The Essential Guide to Enterprise Search in Sharepoint 2013 Everything You Need to Know to Get the Most out of Search and Search-Based Applications About the Authors
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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. -
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. -
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 . -
Text Analysis: the Next Step in Search
eDiscovery & Information Management Text Analysis: The Next Step In Search ZyLAB White Paper Johannes C. Scholtes, Ph.D. Chief Strategy Officer, ZyLAB Contents Summary 3 Finding Without Knowing Exactly What to Look For 4 Beyond the Google Standard 4 Challenges Facing Text Analysis 6 Control of Unstructured Information 6 Different Levels of Semantic Information Extraction 7 Co-reference and Anaphora Resolution 11 Faceted Search and Information Visualization 12 Text Analysis on Non-English Documents 15 Content Analytics on Multimedia Files: Audio Search 16 A Prosperous Future for Text Analysis 17 About ZyLAB 19 Summary Text and content analysis differs from traditional search in that, whereas search requires a user to know what he or she is looking for, text analysis attempts to discover information in a pattern that is not known before- hand. One of the most compelling differences with regular (web) search is that typical search engines are optimized to find only the most relevant documents; they are not optimized to find all relevant documents. The majority of commonly-used search tools are built to retrieve only the most popular hits—which simply doesn’t meet the demands of exploratory legal search. This whitepaper will lead the reader beyond the Google standard, explore the limitations and possibilities of text analysis technology and show how text analysis becomes an essential tool to help process and analyze to- day’s enormous amounts of enterprise information in a timely fashion. 3 Finding Without Knowing Exactly What to Look For In general, text analysis refers to the process of extracting interesting and non-trivial information and knowledge from unstructured text. -
Searching the Enterprise
R Foundations and Trends• in Information Retrieval Vol. 11, No. 1 (2017) 1–142 c 2017 U. Kruschwitz and C. Hull • DOI: 10.1561/1500000053 Searching the Enterprise Udo Kruschwitz Charlie Hull University of Essex, UK Flax, UK [email protected] charlie@flax.co.uk Contents 1 Introduction 2 1.1 Overview........................... 3 1.2 Examples........................... 5 1.3 PerceptionandReality . 9 1.4 RecentDevelopments . 10 1.5 Outline............................ 11 2 Plotting the Landscape 13 2.1 The Changing Face of Search . 13 2.2 DefiningEnterpriseSearch . 14 2.3 Related Search Areas and Applications . 17 2.4 SearchTechniques. 34 2.5 Contextualisation ...................... 37 2.6 ConcludingRemarks. 49 3 Enterprise Search Basics 52 3.1 StructureofData ...................... 53 3.2 CollectionGathering. 59 3.3 SearchArchitectures. 63 3.4 Information Needs and Applications . 68 3.5 SearchContext ....................... 76 ii iii 3.6 UserModelling........................ 78 3.7 Tools, Frameworks and Resources . 81 4 Evaluation 82 4.1 RelevanceandMetrics. 83 4.2 Evaluation Paradigms and Campaigns . 85 4.3 TestCollections ....................... 89 4.4 LessonsLearned ....................... 94 5 Making Enterprise Search Work 95 5.1 PuttingtheUserinControl . 96 5.2 Relevance Tuning and Support . 103 6 The Future 110 6.1 GeneralTrends........................ 110 6.2 TechnicalDevelopments. 111 6.3 Moving towards Cooperative Search . 113 6.4 SomeResearchChallenges . 114 6.5 FinalWords ......................... 117 7 Conclusion 118 Acknowledgements 120 References 121 Abstract Search has become ubiquitous but that does not mean that search has been solved. Enterprise search, which is broadly speaking the use of information retrieval technology to find information within organisa- tions, is a good example to illustrate this. -
Opentext Magellan Text Mining Helps Users Gain Insight from Unstructured Content How to Uncover Insights and Information That Optimize Organizations' Content
WHITE PAPER OpenText Magellan Text Mining helps users gain insight from unstructured content How to uncover insights and information that optimize organizations' content. Contents Introduction: Mining text for meaning 3 Definitions 4 Creating and using semantic metadata 6 Automated metadata assignment 6 Semi-automated metadata assignment 6 Methodology 7 Statistical patterns 7 Grammatical patterns 7 Machine learning 8 Decision trees 8 Post-processing algorithms 8 Knowledge engineering 9 Magellan Text Mining modules and architecture 9 Concept Extractor 10 Named Entity Extractor 10 Categorizer 10 Summarizer 11 Sentiment Analyzer 11 Language Detector 12 Additional Magellan Text Mining components 12 Conclusion 13 OpenText Magellan Text Mining helps users gain insight from unstructured content 2/13 OpenText Magellan Text Mining helps users gain insight from unstructured content. OpenText Magellan Text Mining enables enterprises to take control of their knowledge assets to manage and grow their business efficiently. Using thoughtfully selected text analytics techniques, such as metadata federation or crawlers to access data from multiple repositories, this tool can extract from content the meaningful pieces of information and help users connect with the content most relevant to them. This white paper focuses on how Magellan Text Mining streamlines and speeds up a key task of content analytics, the semantic annotation of content, to make documents more “findable” and usable for uses ranging from indexing and content curation to claim form processing and creating new, value-added content products. It takes on the task of tagging content with semantic metadata, traditionally done manually, and frees up workers from many hours of repetitive labor to exert more judgment in content management. -
Method of Semantic Refinement for Enterprise Search
Method of Semantic Refinement for Enterprise Search Alexey Pismak a, Serge Klimenkov b, Eugeny Tsopa c, Alexandr Yarkeev d, Vladimir Nikolaev e and Anton Gavrilov f ITMO University, Kronverksky pr 49, Saint-Petersburg, Russia Keywords: Semantic Networks, Translingual Data, Apache Lucene, Semantic Queries, Semantic Search, Pertinence of Search Results, Ontologies. Abstract: In this paper, we propose an approach of using the semantic refinement of the input search query for the enterprise search systems. The problem of enterprise search is actual because of the amount of processed data. Even with a good organization of documents, the process of searching for specific documents or specific data in these documents is very laborious. But even more significant problem is that the required content may have the matching meaning, but expressed with different words in the different languages, which prevents it from appearing in the search result. The proposed approach uses semantic refinement of the search query. First, the concepts are extracted from the semantic network based on translingual lexemes of the user query string, allowing to perform the search based on the senses rather than word forms. In addition, several rules are applied to the query in order to include or exclude senses which can affect the relevance and the pertinence of the search result. 1 INTRODUCTION set are closely related to each other and they usually belong to a common domain. Search systems are the mandatory component of any 2. Large number of documents. Typical digital environment of a modern enterprise. enterprise system stores a large set (from thousands Generally, the search in document databases is to millions) of different documents in various carried out by methods of grammatical full-text formats. -
Develop an Enterprise Search Strategy April 2014 Abstract
Develop an Enterprise Search Strategy April 2014 Abstract A decision to implement enterprise search cannot be taken lightly. Many companies end up frustrated with high priced products that failed to live up to their expectations. However, these companies typically put little effort into creating a compelling search experience especially given the potential productivity gains effective search can bring. To avoid the failed search experience, information and knowledge management professionals should follow these steps to maximize the impact of their search investments, while minimizing the risk of over-investing in the technology. Content Growth Demands Better Information Access Tools The past 10 years have seen both the volume and diversity of digital content within enterprises grow at unprecedented rates. Increased use of departmental file shares, collaboration tools, content management systems, messaging systems with file attachments, corporate blogs and wikis, and databases has turned corporate networks into a virtual mix of content where useless work-in-process, duplicate, and untraceable documents are mixed with valuable information needed to get work done. Despite IT efforts to control content through the use of content management systems, only a small percentage of content that gets created makes it into a managed repository like an enterprise portal or content management system. This results in: Hard to use, “system of record” document management repositories. Many of these systems have proven so complex to use that they have been relegated to storing specific authoritative information like business records, parts manuals, and formal methods and procedures that changes infrequently or must be retained for extended time period of time for legal reasons. -
G001 Speakers
New Frontiers in Enterprise Search Monday, August 20, 2018 11:00am-12:00pm #G001 SPEAKERS TODD FRIEDLICH RICK KRZYMINSKI SIMON PECOVNIK Senior Manager KM Technology & Chief Knowledge VP of Product Management Innovation Management Officer iManage LLC Ropes & Gray LLP Baker Donelson @tfriedlich CONVERSATIONAL SEARCH • Conversational Search • Voice Search PERSONAL ASSISTANTS CHATBOTS @ HOME • “Alexa, Turn off the lights.” • “OK Google, Set alarm for 6:30am” • “Siri, Where is ‘Eight Grade’ playing” I thought this was a session on search! Leslie Knope in the Alexa, Pawnee office is an Who is an expert on expert in Blockchain Blockchain? “I ALWAYS FEEL LIKE SOMEBODY'S WATCHING ME.” “Siri is listening to you, but she’s not spying” (Naked Security by Sophos) “Google tracks your movements, like it or not” (AP) “Here's how the Alexa spying scandal could become Amazon's worst nightmare” (Business Insider) NATURAL LANGUAGE PROCESSING (NLP) Intent Entity Who is an expert in Bitcoin? ContentSource:”Expertise” AND Skill:”Bitcoin” SIMPLE ANSWERS? Leslie Knope has experience with bitcoin. CANONICAL TERMS AND SYNONYMS Intent Entity Who is an expert in Bitcoin?Bitcoin? CANONICAL TERMS AND SYNONYMS Smart Contracts Who is an expert in Bitcoin? Ethereum Blockchain CLARIFYING QUESTIONS Leslie Knope has experience with bitcoin. Bitcoin is often a synonym for Blockchain, Ethereum, and Smart Contracts. Would you like me to expand my search to include those terms? MORE CLARIFICATION Contextual Conversational • What do we know about the user? • Expand search to include -
Image and Video Searching on the World Wide Web
Image and Video Searching on the World Wide Web Michael J. Swain Cambridge Research Laboratory Compaq Computer Corporation One Kendall Square, Bldg. 700 Cambridge, MA 02139, USA [email protected] Abstract The proliferation of multimedia on the World Wide Web has led to the introduction of Web search engines for images, video, and audio. On the Web, multimedia is typically embedded within documents that provide a wealth of indexing information. Harsh computational constraints imposed by the economics of advertising-supported searches restrict the complexity of analysis that can be performed at query time. And users may be unwilling to do much more than type a keyword or two to input a query. Therefore, the primary sources of information for indexing multimedia documents are text cues extracted from HTML pages and multimedia document headers. Off-line analysis of the content of multimedia documents can be successfully employed in Web search engines when combined with these other information sources. Content analysis can be used to categorize and summarize multimedia, in addition to providing cues for finding similar documents. This paper was delivered as a keynote address at the Challenge of Image Retrieval ’99. It represents a personal and purposefully selective review of image and video searching on the World Wide Web. 1 Introduction The World Wide Web is full of images, video, and audio, as well as text. Search engines are starting to appear that can allow users to find such multimedia, the quantity of which is growing even faster than text on the Web. As 56 kbps (V.90) modems have become standardized and widely used, and as broadband cable modem and telephone-network based Digital Subscribe Line (DSL) services gain following in the United States and Europe, multimedia on the Web is becoming freed of its major impediment: low-bandwidth consumer Internet connectivity. -
Search Computing Cover.Indd
Search Computing Business Areas, Research and Socio-Economic Challenges Media Search Cluster White Paper European Commission European SocietyInformation and Media LEGAL NOTICE By the Commission of the European Communities, Information Society & Media Directorate-General, Future and Emerging Technologies units. Neither the European Commission nor any person acting on its behalf is responsible for the use which might be made of the information contained in the present publication. The European Commission is not responsible for the external web sites referred to in the present publication. The views expressed in this publication are those of the authors and do not necessarily reflect the official European Commission view on the subject. Luxembourg: Publications Office of the European Union, 2011 ISBN 978-92-79-18514-4 doi:10.2759/52084 © European Union, July 2011 Reproduction is authorised provided the source is acknowledged. © Cover picture: Alinari 24 ORE, Firenze, Italy Printed in Belgium White paper: Search Computing: Business Areas, Research and Socio-Economic Challenges Search Computing: Business Areas, Research and Socio- Economic Challenges Media Search Cluster White Paper Media Search Cluster - 2011 Page 1 White paper: Search Computing: Business Areas, Research and Socio-Economic Challenges Coordinated by the CHORUS+ project co-funded by the European Commission under - 7th Framework Programme (2007-2013) by the –Networked Media and Search Systems Unit of DG INFSO Media Search Cluster - 2011 Page 2 White paper: Search Computing: