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Search results “Xml retrieval in web mining definition”
1. Information Retrieval - Introduction and Boolean Retrieval with example
 
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This video explains the Introduction to Information Retrieval with its basic terminology such as: Corpus, Information Need, Relevance etc. It also explains about the types of data i.e. Structured, Unstructured and Semi Structured. This video also contains the detailed explanation of How to create Term Document Incidence Matrix with the help of real world example, which is called as Boolean Retrieval.
Views: 16934 itechnica
XML Database
 
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Subject:Computer Science Paper: Database management system
Views: 1138 Vidya-mitra
Lecture 17 — The Vector Space Model - Natural Language Processing | Michigan
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning & explanation
 
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What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning - STRUCTURE MINING definition - STRUCTURE MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Structure mining or structured data mining is the process of finding and extracting useful information from semi-structured data sets. Graph mining, sequential pattern mining and molecule mining are special cases of structured data mining. The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases. Much of the world's interesting and mineable data does not easily fold into relational databases, though a generation of software engineers have been trained to believe this was the only way to handle data, and data mining algorithms have generally been developed only to cope with tabular data. XML, being the most frequent way of representing semi-structured data, is able to represent both tabular data and arbitrary trees. Any particular representation of data to be exchanged between two applications in XML is normally described by a schema often written in XSD. Practical examples of such schemata, for instance NewsML, are normally very sophisticated, containing multiple optional subtrees, used for representing special case data. Frequently around 90% of a schema is concerned with the definition of these optional data items and sub-trees. Messages and data, therefore, that are transmitted or encoded using XML and that conform to the same schema are liable to contain very different data depending on what is being transmitted. Such data presents large problems for conventional data mining. Two messages that conform to the same schema may have little data in common. Building a training set from such data means that if one were to try to format it as tabular data for conventional data mining, large sections of the tables would or could be empty. There is a tacit assumption made in the design of most data mining algorithms that the data presented will be complete. The other necessity is that the actual mining algorithms employed, whether supervised or unsupervised, must be able to handle sparse data. Namely, machine learning algorithms perform badly with incomplete data sets where only part of the information is supplied. For instance methods based on neural networks. or Ross Quinlan's ID3 algorithm. are highly accurate with good and representative samples of the problem, but perform badly with biased data. Most of times better model presentation with more careful and unbiased representation of input and output is enough. A particularly relevant area where finding the appropriate structure and model is the key issue is text mining. XPath is the standard mechanism used to refer to nodes and data items within XML. It has similarities to standard techniques for navigating directory hierarchies used in operating systems user interfaces. To data and structure mine XML data of any form, at least two extensions are required to conventional data mining. These are the ability to associate an XPath statement with any data pattern and sub statements with each data node in the data pattern, and the ability to mine the presence and count of any node or set of nodes within the document. As an example, if one were to represent a family tree in XML, using these extensions one could create a data set containing all the individuals in the tree, data items such as name and age at death, and counts of related nodes, such as number of children. More sophisticated searches could extract data such as grandparents' lifespans etc. The addition of these data types related to the structure of a document or message facilitates structure mining.
Views: 532 The Audiopedia
XML parsing using R
 
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How to convert the XML data into data frame using R
Views: 4031 Dinesh Sambasivam
R Programming Read XML Node Details
 
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Learn how to Read XML Child Nodes in R Programming.
Views: 3105 DevNami
inex evaluating content oriented xml retrieval
 
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Subscribe today and give the gift of knowledge to yourself or a friend inex evaluating content oriented xml retrieval INEX: Evaluating content-oriented XML retrieval . Mounia Lalmas Queen Mary University of London http://qmir.dcs.qmul.ac.uk. Outline. Content-oriented XML retrieval Evaluating XML retrieval: INEX. XML Retrieval. Slideshow 3032181 by kyrene show1 : Inex evaluating content oriented xml retrieval show2 : Outline show3 : Xml retrieval show4 : Structured documents show5 : Structured documents1 show6 : Xml e x tensible mark up l anguage show7 : Xml e x tensible mark up l anguage1 show8 : Querying xml documents show9 : Content oriented xml retrieval show10 : Content oriented xml retrieval1 show11 : Challenges show12 : Approaches show13 : Vector space model show14 : Language model show15 : Evaluation of xml retrieval inex show16 : Inex test collection show17 : Tasks show18 : Relevance in xml show19 : Relevance in inex show20 : Relevance assessment task show21 : Interface show22 : Assessments show23 : Metrics show24 : Inex 2002 metric show25 : Inex 2002 metric1 show26 : Overlap problem show27 : Inex 2003 metric show28 : Inex 2003 metric1 show29 : Inex 2003 metric2 show30 : Inex 2003 metric3 show31 : Inex 2003 metric4
Views: 67 slideshowing
XML evaluation techniques 1  (Structural Join) In Hindi| GTU | WEB DATA MANAGEMENT
 
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XML evaluation techniques (Structural Join) In Hindi #GTU #WEBDATAMANAGEMENT #XMLevaluationtechniques
Web crawlers and web information retrieval In Hindi | GTU | WEB DATA MANAGEMENT
 
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Know about web crawlers and web information retrieval In Hindi #GTU #WEBDATAMANAGEMENT #Webcrawlers&webinformationretrieval
Introduction to XML | Business Analytics with R | XML Tutorial | XML Tutorial for Beginners |Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) R is one of the most popular languages developed for analytics, and is widely used by statisticians, data scientists and analytics professionals worldwide. Business Analytics with R helps you to strengthen your existing analytics knowledge and methodology with an emphasis on R Programming. Topics covered in the Video: 1.Installing xml Library 2.Running Programs in R Related Posts: http://www.edureka.co/blog/introduction-business-analytics-with-r/?utm_source=youtube&utm_medium=referral&utm_campaign=introduction-to-r Edureka is a New Age e-learning platform that provides Instructor-Led Live Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics, related to Introduction to XML, have been widely covered in our course ‘Business Analytics with R’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 6626 edureka!
Introduction to web Data Management - Course outline
 
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Lecture video by Mustafa Jarrar, Hannna Bullata at Birzeit University, Palestine. See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/introduction-to-data-integration.html and http://www.jarrar.info The lecture covers: Part I: Tree Data Models, by Dr. Hanna Bullata Part II: Graph data models and semantics, Dr. Mustafa Jarrar Part III: Data integration & retrieval, Dr. Mustafa Jarrar
Views: 2151 Jarrar Courses
What is an Ontology
 
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Description of an ontology and its benefits. Please contact [email protected] for more information.
Views: 152863 SpryKnowledge
XML Data Structure
 
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XML Data Structure
Views: 1487 merigrrl
Application of xml in  web
 
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Application of xml in web Source Code of XML - FO: https://www.dropbox.com/s/zg04csg1eq24kcw/DemoDom.rar
Views: 715 Phạm Anh Đới
Web Data Management With XML In Hindi
 
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In this video i have try to introduce the web data with XML means how to use xml with Web data
Read XML Data in R
 
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Learn how to Read XML Data in R Programming Language.
Views: 8556 DevNami
Semi Structured Data
 
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know about Data Type,structured, unstructured,semi structured
Intro to Web Scraping with Python and Beautiful Soup
 
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Web scraping is a very powerful tool to learn for any data professional. With web scraping the entire internet becomes your database. In this tutorial we show you how to parse a web page into a data file (csv) using a Python package called BeautifulSoup. In this example, we web scrape graphics cards from NewEgg.com. Sublime: https://www.sublimetext.com/3 Anaconda: https://www.anaconda.com/distribution/#download-section If you are not seeing the command line, follow this tutorial: https://www.tenforums.com/tutorials/72024-open-command-window-here-add-windows-10-a.html -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz5HN0 Watch the latest video tutorials here: https://hubs.ly/H0hz5SV0 See what our past attendees are saying here: https://hubs.ly/H0hz5K20 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo #webscraping #python
Views: 534100 Data Science Dojo
XML Databases
 
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Subject:Computer Science Paper: Database management system
Views: 289 Vidya-mitra
Databases & information retrieval
 
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Subject:-zoology Course Name:-B.Sc. 3rd year keywords:-SwayamPrabha
Information Retrieval & Extraction
 
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Slides 2-6
Views: 25 Sgabriel136
What is DOCUMENT RETRIEVAL? What does DOCUMENT RETRIEVAL mean? DOCUMENT RETRIEVAL meaning
 
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What is DOCUMENT RETRIEVAL? What does DOCUMENT RETRIEVAL mean? DOCUMENT RETRIEVAL meaning - DOCUMENT RETRIEVAL definition - DOCUMENT RETRIEVAL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Views: 286 The Audiopedia
Chapter 10 XML Databases
 
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In this chapter, we discuss how to store, process, search and visualize XML documents and how DBMSs can support this. We start by looking at the XML data representation standard and discuss related concepts such as DTDs and XSDs for defining XML documents, XSL for visualizing or transforming XML documents, and namespaces to provide for a unique naming convention. This is followed by introducing XPath, which uses path expressions to navigate through XML documents. We review the DOM and SAX API to process XML documents. Next, we cover both the document and data-oriented approach for storing XML documents. We extensively highlight the key differences between the XML and relational data model. Various mapping methods between XML and (object-)relational data are discussed next: table based mapping, schema-oblivious mapping, schema-aware mapping, and the SQL/XML extension. We also present various ways to search XML data: -full-text search, keyword-based search, structured search, XQuery, and semantic search. We then illustrate how XML can be used for information exchange, both at the company level using RPC and Message-Oriented Middleware and between companies using SOAP or REST-based web services. We conclude the chapter by discussing some other data representation standards, such as JSON and YAML.
Views: 884 Bart Baesens
Retrieve text from a html document with XML package of R
 
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Brief demonstration of XML package of R. Easy way to extract text by defining tags of html.
Views: 6274 Yuki
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1631 Kakoli Bandyopadhyay
Lecture -40 XML Databases
 
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Lecture Series on Database Management System by Dr.S.Srinath IIIT Bangalore . For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 36980 nptelhrd
Information Retrieval
 
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Links to resources: http://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf https://en.wikipedia.org/wiki/Vector_space_model
Views: 4514 Andrei Barbu
Lecture 6 —  Vector Space Model  Simplest Instantiation | UIUC
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
International Journal of Web & Semantic Technology (IJWesT)
 
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International Journal of Web & Semantic Technology (IJWesT) ISSN : 0975 - 9026 ( Online ) 0976- 2280 ( Print ) http://www.airccse.org/journal/ijwest/ijwest.html Scope & Topics International journal of Web & Semantic Technology (IJWesT) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of web & semantic technology. The growth of the World-Wide Web today is simply phenomenal. It continues to grow rapidly and new technologies, applications are being developed to support end users modern life. Semantic Technologies are designed to extend the capabilities of information on the Web and enterprise databases to be networked in meaningful ways. Semantic web is emerging as a core discipline in the field of Computer Science & Engineering from distributed computing, web engineering, databases, social networks, Multimedia, information systems, artificial intelligence, natural language processing, soft computing, and human-computer interaction. The adoption of standards like XML, Resource Description Framework and Web Ontology Language serve as foundation technologies to advancing the adoption of semantic technologies. Topics of Interest Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to * Semantic Query & Search * Semantic Advertising and Marketing * Linked Data, Taxonomies * Collaboration and Social Networks * Semantic Web and Web 2.0/AJAX, Web 3.0 * Semantic Case Studies * Ontologies (Creation, Merging, Linking and Reconciliation) * Semantic Integration Rules * Data Integration and Mashups * Unstructured Information * Developing Semantic Applications * Semantics for Enterprise Information Management (EIM) * Knowledge Engineering and Management * Semantic SOA (Service Oriented Architectures) * Database Technologies for the Semantic Web * Semantic Web for E-Business, Governance and E-Learning * Semantic Brokering, Semantic Interoperability, Semantic Web Mining * Semantic Web Services (Service Description, Discovery, Invocation, Composition) * Semantic Web Inference Schemes * Semantic Web Trust, Privacy, Security and Intellectual Property Rights * Information Discovery and Retrieval in Semantic Web; * Web Services Foundation, Architectures and Frameworks. * Web Languages & Web Service Applications. * Web Services-Driven Business Process Management. * Collaborative Systems Techniques. * Communication, Multimedia Applications Using Web Services * Virtualization * Federated Identity Management Systems * Interoperability and Standards * Social and Legal Aspect of Internet Computing * Internet and Web-based Applications and Services Paper Submission Authors are invited to submit papers for this journal through E-mail : [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://www.airccse.org/journal/ijwest/ijwest.html
Views: 23 IJWEST JOURNAL
What is SEMANTIC SEARCH? What does SEMANTIC SEARCH mean? SEMANTIC SEARCH meaning & explanation
 
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What is SEMANTIC SEARCH? What does SEMANTIC SEARCH mean? SEMANTIC SEARCH meaning - SEMANTIC SEARCH definition - SEMANTIC SEARCH explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results. Major web search engines like Google and Bing incorporate some elements of semantic search. In vertical search, LinkedIn publishes their semantic search approach to job search by recognizing and standardizing entities in both queries and documents, e.g., companies, titles and skills, then constructing various entity-awared features based on the entities. Guha et al. distinguish two major forms of search: navigational and research. In navigational search, the user is using the search engine as a navigation tool to navigate to a particular intended document. Semantic search is not applicable to navigational searches. In research search, the user provides the search engine with a phrase which is intended to denote an object about which the user is trying to gather/research information. There is no particular document which the user knows about and is trying to get to. Rather, the user is trying to locate a number of documents which together will provide the desired information. Semantic search lends itself well with this approach that is closely related with exploratory search. Rather than using ranking algorithms such as Google's PageRank to predict relevancy, semantic search uses semantics, or the science of meaning in language, to produce highly relevant search results. In most cases, the goal is to deliver the information queried by a user rather than have a user sort through a list of loosely related keyword results. However, Google itself has subsequently also announced its own Semantic Search project. Author Seth Grimes lists "11 approaches that join semantics to search", and Hildebrand et al. provide an overview that lists semantic search systems and identifies other uses of semantics in the search process. Other authors primarily regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies and XML as found on the Semantic Web. Such technologies enable the formal articulation of domain knowledge at a high level of expressiveness and could enable the user to specify their intent in more detail at query time.
Views: 1295 The Audiopedia
Web Spiders Kya Hote Hai ? / What is Web Crawler Explained In Hindi
 
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Namaskar Dosto !! aaj main aapko web spiders ya crawlers ke bare me bataunga ki ye kya hote hai aur kaise kaam karte hai aasha karta hu apko ye video apsand ayegi. is video ko like kare aur apne dosto ke sath share kare. agar aap naye hai to is channel ko subscribe karna na bhule. Subscribe to my channel for more videos like this and to support my efforts. Thanks and Love #TechnicalSagar LIKE | COMMENT | SHARE | SUBSCRIBE ---------------------------------------------------------------------------------- For all updates : LIKE My Facebook Page https://www.facebook.com/technicalsagarindia Follow Me on Twitter : http://www.twitter.com/iamasagar Follow Abhishek Sagar on Instagram: theabhisheksagar
Views: 36516 Technical Sagar
Basic concepts of XML
 
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XML tutorials for Beginners- This video provides information about basic concepts of XML, how to make customized tags in Html, it contains tutorial videos how to begin with XML, its extensible properties, metadata( data about data),basic terms like structure and semantics of Xml. For More Information Visit http://codesroom.com/
Extended XML Tree Pattern Matching Theories and Algorithms
 
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As business and enterprises generate and exchange XML data more often, there is an increasing need for efficient processing of queries on XML data. Searching for the occurrences of a tree pattern query in an XML database is a core operation in XML query processing. Prior works demonstrate that holistic twig pattern matching algorithm is an efficient technique to answer an XML tree pattern with parent-child (P-C) and ancestor-descendant (A-D) relationships, as it can effectively control the size of intermediate results during query processing. However, XML query languages (e.g., XPath and XQuery) define more axes and functions such as negation function, order-based axis, and wildcards. In this paper, we research a large set of XML tree pattern, called extended XML tree pattern, which may include P-C, A-D relationships, negation functions, wildcards, and order restriction. We establish a theoretical framework about "matching cross" which demonstrates the intrinsic reason in the proof of optimality on holistic algorithms. Based on our theorems, we propose a set of novel algorithms to efficiently process three categories of extended XML tree patterns. A set of experimental results on both real-life and synthetic data sets demonstrate the effectiveness and efficiency of our proposed theories and algorithms.
Views: 132 Renown Technologies
★What is Web Personalization?★
 
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Web personalization is the ability to show different content to different people visiting your website. You want to show the right content to the right people, at the right time. This will improve customer satisfaction, customer loyalty and drive conversions. If you have any questions or comments, feel free to write them below, i will be happy to answer.
Views: 404 Web In Taiwan
IU X-Informatics Unit 21:Web Search and Text Mining 9: Vector Space Models I
 
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Lesson Overview: Vector Space models are attractive as they use techniques that align with many other Big data analytics. basically we view the bag (of words) as a vector. An example is given. Closeness such as with cosine measure can be defined and its features are analyzed. This measure is generalized to the famous TF-IDF measure. Enroll in this course at https://bigdatacourse.appspot.com/ and download course material, see information on badges and more. It's all free and only takes you a few seconds.
What is WEB INDEXING? What does WEB INDEXING mean? WEB INDEXING meaning & explanation
 
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What is WEB INDEXING? What does WEB INDEXING mean? WEB INDEXING meaning - WEB INDEXING definition - WEB INDEXING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Web indexing (or Internet indexing) refers to various methods for indexing the contents of a website or of the Internet as a whole. Individual websites or intranets may use a back-of-the-book index, while search engines usually use keywords and metadata to provide a more useful vocabulary for Internet or onsite searching. With the increase in the number of periodicals that have articles online, web indexing is also becoming important for periodical websites. Back-of-the-book-style web indexes may be called "web site A-Z indexes". The implication with "A-Z" is that there is an alphabetical browse view or interface. This interface differs from that of a browse through layers of hierarchical categories (also known as a taxonomy) which are not necessarily alphabetical, but are also found on some web sites. Although an A-Z index could be used to index multiple sites, rather than the multiple pages of a single site, this is unusual. Metadata web indexing involves assigning keywords or phrases to web pages or web sites within a metadata tag (or "meta-tag") field, so that the web page or web site can be retrieved with a search engine that is customized to search the keywords field. This may or may not involve using keywords restricted to a controlled vocabulary list. This method is commonly used by search engine indexing.
Views: 1648 The Audiopedia
XML ETL Suite - Db2Xml, Xml2Db & Integrator
 
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A complete demo of the three engines - Db2Xml, Xml2Db and Integrator. 1. Db2Xml engine: The Db2Xml engine can extract data from a number of relational databases, e.g. one SQL Server and one Oracle, and generates one or a number of XML files, no matter how different the source database and the target XML are in terms of data structure. 2. Xml2Db engine: The Xml2Db engine extracts data from one or a number of XML data files, and uploads it into the target database, no matter how different the source XML and target database are in term of data structure. 3. Integrator engine: The integrator engine extracts data from a number of source databases into a XML pipeline and directly uploads it onto the target database. The source and target database can be very different in structure. It moves a whole web of related tables, such as customer, order, order_line, product, employee, etc., in a single atomic transaction, and automatically reconnects the parent-child records at the target database.
Views: 278 Silan Liu
100% FREE WEB CRAWLER SOFTWARE
 
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In this video I demonstrate a 100% free software program called Web Crawler Simple. Find out more about this free web crawler software and/or download the software at http://affiliateswitchblade.com/blog/100-free-web-crawler-software-for-windows/ or http://affiliateswitchblade.com/blog/freewebcrawler The purpose of this software program used to crawl any website you wish extracting and listing every single page that makes of that website including pages with the no index and no follow directive. Although I a lot of people will download the software to use as a site map maker, as a side note, one of the benefits of this software is, because it reveals pages that have the noindex, no followed directives, quite often, these pages contain links to software programs, ebooks, and other digital content that the website owner normally sells because the noindex and nofollow directive is for the search engines telling the search engines to please not list these pages in search results - meaning the website owner wants to hide these pages from public view. Web Crawler Simple reveals these pages to you. How to use Web Crawler Simple Free Website Crawler The name, Web Crawler Simple, a very appropriate name for this software program because the software couldn't be easier to use. ❶ Enter the URL of the website you wish to crawl and extract all the pages from. ❷ Click the crawl button. When the software program has finished crawling the entire web site extracting all the web pages that make up that website you can... ❶ Save all the web pages in a text file. ❷ Save them as a urllist.txt. ❸ Save them as Sitemap.xml. http://www.affiliateswitchblade.com - Giant Array of Affiliate Marketing Software Tools including Link Cloaker, Content Spinner, Account Creator, Disposable Email and much more! free web crawler windows, free web crawler windows 7, free web crawler software for windows, free download win web crawler, free web crawler tools, web crawler tool free download, top free web crawler, free web crawler software, free web crawler software download, free web crawler software for windows, free web crawler script, free web crawler service,
Views: 15455 Affiliate Switchblade
easIE: Easy Information Extraction
 
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easIE: Easy Information Extraction is a framework for quickly and simply generating Web Information Extractors and Wrappers. easIE offers a set of wrappers for obtaining content from Static and Dynamic HTML pages by pointing to the html elements using css Selectors. An additional fuctionality is the definition of a configuration file. Users can define a configuration file in JSON format in order to extract content of a page by only defining this configuration file. easIE is also available on github: https://github.com/MKLab-ITI/easIE/
Views: 198 Vas Gat
Mansi Sheth (Veracode Inc): Building Security Analytics solution using Native XML Database
 
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Mansi Sheth (Veracode Inc) The trove of ever-expanding metadata we are collecting on a daily basis, poses us with the challenge of mining information out of this data-store, to help drive our business analytics solutions. Most non-destructive format of these metadata is in XML formats, so it became crucial to use a technology, which provides sophisticated support for XML specific query technologies. This paper will discuss how Veracode is using Native XML Databases(NxD) tool BaseX, to solve various use cases across multiple departments. It will discuss in depth, how its incorporated, its architecture and eco-system. It will also touch base on lessons learned along the way, including approaches which were tried and didn’t work so well. http://www.xmlprague.cz/sessions2015/#secanalytics
Views: 217 XMLPrague
A RESTful JSON-LD Architecture for Unraveling Hidden References to Research Data
 
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Talk by Konstantin Baierer and Philipp Zumstein, Mannheim University Library, Germany. Title: A RESTful JSON-LD Architecture for Unraveling Hidden References to Research Data Abstract: Data citations are more common today, but more often than not the references to research data don't follow any formalism as do references to publications. The InFoLiS project makes those "hidden" references explicit using text mining techniques. They are made available for integration by software agents (e.g. for retrieval systems). In the second phase of the project we aim to build a flexible and long-term sustainable infrastructure to house the algorithms as well as APIs for embedding them into existing systems. The infrastructure's primary directive is to provide lightweight read/write access to the resources that define the InFoLiS data model (algorithms, metadata, patterns, publications, etc.). The InFoLiS data model is implemented as a JSON schema and provides full forward compatibility with RDF through JSON-LD using a JSON-to-RDF schema-ontology mapping, reusing established vocabularies whenever possible. We are neither using a triplestore nor an RDBMS, but a document database (MongoDB). This allows us to adhere to the Linked Data principles, while minimizing the complexity of mappings between different resource representations. Consequently, our web services are lightweight, making it easy to integrate InFoLiS data into information retrieval systems, publication management systems or reference management software. On the other hand, Linked Data agents expecting RDF can consume the API responses as triples; they can query the SPARQL endpoint or download a full RDF dump of the database. We will demonstrate a lightweight tool that uses the InFoLiS web services to augment the web browsing experience for data scientists and librarians. SWIB15 Conference, 23 – 25 November 2015, Hamburg, Germany. http://swib.org/swib15 #swib15
Views: 634 SWIB
Ch 8 - Storing XML
 
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Storing XML
Views: 78 Michael Devlin
bpmNEXT 2013: Extreme BPMN: Semantic Web Leveraging BPMN XML Serialization
 
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Lloyd Dugan, BPM, Inc. and Mohamed Keshk, Semantic BPMN This session demonstrates some of the most extreme work performed to date with BPMN -- extending it beyond the process view into semantic meaning and systems architecture. Completed inside the U.S. defense enterprise, BPMN is used for enterprise-level services modeling and within an ontology-based semantic search engine to automate search of process models. The resulting engine leverages the power of the Semantic Web to discover patterns and anomalies across now seamlessly linked repositories. This approach for the first time fully exploits the richness of the BPMN notation, uniquely enabling modeling of executable services as well as context-based retrieval of BPMN artifacts. Lloyd Dugan is the Chief Architect for Business Management, Inc., providing BPMN modeling, system design, and architectural advisory services for the Deputy Chief Management Office (DCMO) of the U.S. Department of Defense (DoD). He is also an Independent Consultant that designs executable BPMN processes that leverage Service Component Architecture (SCA) patterns (aka BPMN4SCA), principally on the Oracle BPMN/SOA platform. He has nearly 27 years of experience in providing IT consulting services to both private and public sector clients. He has an MBA from Duke University's Fuqua School of Business. Mohamed Keshk has been working with Semantic Technology since 2001, and Model Driven Architecture (MDA) since 2005. His most recent work focuses on bridging the gap between semantic technology and metamodel-based standards such as UML2 and BPMN 2.0, including the first ontology-based query engine for BPMN 2.0, based on XMI metamodel. As Sr. Semantic Architect, Mohamed is testing the engine in a production environment to let users instantly retrieve information in a model repository.
Using Personalization to Improve XML Retrieval
 
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As the amount of information increases every day and the users normally formulate short and ambiguous queries, personalized search techniques are becoming almost a must. Using the information about the user stored in a user profile, these techniques retrieve results that are closer to the user preferences. On the other hand, the information is being stored more and more in an semi-structured way, and XML has emerged as a standard for representing and exchanging this type of data. XML search allows a higher retrieval effectiveness, due to its ability to retrieve and to show the user specific parts of the documents instead of the full document. In this paper we propose several personalization techniques in the context of XML retrieval. We try to combine the different approaches where personalization may be applied: query reformulation, re-ranking of results and retrieval model modification. The experimental results obtained from a user study using a parliamentary document collection support the validity of our approach.
LPC Webinar Series / XML Document Parsing and Publishing
 
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Alex Garnett, Simon Fraser University (presenter) Presented February 17, 2016 This talk is intended for editors, editorial assistants, journal managers, XML aficionados, developers, and anyone else who has an interest in document format conversion and parsing. We'll be examining PKP's current XML parsing kit, discussing the merits of automated vs. manual markup, and discussing how to accommodate an XML-based workflow with currently available tools. If you're interested in producing National Library of Medicine JATS XML content from authors' Word document submissions with a minimum of effort, and getting matching HTML/PDF/ePub output, you should be interested in this webinar! The presenter, Alex Garnett, is Data Curation and Digital Preservation Librarian at Simon Fraser University in British Columbia, Canada. At SFU Library, he works on initiatives relating to the new Research Data Repository; at the Public Knowledge Project, he works on new tools for automatic typesetting and rendering of scholarly articles, and at SFU Archives, he works on implementing digital preservation tools such as Archivematica and BitCurator. His father was a regular expression.
Relevancy Ranking
 
03:50
Relevancy Ranking and the library.
Matthias Nicola on XML in the Data Warehouse
 
05:32
Matthias Nicola speaks about XML in the Data Warehouse at the IDUG North America 2009 Conference in Denver, Colorado.
Views: 266 Conor O'Mahony
D2I - Efficient Association Discovery with Keyword-based Constraints on Large Graph Data
 
01:06:40
Abstract: In many domains, such as social networks, cheminformatics, bioinformatics, and health informatics, data can be represented naturally in graph model, with nodes being data entries and edges the relationships between them. The graph nature of these data brings opportunities and challenges to data storage and retrieval. In particular, it opens the doors to search problems such as semantic association discovery and semantic search. Our group studied the application requirements in these domains and find that discovering Constrained Acyclic Paths (CAP) is highly in demand, based on such studies, we define the CAP search problem and introduce a set of quantitative metrics for describing keyword-based constraints. In addition, we propose a series of algorithms to efficiently evaluate CAP queries on large scale graph data. In this talk, I will focus on two main aspects of our study: (1) what's CAP query and how to express CAP queries in a structured graph query language; and (2) how to efficiently evaluate CAP queries on large graph data. Bio: Professor Wu completed her Ph.D. in Computer Science from the University of Michigan, Ann Arbor. She earned her M.S. degree from IU Bloomington in December 1999 and an M.S./B.S. degree from Peking University, China. Dr. Wu completed research internships at IBM Almaden Research Center as well as Microsoft Research in 2002 and 2003. Prof. Wu joined IU in 2004, and is currently an Associate Professor of Computer Science, of the School of Informatics and Computing. She is one of the founders of the TIMBER, a high performance native XML database system capable of operating at large scale, through use of a carefully designed tree algebra and judicious use of novel access methods and optimizations techniques. Her research in the Timber project focused on XML data storage, query processing and optimization, especially cost-based query optimization. Prof. Wu's recent research at Indiana University involves algebra for XML queries, normalization, indexing and the security of XML data repositories, the storage and query of data on the Semantic Web and association discovery. Her past research projects include Access Control for XML (ACCESS), which focused on developing a framework for flexible access constraint specification, representation and efficient enforcement. Prof. Wu is also involved in research related to data integration, data mining, and knowledge discovery.
Views: 112 IU_PTI
What is a Web Crawler ? ( in hindi )
 
01:55
Dosto is video me mene,What is a Web Crawler?,What does a web crawler do?,what is web indexing,What is spider?. ke bare me bataya hai, mere hisab se aap ko pasand ayega. #Technandan Subscribe Tech Nandan What is IMSI Catcher ? (hindi/Urdu) https://www.youtube.com/watch?v=BNB7L5t3CKw How does a Weather Radar Work ? https://www.youtube.com/watch?v=DlS_qBjWyak&index=29&list=PL-i8OqyzLeQj45yf4B5KiY1DbqEvzltTr&t=0s Blog - https://technandanm.blogspot.com/ Website - https://www.technandan.ml/ Facebook Page - https://www.facebook.com/technandan #Crawler #spider #Web #Search #index #website #google
Views: 132 Tech Nandan