email protected] Support Forums: http://www.tracker-software.com/forum/ ©2001-2011 TRACKER SOFTWARE PRODUCTS II PDF-XChange Viewer v2.5x Table of Contents INTRODUCTION...................................................................................................... 7 IMPORTANT! FREE vs. PRO version ............................................................................................... 8 What Version Am I Running? ............................................................................................................................. 9 Safety Feature .................................................................................................................................................. 10 Notice! ......................................................................................................................................... 10 Files List ....................................................................................................................................... 10 Latest (available) Release Notes ................................................................................................. -
Lookeen Desktop Search
Lookeen Desktop Search Find your files faster! User Benefits Save time by simultaneously searching for documents on your hard drive, in file servers and the network. Lookeen can also search Outlook archives, the Exchange Search with fast and reliable Server and Public Folders. Advanced filters and wildcard options make search more Lookeen technology powerful. With Lookeen you’ll turn ‘search’ into ‘find’. You’ll be able to manage and organize large amounts of data efficiently. Employees will save valuable time usually Find your information in record spent searching to work on more important tasks. time thanks to real-time indexing Lookeen desktop search can also Search your desktop, Outlook be integrated into Outlook The search tool for Windows files and Exchange folders 10, 8, 7 and Vista simultaneously Ctrl+Ctrl is back: instantly launch Edit and save changes to Lookeen from documents in Lookeen preview anywhere on your desktop Save and re-use favorite queries and access them with short keys View all correspondence with individuals or groups at the push of a button Create one-click summaries of email correspondences Start saving time and money immediately For Companies Features Business Edition Desktop search software compatible with Powerful search in virtual environments like Compatible with standard and virtual Windows 10, 8, 7 and Vista Citrix and VMware desktops like Citrix, VMware and Terminal Servers. Simplified roll out through exten- Optional add-in to Microsoft Outlook 2016, Simple, user friendly interface gives users a sive group directives and ADM files. 2013, 2010, 2007 or 2003 and Office 365 unified view over multiple data sources Automatic indexing of all files on the hard Clear presentation of search results drive, network, file servers, Outlook PST/OST- Enterprise Edition Full fidelity preview option archives, Public Folders and the Exchange Scans additional external indexes. -
An Activity Based Data Model for Desktop Querying (Extended Abstract)?
An activity based data model for desktop querying (Extended Abstract)? Sibel Adalı1 and Maria Luisa Sapino2 1 Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA, [email protected], 2 Universit`adi Torino, Corso Svizzera, 185, I-10149 Torino, Italy [email protected] 1 Introduction With the introduction of a variety of desktop search systems by popular search engines as well as the Mac OS operating system, it is now possible to conduct keyword search across many types of documents. However, this type of search only helps the users locate a very specific piece of information that they are looking for. Furthermore, it is possible to locate this information only if the document contains some keywords and the user remembers the appropriate key- words. There are many cases where this may not be true especially for searches involving multimedia documents. However, a personal computer contains a rich set of associations that link files together. We argue that these associations can be used easily to answer more complex queries. For example, most files will have temporal and spatial information. Hence, files created at the same time or place may have relationships to each other. Similarly, files in the same directory or people addressed in the same email may be related to each other in some way. Furthermore, we can define a structure called “activities” that makes use of these associations to help user accomplish more complicated information needs. Intu- itively, we argue that a person uses a personal computer to store information relevant to various activities she or he is involved 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. -
Organizing User Search Histories
Global Journal of Computer Science and Technology Network, Web & Security Volume 13 Issue 13 Version 1.0 Year 2013 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172 & Print ISSN: 0975-4350 Organizing user Search Histories By Ravi Kumar Yandluri Gokaraju Rangaraju Institute of Engineering & Technology, India Abstract - Internet userscontinuously make queries over web to obtain required information. They need information about various tasks and sub tasks for which they use search engines. Over a period of time they make plenty of related queries. Search engines save these queries and maintain user’s search histories. Users can view their search histories in chronological order. However, the search histories are not organized into related groups. In fact there is no organization made except the chronological order. Recently Hwang et al. studied the problem of organizing historical search information of users into groups dynamically. This automatic grouping of user search histories can help search engines also in various applications such as collaborative search, sessionization, query alterations, result ranking and query suggestions. They proposed various techniques to achieve this. In this paper we implemented those techniques practically using a prototype web application built in Java technologies. The experimental results revealed that the proposed application is useful to organize search histories. Indexterms : search engine, search history, click graph, query grouping. GJCST-E Classification : H.3.5 Organizing user Search Histories Strictly as per the compliance and regulations of: © 2013. Ravi Kumar Yandluri. This is a research/review paper, distributed under the terms of the Creative Commons Attribution- Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction inany medium, provided the original work is properly cited. -
A Community-Based Approach to Personalizing Web Search
COVER FEATURE A Community-Based Approach to Personalizing Web Search Barry Smyth University College Dublin Researchers can leverage the latent knowledge created within search communities by recording users’search activities—the queries they submit and results they select—at the community level.They can use this data to build a relevance model that guides the promotion of community-relevant results during regular Web search. ver the past few years, current Web search through rates should be preferred during ranking. engines have become the dominant tool for Unfortunately, Direct Hit’s so-called popularity engine accessing information online. However, even did not play a central role on the modern search stage today’s most successful search engines struggle (although the technology does live on as part of the O to provide high-quality search results: Approx- Teoma search engine) largely because the technology imately 50 percent of Web search sessions fail to find proved inept at identifying new sites or less well- any relevant results for the searcher. traveled ones, even though they may have had more The earliest Web search engines adopted an informa- contextual relevance to a given search. tion-retrieval view of search, using sophisticated term- Despite Direct Hit’s fate, the notion that searchers based matching techniques to identify relevant docu- themselves could influence the ranking of results by virtue ments from repeated occurrences of salient query terms. of their search activities remained a powerful one. It res- Although such techniques proved useful for identifying onates well with the ideas that underpin the social web, a set of potentially relevant results, they offered little a phrase often used to highlight the importance of a suite insight into how such results could be usefully ranked. -
Do Metadata Models Meet IQ Requirements?
Do Metadata Mo dels meet IQ Requirements Claudia Rolker Felix Naumann Humb oldtUniversitat zu Berlin Forschungszentrum Informatik FZI Unter den Linden HaidundNeuStr D Berlin D Karlsruhe Germany Germany naumanndbisinformatikhub erli nde rolkerfzide Abstract Research has recognized the imp ortance of analyzing information quality IQ for many dierent applications The success of data integration greatly dep ends on the quality of the individual data In statistical applications p o or data quality often leads to wrong conclusions High information quality is literally a vital prop erty of hospital information systems Po or data quality of sto ck price information services can lead to economically wrong decisions Several pro jects have analyzed this need for IQ metadata and have prop osed a set of IQ criteria or attributes which can b e used to prop erly assess information quality In this pap er we survey and compare these approaches In a second step we take a lo ok at existing prominent prop osals of metadata mo dels esp ecially those on the Internet Then we match these mo dels to the requirements of information quality mo deling Finally we prop ose a quality assurance pro cedure for the assurance of metadata mo dels Intro duction The quality of information is b ecoming increasingly imp ortant not only b ecause of the rapid growth of the Internet and its implication for the information industry Also the anarchic nature of the Internet has made industry and researchers aware of this issue As awareness of quality issues amongst information -
Using Context to Enhance File Search
Connections: Using Context to Enhance File Search Craig A. N. Soules, Gregory R. Ganger Carnegie Mellon University ABSTRACT Attribute-based naming allows users to classify each file Connections is a file system search tool that combines tradi- with multiple attributes [9, 12, 37]. Once in place, these at- tional content-based search with context information gath- tributes provide additional paths to each file, helping users ered from user activity. By tracing file system calls, Con- locate their files. However, it is unrealistic and inappropri- nections can identify temporal relationships between files ate to require users to proactively provide accurate and use- and use them to expand and reorder traditional content ful classifications. To make these systems viable, they must search results. Doing so improves both recall (reducing false- automatically classify the user's files, and, in fact, this re- positives) and precision (reducing false-negatives). For ex- quirement has led most systems to employ search tools over ample, Connections improves the average recall (from 13% hierarchical file systems rather than change their underlying to 22%) and precision (from 23% to 29%) on the first ten methods of organization. results. When averaged across all recall levels, Connections The most prevalent automated classification method to- improves precision from 17% to 28%. Connections provides day is content analysis: examining the contents and path- these benefits with only modest increases in average query names of files to determine attributes that describe them. time (2 seconds), indexing time (23 seconds daily), and in- Systems using attribute-based naming, such as the Seman- dex size (under 1% of the user's data set). -
Dtsearch Desktop/Dtsearch Network Manual
dtSearch Desktop dtSearch Network Version 7 Copyright 1991-2021 dtSearch Corp. www.dtsearch.com SALES 1-800-483-4637 (301) 263-0731 Fax (301) 263-0781 [email protected] TECHNICAL (301) 263-0731 [email protected] 1 Table of Contents 1. Getting Started _____________________________________________________________ 1 Quick Start 1 Installing dtSearch on a Network 7 Automatic deployment of dtSearch on a Network 8 Command-Line Options 10 Keyboard Shortcuts 11 2. Indexes __________________________________________________________________ 13 What is a Document Index? 13 Creating an Index 13 Caching Documents and Text in an Index 14 Indexing Documents 15 Noise Words 17 Scheduling Index Updates 17 3. Indexing Web Sites _________________________________________________________ 19 Using the Spider to Index Web Sites 19 Spider Options 20 Spider Passwords 21 Login Capture 21 4. Sharing Indexes on a Network _________________________________________________ 23 Creating a Shared Index 23 Sharing Option Settings 23 Index Library Manager 24 Searching Using dtSearch Web 25 5. Working with Indexes _______________________________________________________ 27 Index Manager 27 Recognizing an Existing Index 27 Deleting an Index 27 Renaming an Index 27 Compressing an Index 27 Verifying an Index 27 List Index Contents 28 Merging Indexes 28 6. Searching for Documents _____________________________________________________ 29 Using the Search Dialog Box 29 Browse Words 31 More Search Options 32 Search History 33 i Table of Contents Searching for a List of Words 33 7. -
Comparison of Indexers
Comparison of indexers Beagle, JIndex, metaTracker, Strigi Michal Pryc, Xusheng Hou Sun Microsystems Ltd., Ireland November, 2006 Updated: December, 2006 Table of Contents 1. Introduction.............................................................................................................................................3 2. Indexers...................................................................................................................................................4 3. Test environment ....................................................................................................................................5 3.1 Machine............................................................................................................................................5 3.2 CPU..................................................................................................................................................5 3.3 RAM.................................................................................................................................................5 3.4 Disk..................................................................................................................................................5 3.5 Kernel...............................................................................................................................................5 3.6 GCC..................................................................................................................................................5 -
List of Search Engines
A blog network is a group of blogs that are connected to each other in a network. A blog network can either be a group of loosely connected blogs, or a group of blogs that are owned by the same company. The purpose of such a network is usually to promote the other blogs in the same network and therefore increase the advertising revenue generated from online advertising on the blogs.[1] List of search engines From Wikipedia, the free encyclopedia For knowing popular web search engines see, see Most popular Internet search engines. This is a list of search engines, including web search engines, selection-based search engines, metasearch engines, desktop search tools, and web portals and vertical market websites that have a search facility for online databases. Contents 1 By content/topic o 1.1 General o 1.2 P2P search engines o 1.3 Metasearch engines o 1.4 Geographically limited scope o 1.5 Semantic o 1.6 Accountancy o 1.7 Business o 1.8 Computers o 1.9 Enterprise o 1.10 Fashion o 1.11 Food/Recipes o 1.12 Genealogy o 1.13 Mobile/Handheld o 1.14 Job o 1.15 Legal o 1.16 Medical o 1.17 News o 1.18 People o 1.19 Real estate / property o 1.20 Television o 1.21 Video Games 2 By information type o 2.1 Forum o 2.2 Blog o 2.3 Multimedia o 2.4 Source code o 2.5 BitTorrent o 2.6 Email o 2.7 Maps o 2.8 Price o 2.9 Question and answer .