Search results “Xml retrieval in web mining definition”
Find the notes of INFORMATION RETRIEVAL on this link - https://viden.io/knowledge/information-retrieval?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=ajaze-khan-1
Views: 9170 LearnEveryone
What is an Ontology
Description of an ontology and its benefits. Please contact [email protected] for more information.
Views: 147188 SpryKnowledge
XML Databases
Subject:Computer Science Paper: Database management system
Views: 208 Vidya-mitra
Intro to Web Scraping with Python and Beautiful Soup
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/ -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f6wzS0 See what our past attendees are saying here: https://hubs.ly/H0f6wzY0 -- 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
Views: 493600 Data Science Dojo
inex evaluating content oriented xml retrieval
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: 50 slideshowing
Application of xml in  web
Application of xml in web Source Code of XML - FO: https://www.dropbox.com/s/zg04csg1eq24kcw/DemoDom.rar
Views: 679 Phạm Anh Đới
Basic concepts of XML
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/
XML Database
Subject:Computer Science Paper: Database management system
Views: 680 Vidya-mitra
Using Personalization to Improve XML Retrieval
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.
Information Retrieval
Links to resources: http://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf https://en.wikipedia.org/wiki/Vector_space_model
Views: 4398 Andrei Barbu
Retrieve text from a html document with XML package of R
Brief demonstration of XML package of R. Easy way to extract text by defining tags of html.
Views: 6223 Yuki
★What is Web Personalization?★
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: 396 Web In Taiwan
Web crawlers and web information retrieval In Hindi | GTU | WEB DATA MANAGEMENT
Know about web crawlers and web information retrieval In Hindi #GTU #WEBDATAMANAGEMENT #Webcrawlers&webinformationretrieval
information retrieval
Subscribe today and give the gift of knowledge to yourself or a friend information retrieval Information Retrieval. Content. Introduction to IR Problem definition Characteristics of text data IR Models Evaluation Implementation Text Classification Web IR Crawling Link analysis. Information Retrieval (IR). The indexing and retrieval of textual documents. Slideshow 3034234 by wan show1 : Information retrieval show2 : Content show3 : Information retrieval ir show4 : Typical ir task show5 : Ir system show6 : Ir system architecture show7 : Ir system components show8 : Ir system components continued show9 : Web search show10 : Web search system show11 : Other ir related tasks show12 : History of ir show13 : Ir history continued show14 : Ir history continued1 show15 : Ir history continued2 show16 : Recent ir history show17 : Recent ir history1 show18 : Related areas show19 : Boolean and vector space retrieval models show20 : Retrieval models show21 : Classes of retrieval models show22 : Boolean model show23 : Boolean retrieval model show24 : Boolean models problems show25 : Statistical models show26 : Statistical retrieval show27 : Issues for vector space model show28 : The vector space model show29 : Graphic representation show30 : Document collection show31 : Term weights term frequency
Views: 711 slideshowing
Mining Knowledge from Databases: An Information Network Analysis Approach
Most people consider a database is merely a data repository that supports data storage and retrieval. Actually, a database contains rich, inter-related, multi-typed data and information, forming one or a set of gigantic, interconnected, heterogeneous information networks. Much knowledge can be derived from such information networks if we systematically develop an effective and scalable database-oriented information network analysis technology. In this talk, we introduce database-oriented information network analysis methods and demonstrate how information networks can be used to improve data quality and consistency, facilitate data integration, and generate interesting knowledge. Moreover, we present interesting case studies on real datasets, including DBLP and Flickr, and show how interesting and organized knowledge can be generated from database-oriented information networks
Views: 75 Microsoft Research
What is INFORMATION FILTERING SYSTEM? What does INFORMATION FILTERING SYSTEM mean? INFORMATION FILTERING SYSTEM meaning - INFORMATION FILTERING SYSTEM definition - INFORMATION FILTERING SYSTEM 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 An information filtering system is a system that removes redundant or unwanted information from an information stream using (semi)automated or computerized methods prior to presentation to a human user. Its main goal is the management of the information overload and increment of the semantic signal-to-noise ratio. To do this the user's profile is compared to some reference characteristics. These characteristics may originate from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach). Whereas in information transmission signal processing filters are used against syntax-disrupting noise on the bit-level, the methods employed in information filtering act on the semantic level. The range of machine methods employed builds on the same principles as those for information extraction. A notable application can be found in the field of email spam filters. Thus, it is not only the information explosion that necessitates some form of filters, but also inadvertently or maliciously introduced pseudo-information. On the presentation level, information filtering takes the form of user-preferences-based newsfeeds, etc. Recommender systems and content discovery platforms are active information filtering systems that attempt to present to the user information items (film, television, music, books, news, web pages) the user is interested in. These systems add information items to the information flowing towards the user, as opposed to removing information items from the information flow towards the user. Recommender systems typically use collaborative filtering approaches or a combination of the collaborative filtering and content-based filtering approaches, although content-based recommender systems do exist.
Views: 526 The Audiopedia
Information Retrieval & Extraction
Slides 2-6
Views: 24 Sgabriel136
What is web personalization?
Learn more about web personalization and what it can do for you. https://www.persosa.com/whitepapers/what-is-personalization
Views: 564 Persosa
Lecture -40 XML Databases
Lecture Series on Database Management System by Dr.S.Srinath IIIT Bangalore . For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 36764 nptelhrd
text mining, web mining and sentiment analysis
text mining, web mining
Views: 1591 Kakoli Bandyopadhyay
XML Data Structure
XML Data Structure
Views: 1403 merigrrl
Web Graph and Web graph mining and hot topics in web search in Hindi
Web Graph and Web graph mining and hot topics in web search in Hindi
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: 15058 Affiliate Switchblade
Introduction to Vector Space Model
Vector Space Model - Scoring, term weighting & the vector space model
Views: 8812 ashishsurekadelhi
What is WEB INDEXING? What does WEB INDEXING mean? WEB INDEXING meaning & explanation
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: 1551 The Audiopedia
Introduction video of Multimedia Retrieval Project
Part of the delivery
Views: 41 Shaowei Zhang
Matthias Nicola on XML in the Data Warehouse
Matthias Nicola speaks about XML in the Data Warehouse at the IDUG North America 2009 Conference in Denver, Colorado.
Views: 266 Conor O'Mahony
A RESTful JSON-LD Architecture for Unraveling Hidden References to Research Data
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: 617 SWIB
Web Spiders Kya Hote Hai ? / What is Web Crawler Explained In Hindi
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: 35129 Technical Sagar
D2I - Efficient Association Discovery with Keyword-based Constraints on Large Graph Data
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
Extended XML Tree Pattern Matching Theories and Algorithms
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: 129 Renown Technologies
A Survey of XML Tree Patterns
2013 IEEE- A Survey of XML Tree Patterns Ecway Technologies...Cell: +91 98949 17187.
Views: 75 Ecway Karur
Personal Search Engines for Multimedia Information Retrieval
A Survey on Content Based Video Analysis: IN4314 Seminar Selected Topics in Multimedia Computing (2010-2011 Q3) at Delft University of Technology. Survey talk on the topic of personal multimedia search by Ankur Sharma.
Views: 818 M. Larson
023 What is XPath
XML, or Extensible Markup Language, was designed to make information sharing and data interpretation easier. In this course, developer and author Joe Marini takes you through the basic rules of XML, explains its syntax, and covers more advanced topics such as styling your XML with CSS and XSLT and manipulating XML through the DOM. From integrating XML into your site to creating document type declarations and schema definitions, this course covers everything you need to not only get started with XML but also master it.
Views: 363 top video
A Survey of XML Tree Patterns.
Android projects are Available at: Softmerge Solutions Pvt Ltd. Hyderabad Contact:N.BHARGAV 9493049639,04065745230
Views: 102 krishna sms
Parsing Text Files in Python
A short program to read lines from a text file and extract information, patterns, from each line.
Views: 102910 Dominique Thiebaut
Excel Magic Trick 1336: Power Query: Import Big Data Text Files: Connection Only or Data Model?
Download File: http://people.highline.edu/mgirvin/excelisfun.htm See how to use Import 10 Text Files and Append (combine) then into a single Proper Data Set before making a PivotTable Report. Compare and Contrast whether we should use Connection Only or Data Model to store the data. 1. (00:18) Introduction & Look at Text Files that Contain 7 Million Transactional Records 2. (01:43) Power Query (Get & Transform) Import From Folder to append (combine) 10 Text Files that contain 7 Millions transactional records. 3. (05:07) Load Data as Connection Only and Make PivotTable 4. (08:17) Load Data into Data Model and Make PivotTable. 5. (10:46) Summary
Views: 30292 ExcelIsFun
What is DATA PUBLISHING? What does DATA PUBLISHING mean? DATA PUBLISHING meaning & explanation
What is DATA PUBLISHING? What does DATA PUBLISHING mean? DATA PUBLISHING meaning - DATA PUBLISHING definition - DATA PUBLISHING 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 Data publishing (also data publication) is the act of releasing data in published form for (re)use by others. It is a practice consisting in preparing certain data or data set(s) for public use thus to make them available to everyone to use as they wish. This practice is an integral part of the open science movement. There is a large and multidisciplinary consensus on the benefits resulting from this practice. The main goal is to elevate data to be first class research outputs. There are a number of initiatives underway as well as points of consensus and issues still in contention. However, publishers supported data publishing/publication either as an integral part of the paper or as supplemental material published jointly with the paper. These approaches are affected from a number of drawbacks from the data publication perspective including the difficulties in separating the data from the rest. Data publishing/publication is a practice on its own: A number of data journals have been developed to support data publication. A number of repositories have been developed to support data publication, e.g. figshare, Dryad, Dataverse. A survey on how generalist repositories are supporting data publishing is available Data papers are “scholarly publication of a searchable metadata document describing a particular on-line accessible dataset, or a group of datasets, published in accordance to the standard academic practices”. Their final aim being to provide “information on the what, where, why, how and who of the data”. The intent of a data paper is to offer descriptive information on the related dataset(s) focusing on data collection, distinguishing features, access and potential reuse rather than on data processing and analysis. Because data papers are considered academic publications no different than other types of papers they allow scientists sharing data to receive credit in currency recognizable within the academic system, thus "making data sharing count". This provides not only an additional incentive to share data, but also through the peer review process, increases the quality of metadata and thus reusability of the shared data. Thus data papers represent the scholarly communication approach to data sharing. Despite their potentiality, data papers are not the ultimate and complete solution for all the data sharing and reuse issues and, in some cases, they are considered to induce false expectations in the research community. Data papers are supported by a rich array of journals, some of which are "pure", i.e. they are dedicated to publish data papers only, while others – the majority – are "mixed", i.e. they publish a number of articles types including data papers. A comprehensive survey on data journals is available A non-exhaustive list of data journals has been compiled by staff at the University of Edinburgh. Examples of "pure" data journals are: Earth System Science Data, Scientific Data, Journal of Open Archaeology Data, and Open Health Data. Examples of "mixed" journals publishing data papers are: SpringerPlus, PLOS ONE, Biodiversity Data Journal, F1000Research, and GigaScience. Data citation is the provision of accurate, consistent and standardised referencing for datasets just as bibliographic citations are provided for other published sources like research articles or monographs. Typically the well established Digital Object Identifier (DOI) approach is used with DOIs taking users to a website that contains the metadata on the dataset and the dataset itself.
Views: 191 The Audiopedia
What is SEMANTIC MATCHING? What does SEMANTIC MATCHING mean? SEMANTIC MATCHING meaning - SEMANTIC MATCHING definition - SEMANTIC MATCHING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Semantic matching is a technique used in computer science to identify information which is semantically related. Given any two graph-like structures, e.g. classifications, taxonomies database or XML schemas and ontologies, matching is an operator which identifies those nodes in the two structures which semantically correspond to one another. For example, applied to file systems it can identify that a folder labeled “car” is semantically equivalent to another folder “automobile” because they are synonyms in English. This information can be taken from a linguistic resource like WordNet. In the recent years many of them have been offered. S-Match is an example of a semantic matching operator. It works on lightweight ontologies, namely graph structures where each node is labeled by a natural language sentence, for example in English. These sentences are translated into a formal logical formula (according to an artificial unambiguous language) codifying the meaning of the node taking into account its position in the graph. For example, in case the folder “car” is under another folder “red” we can say that the meaning of the folder “car” is “red car” in this case. This is translated into the logical formula “red AND car”. The output of S-Match is a set of semantic correspondences called mappings attached with one of the following semantic relations: disjointness (?), equivalence (?), more specific (?) and less specific (?). In our example the algorithm will return a mapping between ”car” and ”automobile” attached with an equivalence relation. Information semantically matched can also be used as a measure of relevance through a mapping of near-term relationships. Such use of S-Match technology is prevalent in the career space where it is used to gauge depth of skills through relational mapping of information found in applicant resumes. Semantic matching represents a fundamental technique in many applications in areas such as resource discovery, data integration, data migration, query translation, peer to peer networks, agent communication, schema and ontology merging. Its use is also being investigated in other areas such as event processing. In fact, it has been proposed as a valid solution to the semantic heterogeneity problem, namely managing the diversity in knowledge. Interoperability among people of different cultures and languages, having different viewpoints and using different terminology has always been a huge problem. Especially with the advent of the Web and the consequential information explosion, the problem seems to be emphasized. People face the concrete problem to retrieve, disambiguate and integrate information coming from a wide variety of sources.
Views: 448 The Audiopedia
IU X-Informatics Unit 21:Web Search and Text Mining 9: Vector Space Models I
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.
How to Define Requirements for Managing Your Unstructured Data
Organizations large and small are recognizing the untapped value in their unstructured data. Inefficiencies in the management and processing of this type of intellectual property lead to significant losses in productivity and new product revenue. This session will cover three key use cases which trigger improvement projects for the management of unstructured data: productivity, business intelligence and new product generation. We will review the triggers and desired outcomes in each case so that we may identify the tools and processes needed to deliver success. We will also review the capabilities afforded by technology so that participants will understand which requirements can be expected and which may still be 'blue sky.'
Introduction to XML | Business Analytics with R | XML Tutorial | XML Tutorial for Beginners |Edureka
( 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: 6462 edureka!
International Journal of Web & Semantic Technology (IJWesT)
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
2012 IEEE Project Combining Tag and Value
To Get any Project for CSE,IT ECE,EEE Contact Me @9966032699,8519950799 or mail us - [email protected]­m-Visit Our WebSite www.liotechprojects.com,www.iotech.in Web databases generate query result pages based on a user's query. Automatically extracting the data from these query result pages is very important for many applications, such as data integration, which need to cooperate with multiple web databases. We present a novel data extraction and alignment method called CTVS that combines both tag and value similarity. CTVS automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the QRRs are not contiguous, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested structure that may exist in the QRRs. We also design a new record alignment algorithm that aligns the attributes in a record, first pairwise and then holistically, by combining the tag and data value similarity information. Experimental results show that CTVS achieves high precision and outperforms existing state-of-the-art data extraction methods.
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Data Lineage in Malicious Environments | Java IEEE 2016-2017
Data Lineage in Malicious Environments | Java IEEE 2016-2017 To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications. In this work, we present a generic data lineage framework LIME for data flow across multiple entities that take two characteristic, principal roles (i.e., owner and consumer). We define the exact security guarantees required by such a data lineage mechanism toward identification of a guilty entity, and identify the simplifying non-repudiation and honesty assumptions. We then develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol and apply our framework to the important data leakage scenarios of data outsourcing and social networks. In general, we consider LIME , our lineage framework for data transfer, to be an key step towards achieving accountability by design.
UGM2017 / 19. Data Management and Analytics Solution - Tata Consultancy, Bayer Crop Science
Navya Dabbiru - Tata Consultancy, Bayer Crop Science, Digital Farming Background The Digital Farming department at Bayer Crop Science generates massive amounts of field analytics data. These large data sets consists of diverse content types like text, CSV, JSON, XML, Shape files and images spread across geographies – Germany, France, Brazil etc. Major drawback when dealing with these files is their lack of associated meta-data useful for information retrieval, exploratory analysis and generally analytical tasks. iRODS is a distributed big data management system, that serves as a powerful open source solution for storing and retrieval of structured and unstructured data and associate meta-data to it. The major challenge for any data analyst or scientist is to expose this data store to analytics platforms like R for finding insights, performing various statistical analysis and data downstream processing operations. However, exposing a data store to R environment is a multistep process and there is a huge compromise on preserving the security and integrity of the exposed data. Results Our approach addresses this issue by implementing a platform independent R-iRODS package that helps to access, modify, perform stateful navigation, CRUD operations on iRODS collections, data objects and meta-data, search collections and data objects based on meta-data from R language. The R based commands function similar to iRODS icommands integrated by IRODS REST services. The R-iRODS package has been engineered to have semantics equivalent to the icommands with business defined access rights preserving iRODS ACLs and can easily be used as a basis for further customization. iMetaExploreR is a Shiny based rich web interface that supports (a) iRODS file system interaction (b) File system- and meta-data-based search integrated with text mining word cloud (c) Recently modified and frequently accessed meta-data, and (d) spatial meta-data views. The prospects for more advanced future developments to be discussed are: a fully extensible support for data and visual analytics, powerful regex based search and meta-data associations. June 15, 2017 - Utrecht, Netherlands https://irods.org/ugm2017
Datawatch Panopticon | OLAP Connectivity
Datawatch has a connector for Microsoft SQL Server Analysis Services. You specify a host name, or IP-address, or URL. You can connect using an ADOMD connection, or an HTTP connection. With ADOMD, the current user’s domain credentials will be used for authentication. If the HTTP end point is exposed on the Analysis Services site, you can enter a valid username and password in the connector settings. When the connection is established, you select one of the available catalogs, and then one of the cubes available in that catalog. From a tree structure, you select particular measures and dimensions that you want to use in your dashboard. The data preview list will be empty, since data is retrieved only after applying columns to a dashboard. You drag and drop column onto a visualization part on the dashboard, and data is loaded. Similarly, there is also a general purpose MDX connector for OLAP sources supporting the XMLA interface. Just like with the data connectors for SQL sources, a query statement is automatically built based on the columns selected.