Enterprise Search
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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. -
Pittsburgh Dataworks
Pittsburgh Dataworks Content Marketing Samples By Matthew J. De Reno e. [email protected] | p. (412) 969-1342 | w. PghDataworks.org As the Pittsburgh Dataworks Content Marketing Strategist, I am responsible for creating, managing, and executing the content marketing strategy which includes the duties of social media, content development, PR, event planning, and website management. Pittsburgh Dataworks is a community outreach organization that was founded by IBM to help promote data science in the greater Pittsburgh area. Figure 1: Created image in Photoshop. Notice the “Techsburgh” text over the river! “#Techsburgh” is my effort to create a #hashtag campaign promoting the Pittsburgh technical community (a work in progress – as of Nov. 2015). Select Social Media Figure 2: Created "Data Wars" theme to promote the 2016 Pittsburgh Data Jam. The Pittsburgh Data Jam is the signature annual event of Pittsburgh Dataworks. The goal of this graphic is to engage high school students by tapping into the buzz surrounding the Star Wars film, The Force Awakens, scheduled for release Dec. 2016. Graphic created in Photoshop. Matt De Reno - 2 | P a g e Figure 3: The “Techsburgh” concept repurposed for a general social media post. Matt De Reno - 3 | P a g e Figure 4: General social media posting to keep participating members of Pittsburgh Dataworks interested in the website and aware of new content on the website. Matt De Reno - 4 | P a g e Figure 5: Example of participating in the big data conversation. Figure 6: This posting resulted in the most impressions and engagements for a Pittsburgh Dataworks Twitter posting (as of Nov. -
Summer 2008 Page 5 Letter from the Outgoing President Greetings Band Alumni!
The Herald Summer Pitt Band Alumni Council Newsletter 2008 Alumni Band 2008: Pitt vs. Buffalo, September 5-6 Inside this issue: Letter from the Please return the attached registration form. 2 The planned schedule will be similar to prior years: Band Director Alumni Day 3 Friday, September 5, 2008— Practice Field Behind Cost Center Registration Alumni are invited (as well as encouraged & recommended) to attend an Family Ticket 3 optional rehearsal Friday night down the hill from Trees Hall behind the Order Form Cost Center at 6 pm. We will practice the drill and music for our number Letter from the 5 with the Varsity Band during the practice. President Following the practice, we will have pizza and beverages with the senior members of the band. New Officers 5 Saturday, September 6, 2008— Heinz Field 2:00 Tailgate in parking lot (see below) 5:00 Pre-game concert with the Varsity Band outside of Gate A in the amphitheater. March to Victory with the Varsity Band to Heinz Field following the pre- Things to remember: game concert. Take our seats in Heinz Field for 6:00 kickoff Send in your dues! Post-game Tailgate Register for Alumni Day! Send in your family’s ticket order or call the ticket office by August 29 You're Invited to be Part of a New Tradition! If you know someone who did not receive this newsletter, please share In addition to Alumni Band Day this year, you're invited to tailgate with the Pitt it with them! Band at every Pitt home football game. -
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. -
COMPLAINT I 1 G
1 TABLE OF CONTENTS Page 2 I. INTRODUCTION. 1 3 II. NATURE OF THE ACTION. 6 4 III. JURISDICTION AND VENUE.. 10 5 III. THE PARTIES. 11 6 A. The Plaintiff. 11 7 B. The Nominal Defendant. 11 8 C. The Individual Defendants. 11 9 D. The Bank Defendants.. 14 10 E. The Auditor Defendant. 15 11 F. Unnamed Participants. 15 12 IV. STATEMENT OF FACTS. 16 13 A. A Brief History of the Hewlett-Packard Company. 16 14 B. Mark Hurd Rejects Autonomy Acquisition. 17 15 C. HP’s Recent History of Bad Deals and Failures.. 18 16 D. Road to Autonomy: Léo Apotheker Becomes New CEO.. 20 17 E. HP Acquires Autonomy.. 23 18 1. August 18, 2011: HP Announces Autonomy Acquisition.. 23 19 2. September 13, 2011: HP Hypes The Value of the Transformative 20 Autonomy IDOL Technology in Order to Finalize the Autonomy Acquisition. 28 21 3. September 22, 2011: CEO Léo Apotheker Forced Out of HP; New 22 CEO Meg Whitman Continues to Praise the Autonomy IDOL Technology. 30 23 F. HP Ignored Serious Concerns About The Propriety of the Autonomy 24 Acquisition For $11.7 Billion.. 30 25 1. HP’s Chief Financial Officer Warned HP Against the Autonomy Acquisition. 30 26 2. HP Knew About Multiple Reports of Improprieties at Autonomy 27 and Multiple Red Flags About Autonomy. 31 28 3. Analysts Warned of Autonomy’s Outdated Technology.. 34 DERIVATIVE COMPLAINT i 1 G. Multiple Companies Refuse to Acquire Autonomy Because It Was OverPriced.. 36 2 1. Oracle Warns HP of Autonomy’s Overvaluation. -
(R&D) Tax Credit
Report toThe the Pennsylvania Pennsylvania Department of General Revenue Assembly Bureau of Research on the Research and Development (R&D) Tax Credit The Pennsylvania Department of Revenue Bureau of Research March 15, 2012 Pennsylvania Research and Development Tax Credit Page 1 of 14 The Pennsylvania R&D Tax Credit Statute On May 7, 1997, Act 7 of 1997 created the Pennsylvania research and development (R&D) tax credit. The R&D tax credit provision became Article XVII-B of the Tax Reform Code of 1971 (TRC). The intent of the R&D tax credit was to encourage taxpayers to increase R&D expenditures within the Commonwealth in order to enhance economic growth. The terms and concepts used in the calculation of the Commonwealth’s R&D tax credit are based on the federal government’s R&D tax credit definitions for qualified research expense.1 For R&D tax credits awarded between December 1997 and December 2003, Act 7 of 1997 authorized the Department of Revenue (Department) to approve up to $15 million in total tax credits per fiscal year. Additionally, $3 million of the $15 million was set aside for “small” businesses, where a “small business” is defined as a “for-profit corporation, limited liability company, partnership or proprietorship with net book value of assets totaling…less than five million dollars ($5,000,000).” Over the years, several changes have been made to the R&D tax credit statute. Table 1 lists all of the acts that have changed the R&D tax credit statute, along with the applicable award years, the overall tax credit cap and the “small” business set aside. -
IEEE Pittsburgh Section March 2005 Bulletin
Pittsburgh Section IEEE Bulletin March 2005/Volume 54, No. 7 www.ewh.ieee.org/r2/pittsburgh From Next Generation Search Engines: the Altavista, Google, What ’s Next? Chair Raul Valdes-Perez, Ph.D. The biggest event for the Altavista was the first mass-market web search engine, but it had problems (user experience, month of February was the technical quality) which Google was able to exploit. Users of Google, MSN, and Yahoo face a celebration of Engineers’ different set of issues which competing technologies are racing to exploit. week at the Carnegie Science Center. We thank Vivisimo, a CMU spinoff based in Squirrel Hill, is the leader in one of these technologies: clustering the IEEE members, of search results into meaningful category folders. Vivisimo hosts its own popular web search engine especially the students from at Clusty.com and recently licensed to AOL Search its clustering technology as part of AOL's the University of Pittsburgh stepped-up competition with Google, MSN, and Yahoo. Vivisimo CEO and co-founder Raul Valdes- at Johnstown campus for Perez, Ph.D. will offer an historical perspective, discuss the inefficiencies of web and enterprise volunteering their time at search, and try his hand at foretelling the future. the 12th IEEE Robot Car Mr. Valdes-Perez has been President and Chairman of Vivísimo Inc. since he co-founded it in June Race. The race was between 2000. Before starting Vivísimo, he was on the Carnegie Mellon computer science department faculty 10 schools from around the since 1991; he is now an Adjunct Associate Professor there. -
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.