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Web Mining - Tutorial
 
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Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
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: 222 The Audiopedia
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 39566 Well Academy
Mining for structured data on the web
 
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Tools that turn HTML content into APIs.
Views: 327 Mark Headd
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1408 Kakoli Bandyopadhyay
R tutorial: What is text mining?
 
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Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 19510 DataCamp
What is DOCUMENT CLASSIFICATION? What does DOCUMENT CLASSIFICATION mean?
 
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What is DOCUMENT CLASSIFICATION? What does DOCUMENT CLASSIFICATION mean? DOCUMENT CLASSIFICATION meaning - DOCUMENT CLASSIFICATION definition - DOCUMENT CLASSIFICATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Document classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories. This may be done "manually" (or "intellectually") or algorithmically. The intellectual classification of documents has mostly been the province of library science, while the algorithmic classification of documents is mainly in information science and computer science. The problems are overlapping, however, and there is therefore interdisciplinary research on document classification. The documents to be classified may be texts, images, music, etc. Each kind of document possesses its special classification problems. When not otherwise specified, text classification is implied. Documents may be classified according to their subjects or according to other attributes (such as document type, author, printing year etc.). In the rest of this article only subject classification is considered. There are two main philosophies of subject classification of documents: the content-based approach and the request-based approach. Content-based classification is classification in which the weight given to particular subjects in a document determines the class to which the document is assigned. It is, for example, a common rule for classification in libraries, that at least 20% of the content of a book should be about the class to which the book is assigned. In automatic classification it could be the number of times given words appears in a document. Request-oriented classification (or -indexing) is classification in which the anticipated request from users is influencing how documents are being classified. The classifier asks himself: “Under which descriptors should this entity be found?” and “think of all the possible queries and decide for which ones the entity at hand is relevant” (Soergel, 1985, p. 230). Request-oriented classification may be classification that is targeted towards a particular audience or user group. For example, a library or a database for feminist studies may classify/index documents differently when compared to a historical library. It is probably better, however, to understand request-oriented classification as policy-based classification: The classification is done according to some ideals and reflects the purpose of the library or database doing the classification. In this way it is not necessarily a kind of classification or indexing based on user studies. Only if empirical data about use or users are applied should request-oriented classification be regarded as a user-based approach. Sometimes a distinction is made between assigning documents to classes ("classification") versus assigning subjects to documents ("subject indexing") but as Frederick Wilfrid Lancaster has argued, this distinction is not fruitful. "These terminological distinctions,” he writes, “are quite meaningless and only serve to cause confusion” (Lancaster, 2003, p. 21). The view that this distinction is purely superficial is also supported by the fact that a classification system may be transformed into a thesaurus and vice versa (cf., Aitchison, 1986, 2004; Broughton, 2008; Riesthuis & Bliedung, 1991). Therefore, is the act of labeling a document (say by assigning a term from a controlled vocabulary to a document) at the same time to assign that document to the class of documents indexed by that term (all documents indexed or classified as X belong to the same class of documents).
Views: 1796 The Audiopedia
Website Basic Structure and Navigation -  Web Design Basics - Episode 2
 
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Understanding Website Basic Structure and Website Navigation - Hierarchical Structure of Website including Web Navigation Elements and Website Menus.
Views: 7775 Weboq
Extract Structured Data from unstructured Text (Text Mining Using R)
 
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A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 9282 Stat Pharm
Chinese Name to Structure (CN2S) and Its Application in Chinese Text Mining
 
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Name to Structure (N2S) is a mature English name-to-structure conversion API development by ChemAxon. It is the underlying technology used in ChemAxon's chemical text mining tool D2S (Document to Structure). D2S can extract chemical information from individual file or a document repository system, such as Documentum and SharePoint. To accommodate the fast growing Chinese scientific literature, ChemAxon has recently developed CN2S (Chinese Name to Structure). In this presentation, we will demonstrate how CN2S can convert Chinese chemical names to structures, and its application in Chinese text mining.
Views: 261 ChemAxon
GraphFrames: Scaling Web Scale Graph Analytics with Apache Spark - Tim Hunter
 
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"Graph analytics has a wide range of applications, from information propagation and network flow optimization to fraud and anomaly detection. The rise of social networks and the Internet of Things has given us complex web-scale graphs with billions of vertices and edges. However, in order to extract the hidden gems of understanding and information within those graphs, you need tools to analyze the graphs easily and efficiently. At Spark Summit 2016, Databricks introduced GraphFrames, which implements graph queries and pattern matching on top of Spark SQL to simplify graph analytics. In this talk, we'll discuss the work that has made graph algorithms in GraphFrames faster and more scalable. For example, new implementations of connected components have received algorithm improvements based on recent research, as well as performance improvements from Spark DataFrames. Discover lessons learned from scaling the implementation from millions to billions of nodes; see its performance in the context of other popular graph libraries; and hear about real-world applications. Session hashtag: #EUds6"
Views: 639 Databricks
02 HTML 5 Web Structure in Urdu with example
 
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In this video, You will learn about web structure in Urdu and Hindi. This is a part of HTML-5 tutorial for beginners course. If you want to learn Complete HTML-5 Course so Watch whole playlist and learn from it. Download my HTML-5 android app: https://play.google.com/store/apps/details?id=html.tutorial.urdu Complete HTML Course Playlist: https://www.youtube.com/watch?v=fs3wwqwp9DQ&list=PLlBXMnsxdNpY8xN-Cl4fq60JcsygZO6Mb Complete CSS Course Playlist: https://www.youtube.com/watch?v=FAxHKtnRXEg&list=PLlBXMnsxdNpYEWYacZCIX9dXIBd2U94Cr Complete JavaScript Course Playlist: https://www.youtube.com/watch?v=NsDvhMLp4fU&list=PLlBXMnsxdNpaKQKlzc3qhbRYMuLmioUHf Complete JQuery Course Playlist: https://www.youtube.com/watch?v=7wyKqNsfAlA&list=LLjASGhx9SQ9pB8Qk3jqa5Rw Complete PHP and MySQL Course Playlist: https://www.youtube.com/watch?v=_4RlQYNf-hQ&list=PLlBXMnsxdNpZ1uQ5s6N65Qs0usrZ85Z5q html 5 basic to advance,complete course,abdul aleem baig,free technology tutor,html 5 web structure,web structure,html tutorial in urdu,html tutorial in hindi,html tutorial in urdu/hindi,HTML,Introduction,HTML Urdu,HTML Hindi,HTML Training,HTML Tutorials,HTML Free,Abdulaleembaig,web structure in urdu,web structure mining,web structure design,web structure mining in hindi,web development
Views: 327 Abdul Aleem Baig
Text Mining Improves Prediction of Protein Functional Sites
 
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The ISMB 2012 Highlights Track presentation of our PLoS ONE paper. This was presented in Long Beach, CA on July 16, 2012. Verspoor KM, Cohn JD, Ravikumar KE, Wall ME (2012) Text Mining Improves Prediction of Protein Functional Sites. PLoS ONE 7(2): e32171. doi:10.1371/journal.pone.0032171 http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0032171 LEAP-FS is available on-line at: http://leapfs.com/ Abstract: We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.
Mining Web graph Recommendations
 
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Data Alcott System (http://www.finalsemprojects.com) ieee projects,ns2 projects,ieee projects 2013,ieee project on networking,ieee project titles,ieee projects 2012,ieee project 2011,ns2 project,ieee projects demo CHECK DEMOS in http://www.finalsemprojects.in and http://www.finalsemprojects.com CONTACT US 09600095046 / 47 Contact : DATA ALCOTT SYSTEMS 27, BRINDAVAN STREET, WEST MAMBALAM CHENNAI-600 033 Ph: 9600095047 EMAIL:[email protected] http://www.finalsemprojects.com, http://www.finalsemprojects.in http://www.ns2projects.com IEEE project 2013 Titles in Cloud Computing http://www.finalsemprojects.in/IEEE%20Projects%202013-Cloud%20Computing.doc NS2 project IEEE Projects Titles http://www.finalsemprojects.in/NS2%20IEEE%20Projects%20Update.doc ieee projects 2012 titles http://www.finalsemprojects.in/IEEE%20Projects%202012.doc M.E Titles IEEE Project 2012 titles http://www.finalsemprojects.in/M.E%20Titles%20IEEE%20projects%202012%20titles.doc 2012 IEEE Project -Dotnet http://www.finalsemprojects.in/2012%20IEEE%20Projects-Dotnet.doc 2012 IEEE project-Java http://www.finalsemprojects.in/2012%20IEEE%20Projects-Java.doc IEEE project 2013 Titles http://www.finalsemprojects.in/IEEE%20projects%202013%20Data%20Alcott%20Systems.doc
Views: 391 finalsemprojects
Single and Multiple Document Summarization with Graph-based Ranking Algorithms
 
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Graph-based ranking algorithms have been traditionally and successfully used in citation analysis, social networks, and the analysis of the link-structure of the World Wide Web. In short, these algorithms provide a way of deciding on the importance of a vertex within a graph, by taking into account global information recursively computed from the entire graph, rather than relying only on local vertex-specific information. In this talk, I will present an innovative unsupervised method for extractive summarization using graph-based ranking algorithms. I will describe several ranking algorithms, and show how they can be successfully applied to the task of automatic sentence extraction. The method was evaluated in the context of both a single and multiple document summarization task, with results showing improvement over previously developed state-of-the-art systems. I will also outline a number of other NLP applications that can be addressed with graph-based ranking algorithms, including word sense disambiguation, domain classification, and keyphrase extraction.
Views: 1113 Microsoft Research
Introduction to Data Mining: Graph & Ordered Data
 
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Part three of data types, we introduce graph data and ordered data. And discuss the types of ordered data such as spatial-temporal and genomic data. -- At Data Science Dojo, we're extremely passionate about data science. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2noz3WU See what our past attendees are saying here: http://bit.ly/2ni6Pwv -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 4085 Data Science Dojo
Discovering Latent Semantics in Web Documents using Fuzzy Clustering
 
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Discovering Latent Semantics in Web Documents using Fuzzy Clustering TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com Web documents are heterogeneous and complex. There exists complicated associations within one web document and linking to the others. The high interactions between terms in documents demonstrate vague and ambiguous meanings. Efficient and effective clustering methods to discover latent and coherent meanings in context are necessary. This paper presents a fuzzy linguistic topological space along with a fuzzy clustering algorithm to discover the contextual meaning in the web documents. The proposed algorithm extracts features from the web documents using conditional random field methods and builds a fuzzy linguistic topological space based on the associations of features. The associations of co-occurring features organize a hierarchy of connected semantic complexes called ‘CONCEPTS,’ wherein a fuzzy linguistic measure is applied on each complex to evaluate (1) the relevance of a document belonging to a topic, and (2) the difference between the other topics. Web contents are able to be clustered into topics in the hierarchy depending on their fuzzy linguistic measures; web users can further explore the CONCEPTS of web contents accordingly. Besides the algorithm applicability in web text domains, it can be extended to other applications, such as data mining, bioinformatics, content-based or collaborative information filtering, and so forth.
Views: 283 jpinfotechprojects
Domain Driven Design: The Good Parts - Jimmy Bogard
 
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The greenfield project started out so promising. Instead of devolving into big ball of mud, the team decided to apply domain-driven design principles. Ubiquitous language, proper boundaries, encapsulation, it all made sense. But along the way, something went completely and utterly wrong. It started with arguments on the proper way of implementing aggregates and entities. Arguments began over project and folder structure. Someone read a blog post that repositories are evil, and ORMs the devil incarnate. Another read that relational databases are last century, we need to store everything as a stream of events. Then came the actor model and frameworks that sounded like someone clearing their throat. Instead of a nice, clean architecture, the team chased the next new approach without ever actually shipping anything. Beyond the endless technical arguments it causes, domain-driven design can actually produce great software. We have to look past the hype into the true value of DDD, what it can bring to our organizations and how it can enable us to build quality systems. With the advent of microservices, DDD is more important than ever - but only if we can get to the good parts.
Views: 69032 NDC Conferences
Mining Online Data Across Social Networks
 
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Capturing Data, Modeling Patterns, Predicting Behavior. Capturing Data, Modeling Patterns, Predicting Behavior - Based on collecting more than 20 million blog posts and news media articles per day, Professor Jure Leskovec discusses how to mine such data to capture and model temporal patterns in the news over a daily time-scale --in particular, the succession of story lines that evolve and compete for attention. He discusses models to quantify the influence of individual media sites on the popularity of news stories and algorithms for inferring hidden networks of information flow. Learn more: http://scpd.stanford.edu/
Views: 19633 stanfordonline
Facilitating Effective User Navigation through Website Structure Improvement
 
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Facilitating Effective User Navigation through Website Structure Improvement ieee data mining 2013 project Read more at: http://ieee-projects10.com/facilitating-effective-user-navigation-through-website-structure-improvement/
Views: 503 satya narayana
How NLP text mining works: find knowledge hidden in unstructured data
 
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Connect with us: http://www.linguamatics.com/contact What use is big data if you can't find what you're looking for? Follow: @Linguamatics https://twitter.com/Linguamatics https://www.linkedin.com/company/linguamatics https://www.facebook.com/Linguamatics https://plus.google.com/+Linguamatics https://www.youtube.com/user/Linguamatics/videos In knowledge driven industries such as the life sciences and healthcare, finding the right information quickly from huge volumes of text is crucial in supporting the best business decisions. However, around 80% of available information exists as unstructured text, and conventional keyword searches only retrieve documents, which still have to be read. This is very time consuming, unreliable, and, when important decisions rest on it, costly. Linguamatics’ text mining solution, I2E, uses Natural Language Processing to identify and extract relevant knowledge at least 10 times faster than conventional search, often uncovering insights that would otherwise remain unknown. I2E analyses the meaning of the text using powerful linguistic algorithms, enabling you to ask open questions, find the relevant facts and identify valuable connections. Going beyond simple keywords, I2E can recognise concepts and the different ways the same thing can be expressed, increasing the recall of relevant information. I2E then presents high quality results as structured, actionable knowledge, enabling fast review and analysis, and providing dramatically improved speed to insight. Our market leading software is supported by highly qualified domain experts who work with our customers to ensure successful project outcomes. Text mining for beginners: https://www.youtube.com/watch?v=40QIW9Sr6Io
Views: 13924 Linguamatics
Web data extractor & data mining- Handling Large Web site Item | Excel data Reseller & Dropship
 
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Web scraping web data extractor is a powerful data, link, url, email tool popular utility for internet marketing, mailing list management, site promotion and 2 discover extractor, the scraper that captures alternative from any website social media sites, or content area on if you are interested fully managed extraction service, then check out promptcloud's services. Use casesweb data extractor extracting and parsing github wanghaisheng awesome web a curated list webextractor360 open source codeplex archive. It uses regular expressions to find, extract and scrape internet data quickly easily. Whether seeking urls, phone numbers, 21 web data extractor is a scraping tool specifically designed for mass gathering of various types. Web scraping web data extractor extract email, url, meta tag, phone, fax from download. Web data extractor pro 3. It can be a url, meta tags with title, desc and 7. Extract url, meta tag (title, desc, keyword), body text, email, phone, fax from web site, search 27 data extractor can extract of different kind a given website. Web data extraction fminer. 1 (64 bit hidden web data extractor semantic scholar. It is very web data extractor pro a scraping tool specifically designed for mass gathering of various types. The software can harvest urls, extracting and parsing structured data with jquery selector, xpath or jsonpath from common web format like html, xml json a curated list of promising extractors resources webextractor360 is free open source extractor. It scours the internet finding and extracting all relative. Download the latest version of web data extractor free in english on how to use pro vimeo. It can harvest urls, web data extractor a powerful link utility. A powerful web data link extractor utility extract meta tag title desc keyword body text email phone fax from site search results or list of urls high page 1komal tanejashri ram college engineering, palwal gandhi1211 gmail mdu rohtak with extraction, you choose the content are looking for and program does rest. Web data extractor free download for windows 10, 7, 8. Custom crawling 27 2011 web data extractor promises to give users the power remove any important from a site. A deep dive into natural language processing (nlp) web data mining is divided three major groups content mining, structure and usage. Web mining wikipedia web is the application of data techniques to discover patterns from world wide. This survey paper reports the basic web mining aims to discover useful information or knowledge from hyperlink structure, page, and usage data. Web data mining, 2nd edition exploring hyperlinks, contents, and web mining not just on the software advice. Data mining in web applications. Web data mining exploring hyperlinks, contents, and usage in web applications what is mining? Definition from whatis searchcrm. Web data mining and applications in business intelligence web humboldt universitt zu berlin. Web mining aims to dis cover useful data and web are not the same thing. Extracting the rapid growth of web in past two decades has made it larg est publicly accessible data source world. Web mining wikipedia. The web is one of the biggest data sources to serve as input for mining applications. Web data mining exploring hyperlinks, contents, and usage web mining, book by bing liu uic computer sciencewhat is mining? Definition from techopedia. Most useful difference between data mining vs web. As the name proposes, this is information gathered by web mining aims to discover useful and knowledge from hyperlinks, page contents, usage data. Although web mining uses many is the process of using data techniques and algorithms to extract information directly from by extracting it documents 19 that are generated systems. Web data mining is based on ir, machine learning (ml), statistics web exploring hyperlinks, contents, and usage (data centric systems applications) [bing liu] amazon. Based on the primary kind of data used in mining process, web aims to discover useful information and knowledge from hyperlinks, page contents, usage. Data mining world wide web tutorialspoint.
Views: 216 CyberScrap youpul
Mozenda - Data Mining - Web Crawler - ForumExample
 
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http://www.twitter.com/jbmcclelland Justin McClelland (http://www.justinmcclelland.com), provides various how-to demonstrations and example applications of the Mozenda software (http://www.getmozenda.com ). The Mozenda, Software as a Service (SaaS), platform is ideal for performing comprehensive web data gathering (a.k.a web data extraction, screen scraping, web crawling, web harvesting, etc.)
Views: 4434 Justin McClelland
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
 
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Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. ------- Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 1344 Quantopian
DATA MINING   2 Text Retrieval and Search Engines   Lesson 5 5 Web Indexing
 
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https://www.coursera.org/learn/text-retrieval
Views: 89 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   1 1 2 Course Introduction Video
 
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https://www.coursera.org/learn/text-retrieval
Views: 544 Ryo Eng
Text mining for chemical information: the ChiKEL project - David Milward (Linguamatics)
 
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As scientific and patent literature expands, we need more efficient ways to find and extract information. Text mining is already being used successfully to analyse sets of documents after they are found by structure search, in a two‐step process. Integrating name‐to‐structure and structure search directly within an interactive text mining system enables structure search to be mixed with linguistic constraints for more precise filtering. This talk will describe work done in partnership between ChemAxon and Linguamatics in the EU funded project, ChiKEL, including improvements made to name‐to‐structure software, how we evaluated this, and the approach taken to integrating name to structure within the text mining platform, I2E.
Views: 232 ChemAxon
Mastering R Programming : Scraping Web Pages and Processing Texts | packtpub.com
 
09:27
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2jDsrGS]. In this video, we'll take a look at how to scrape data from web pages and how to clean and process raw web and other textual data. • Show a web scraping example with rvest • Explain the structure of a typical webpage and basics of HTML and extract selector paths • Process and clean text data For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 2972 Packt Video
TEXT CLASSIFICATION ALGORITHM IN DATA MINNING
 
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A lot of side-information is available along with the text documents in online forums. Information may be of different kinds, such as the links in the document, user-access behavior from web logs, or other non-textual attributes which are embedded into the text document. The relative importance of this side-information may be difficult to estimate, especially when some of the information is noisy., or can add noise to the process. It can be risky to incorporate side information into the clustering process, because it can either improve the quality of the representation for clustering
Views: 178 Dhivya Balu
TREASURE HOUSE RELICS Project 1
 
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A first look and visual introduction to the TREASURE HOUSE RELICS Project - an effort to document Utah's historic mining structures and other assorted relics and artifacts. All to be found on the web at MININGUTAH.com or MININGUTAH.org.
Views: 420 TreasureHouseRelics
More Data Mining with Weka (2.4: Document classification)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: Document classification http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7245 WekaMOOC
Document Classification and Unstructured Data Extraction SaaS Solution Offering for BPO and SI’s
 
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Experts estimate that up to 80% of the data in an organization is unstructured, information that does not have a well-defined or organized data model. The amount of this information in enterprises is staggering and growing substantially, often many times faster than structured information. Unstructured content is characteristically text-heavy, but may also contain critical data elements such as: amounts, percentage's, dates, numbers, and facts as well, like that found in contracts, loan amounts and terms, correspondences, proposals, legal descriptions, vesting information, EOB’s, transcriptions, and much more. Unfortunately, it is often very difficult to analyze, classify and extract this unstructured data as it is typically highly variable in nature. Due this complexity, BPO’s and SI’s have traditionally relied on data entry shops to manually enter this data to process and/or store this information into a more structured database-friendly format. With offshoring offering lower price solutions (as compared to onshore), many companies have settled as this to be the only practical solution. However, is this really the most affordable, scalable, secure and flexible option? Axis Technical Group has developed the next generation alternative with a hosted advanced data extraction and classification solution as a Service (SaaS). Axis AI uses its proprietary Natural Language Processing (NLP) and Machine Learning algorithms and processes that can now classify and capture data from all your content, complex, unstructured documents, as well as, the traditional structured and semi-structured documents. During the presentation, we’ll cover the following. •Challenges of capturing information from complex unstructured document formats •Overview of how NLP works and how these advanced technologies take capture to a new level •Various document candidates for advanced unstructured data extraction and classification •How you can save money as an SI/BPO developing solutions for your clients and offering Classification/Extraction as a Service •How Axis AI eliminates the upfront time and cost associated with a installing and configuring, training your technical teams and supporting a new solution •Product process overview and demonstration •Axis client case studies and success stories
Ontologies
 
01:03:03
Dr. Michel Dumontier from Stanford University presents a lecture on "Ontologies." Lecture Description Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery. View slides from this lecture: https://drive.google.com/open?id=0B4IAKVDZz_JUVjZuRVpMVDMwR0E About the Speaker Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
SmartCrawler: A Two stage Crawler for Efficiently Harvesting Deep Web Interfaces
 
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Title: SmartCrawler: A Two stage Crawler for Efficiently Harvesting Deep Web Interfaces Domain: Data Mining Key Features: 1. We propose a two-stage framework, namely Smart Crawler, for efficient harvesting deep web interfaces. In the first stage, Smart Crawler performs site-based searching for center pages with the help of search engines, avoiding visiting a large number of pages. To achieve more accurate results for a focused crawl, Smart Crawler ranks websites to prioritize highly relevant ones for a given topic. 2. In the second stage, Smart Crawler achieves fast in-site searching by excavating most relevant links with an adaptive link-ranking. To eliminate bias on visiting some highly relevant links in hidden web directories, we design a link tree data structure to achieve wider coverage for a website. We construct a SPCHS scheme from scratch in which the cipher texts have a hidden star-like structure. We prove our scheme to be semantically secure in the Random Oracle (RO) model. 3. It is challenging to locate the deep web databases, because they are not registered with any search engines, are usually sparsely distributed, and keep constantly changing. To address this problem, previous work has proposed two types of crawlers, generic crawlers and focused crawlers. Generic crawlers fetch all searchable forms and cannot focus on a specific topic. Focused crawlers such as Form-Focused Crawler (FFC) and Adaptive Crawler for Hidden-web Entries (ACHE) can automatically search online databases on a specific topic. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2016 - 2017 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students 62. 2017 mini projects on data mining 63. latest mini projects on data mining 64. data mining projects for engineering students 65. cse projects on data mining 66. data mining related ieee projects 67. ieee projects in content mining 68. data mining ieee major projects 69. 2017 ieee projects on data mining with abstract 70. 2017 data mining with abstract
David Deng (ChemAxon): Extracting Chemical Information within Documents - from Desktop to Enterprise
 
26:06
By providing reliable name to structure conversion, Naming has become the backbone of ChemAxon's chemical text mining tools, such as Document to Structure, JChem for SharePoint and chemicalize.org. In this presentation, a new addition to the text mining family, Document to Database will be introduced. Document to Database can continuously index chemical information from documents in a repository system (e.g. Documentum). Document to Database also provides a web interface, in which users can perform chemical search within the documents, or view the augmented documents with chemical information annotated. In addition to the Document to Database demonstration, the new improvements in Naming will also be covered, including Chinese chemical name recognition to accommodate the fast growing Chinese scientific literature; custom corporate ID to structure conversion via web service; and accuracy improvements.
Views: 319 ChemAxon
DocEng 2011: A Versatile Model for Web Page Representation
 
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The 11th ACM Symposium on Document Engineering Mountain View, California, USA September 19-22, 2011 A Versatile Model for Web Page Representation, Information Extraction and Content Re-Packaging Bernhard Krüpl-Sypien, Ruslan Fayzrakhmanov, Wolfgang Holzinger, Mathias Panzenböck, Robert Baumgartner Presented by Bernhard Krüpl-Sypien. ABSTRACT On todays Web, designers take huge efforts to create visu- ally rich websites that boast a magnitude of interactive ele- ments. Contrarily, most web information extraction (WIE) algorithms are still based on attributed tree methods which struggle to deal with this complexity. In this paper, we in- troduce a versatile model to represent web documents. The model is based on gestalt theory principlestrying to cap- ture the most important aspects in a formally exact way. It (i) represents and unifies access to visual layout, content and functional aspects; (ii) is implemented with semantic web techniques that can be leveraged for i.e. automatic reason- ing. Considering the visual appearance of a web page, we view it as a collection of gestalt figuresbased on gestalt primitiveseach representing a specific design pattern, be it navigation menus or news articles. Based on this model, we introduce our WIE methodology, a re-engineering pro- cess involving design patterns, statistical distributions and text content properties. The complete framework consists of the UOM model, which formalizes the mentioned com- ponents, and the MANM layer that hints on structure and serialization, providing document re-packaging foundations. Finally, we discuss how we have applied and evaluated our model in the area of web accessibility.
Views: 549 GoogleTechTalks
Symmetric Key and Public Key Encryption
 
06:45
Modern day encryption is performed in two different ways. Check out http://YouTube.com/ITFreeTraining or http://itfreetraining.com for more of our always free training videos. Using the same key or using a pair of keys called the public and private keys. This video looks at how these systems work and how they can be used together to perform encryption. Download the PDF handout http://itfreetraining.com/Handouts/Ce... Encryption Types Encryption is the process of scrambling data so it cannot be read without a decryption key. Encryption prevents data being read by a 3rd party if it is intercepted by a 3rd party. The two encryption methods that are used today are symmetric and public key encryption. Symmetric Key Symmetric key encryption uses the same key to encrypt data as decrypt data. This is generally quite fast when compared with public key encryption. In order to protect the data, the key needs to be secured. If a 3rd party was able to gain access to the key, they could decrypt any data that was encrypt with that data. For this reason, a secure channel is required to transfer the key if you need to transfer data between two points. For example, if you encrypted data on a CD and mail it to another party, the key must also be transferred to the second party so that they can decrypt the data. This is often done using e-mail or the telephone. In a lot of cases, sending the data using one method and the key using another method is enough to protect the data as an attacker would need to get both in order to decrypt the data. Public Key Encryption This method of encryption uses two keys. One key is used to encrypt data and the other key is used to decrypt data. The advantage of this is that the public key can be downloaded by anyone. Anyone with the public key can encrypt data that can only be decrypted using a private key. This means the public key does not need to be secured. The private key does need to be keep in a safe place. The advantage of using such a system is the private key is not required by the other party to perform encryption. Since the private key does not need to be transferred to the second party there is no risk of the private key being intercepted by a 3rd party. Public Key encryption is slower when compared with symmetric key so it is not always suitable for every application. The math used is complex but to put it simply it uses the modulus or remainder operator. For example, if you wanted to solve X mod 5 = 2, the possible solutions would be 2, 7, 12 and so on. The private key provides additional information which allows the problem to be solved easily. The math is more complex and uses much larger numbers than this but basically public and private key encryption rely on the modulus operator to work. Combing The Two There are two reasons you want to combine the two. The first is that often communication will be broken into two steps. Key exchange and data exchange. For key exchange, to protect the key used in data exchange it is often encrypted using public key encryption. Although slower than symmetric key encryption, this method ensures the key cannot accessed by a 3rd party while being transferred. Since the key has been transferred using a secure channel, a symmetric key can be used for data exchange. In some cases, data exchange may be done using public key encryption. If this is the case, often the data exchange will be done using a small key size to reduce the processing time. The second reason that both may be used is when a symmetric key is used and the key needs to be provided to multiple users. For example, if you are using encryption file system (EFS) this allows multiple users to access the same file, which includes recovery users. In order to make this possible, multiple copies of the same key are stored in the file and protected from being read by encrypting it with the public key of each user that requires access. References "Public-key cryptography" http://en.wikipedia.org/wiki/Public-k... "Encryption" http://en.wikipedia.org/wiki/Encryption
Views: 418464 itfreetraining
What is WEB CONTENT? What doe WEB CONTENT mean? WEB CONTENT meaning & explanation
 
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What is WEB CONTENT? What doe WEB CONTENT mean? WEB CONTENT meaning - WEB CONTENT definition - WEB CONTENT 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 content is the textual, visual, or aural content that is encountered as part of the user experience on websites. It may include—among other things—text, images, sounds, videos, and animations. In Information Architecture for the World Wide Web, Lou Rosenfeld and Peter Morville write, "We define content broadly as 'the stuff in your Web site.' This may include documents, data, applications, e-services, images, audio and video files, personal Web pages, archived e-mail messages, and more. And we include future stuff as well as present stuff." While the Internet began with a U.S. Government research project in the late 1950s, the web in its present form did not appear on the Internet until after Tim Berners-Lee and his colleagues at the European laboratory (CERN) proposed the concept of linking documents with hypertext. But it was not until Mosaic, the forerunner of the famous Netscape Navigator, appeared that the Internet become more than a file serving system. The use of hypertext, hyperlinks, and a page-based model of sharing information, introduced with Mosaic and later Netscape, helped to define web content, and the formation of websites. Today, we largely categorize websites as being a particular type of website according to the content a website contains. Web content is dominated by the "page" concept, its beginnings in an academic setting, and in a setting dominated by type-written pages, the idea of the web was to link directly from one academic paper to another academic paper. This was a completely revolutionary idea in the late 1980s and early 1990s when the best a link could be made was to cite a reference in the midst of a type written paper and name that reference either at the bottom of the page or on the last page of the academic paper. When it was possible for any person to write and own a Mosaic page, the concept of a "home page" blurred the idea of a page. It was possible for anyone to own a "Web page" or a "home page" which in many cases the website contained many physical pages in spite of being called "a page". People often cited their "home page" to provide credentials, links to anything that a person supported, or any other individual content a person wanted to publish. Even though we may embed various protocols within web pages, the "web page" composed of "HTML" (or some variation) content is still the dominant way whereby we share content. And while there are many web pages with localized proprietary structure (most usually, business websites), many millions of websites abound that are structured according to a common core idea. Blogs are a type of website that contain mainly web pages authored in HTML (although the blogger may be totally unaware that the web pages are composed using HTML due to the blogging tool that may be in use). Millions of people use blogs online; a blog is now the new "home page", that is, a place where a persona can reveal personal information, and/or build a concept as to who this persona is. Even though a blog may be written for other purposes, such as promoting a business, the core of a blog is the fact that it is written by a "person" and that person reveals information from her/his perspective. Blogs have become a very powerful weapon used by content marketers who desire to increase their site's traffic, as well as, rank in the search engine result pages (SERPs). In fact, new research from Technorati shows that blogs now outrank social networks for consumer influence (Technorati’s 2013 Digital Influence Report data).
Views: 152 The Audiopedia
What Is The Root Directory Of A Website?
 
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What Is The Root Directory Of A Website? FIND MORE ABOUT What Is The Root Directory Of A Website? Htm in 18 mar 2016 by default, your primary domain document root is public_html. Your root directory contains several 7 oct 2009 the web application is folder on your hard disk that corresponds to thisnamespace. Answered mar 31, 2014 author has 3. Apache2 where is apache web root directory on ubuntu? Ask ubuntu. Since cpanel allows for multiple domain names (addon domains and subdomains), you need to have a unique folder each. How to find the web application root msdn microsoft. Root directory for my website? What is website's root directory? . Googleusercontent search. Ltd, cui jianping jason. 04) or var html (ubuntu 14. Your primary domain is rooted in the public_html folder 5 aug 2015 root directory of a website not top level volume, but rather. Domain ) it would be public_html subdomain in this article, we will explain the folder, your website's document root, and add on subdomains doc root 2 jul 2017hi, i found a script that want to install but setup asks for directory of my site e. For example, placing a file called. These files contain important settings specific to your wordpress site re website root directoryqingdao oudu software co. The root directory of your website is the content that loads when visitors access domain name in a web browser. What directory should i upload my files to? How to find root of wordpress website &. Root directory for my website? . How to find the document root for your website in cpanel (x3 how do you directory of website? Webmaster what is a folder? Hostgator support portal. Dreamweaver refers to this folder as your local site root. How to add files the root directory of a website? Quora. Godaddy what is my websites root directory 9123 "imx0m" url? Q webcache. However, many modern web development frameworks the default document root for apache is var (before ubuntu 14. G home html how do i find out what it is? . Web & classic hosting where can i find the correct root directory for my website? . For addon domains (separate websites) it would be public_html domain and for subdomains (like subdomain. Inmotion how to find the document root of your website? What is directory a Youtube. Beginner's guide to wordpress file and directory structureodoo. Htaccess file 2 jan 2018 how to find root directory of wordpress website upload a such as xml sitemap, robots. Website directory structure plesk documentation. Since others may be interested in this topic, i'd james martin, web developer, etc. See the file 19 apr 2016 your wordpress root directory contains some special configuration files. Web & classic hosting in. The document root is the folder where you keep website files for a domain name. On an apache this directory is where your website will reside on our servers; Often referred to as the domain root or. Root directory definition the tech terms computer dictionaryix web hosting. You can change the directory index
Views: 11 E Questions
Lexis: An Optimization Framework for Discovering the Hierarchical Structure of Sequential Data
 
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Author: Payam Siyari, Georgia Institute of Technology Abstract: Data represented as strings abounds in biology, linguistics, document mining, web search and many other fields. Such data often have a hierarchical structure, either because they were artificially designed and composed in a hierarchical manner or because there is an underlying evolutionary process that creates repeatedly more complex strings from simpler substrings. We propose a framework, referred to as Lexis, that produces an optimized hierarchical representation of a given set of “target” strings. The resulting hierarchy, “Lexis-DAG”, shows how to construct each target through the concatenation of intermediate substrings, minimizing the total number of such concatenations or DAG edges. The Lexis optimization problem is related to the smallest grammar problem. After we prove its NP-hardness for two cost formulations, we propose an efficient greedy algorithm for the construction of Lexis-DAGs. We also consider the problem of identifying the set of intermediate nodes (substrings) that collectively form the “core” of a Lexis-DAG, which is important in the analysis of Lexis-DAGs. We show that the Lexis framework can be applied in diverse applications such as optimized synthesis of DNA fragments in genomic libraries, hierarchical structure discovery in protein sequences, dictionary-based text compression, and feature extraction from a set of documents. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 108 KDD2016 video
Data Mining  High impact Strategies   What You Need to Know  Definitions  Adoptions  Impact  Benefit
 
00:31
https://store.theartofservice.com/data-mining-high-impact-strategies-what-you-need-to-know-definitions-adoptions-impact-benefits-maturity-vendors.html In easy to read chapters, with extensive references and links to get you to know all there is to know about Data Mining right away, covering: Data mining, Able Danger, Accuracy paradox, Affinity analysis, Alpha algorithm, Anomaly detection, Apatar, Apriori algorithm, Association rule learning, Automatic distillation of structure, Ball tree, Biclustering, Big data, Biomedical text mining, Business analytics, CANape, Cluster analysis, Clustering high-dimensional data, Co-occurrence networks, Concept drift, Concept mining, Consensus clustering, Correlation clustering, Cross Industry Standard Process for Data Mining, Cyber spying, Data Applied, Data classification (business intelligence), Data dredging, Data fusion, Data mining agent, Data Mining and Knowledge Discovery, Data mining in agriculture, Data mining in meteorology, Data stream mining, Data visualisation, DataRush Technology, Decision tree learning, Deep Web Technologies, Document classification, Dynamic item set counting, Early stopping, Educational data mining, Elastic map, Environment for DeveLoping KDD-Applications Supported by Index-Structures, Evolutionary data mining, Extension neural network, Feature Selection Toolbox, FLAME clustering, Formal concept analysis, General Architecture for Text Engineering, Group method of data handling, GSP Algorithm, In-database processing, Inference attack, Information Harvesting, Institute of Analytics Professionals of Australia, K-optimal pattern discovery, Keel (software), KXEN Inc., Languageware, Lattice Miner, Lift (data mining), List of machine learning algorithms, Local outlier factor, Molecule mining, Nearest neighbour search, Neural network, Non-linear iterative partial least squares, Open source intelligence, Optimal matching, Overfitting, Principal component analysis, Profiling practices, RapidMiner, Reactive Business Intelligence, Receiver operating characteristic, Ren-rou, Sequence mining, Silhouette (clustering), Software mining, Structure mining, Talx, Text corpus, Text mining, Transaction (data mining), Weather data mining, Web mining, Weka (machine learning), Zementis Inc.
Views: 158 TheArtofService
Data Mining-Structured Data, Unstructured data and Information Retrieval
 
17:12
Structured Data, Unstructured data and Information Retrieval
Views: 1251 John Paul
A Search Engine Architecture Based on Collection Selection
 
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Google Tech Talks December, 19 2007 ABSTRACT We present a distributed architecture for a Web search engine, based on the concept of collection selection. We introduce a novel approach to partition the collection of documents, able to greatly improve the effectiveness of standard collection selection techniques (CORI), and a new selection function outperforming the state of the art. Our technique is based on the novel query-vector (QV) document model, built from the analysis of query logs, and on our strategy of co-clustering queries and documents at the same time. By suitably partitioning the documents in the collection, our system is able to select the subset of servers containing the most relevant documents for each query. Instead of broadcasting the query to every server in the computing platform, only the most relevant will be polled, this way reducing the average computing cost to solve a query. We introduce a novel strategy to use the instant load at each server to drive the query routing. Also, we describe a new approach to caching, able to incrementally improve the quality of the stored results. Our caching strategy is effectively both in reducing computing load and in improving result quality. The proposed architecture, overall, presents a trade-off between computing cost and result quality, and we show how to guarantee very precise results in face of a dramatic reduction to computing load. This means that, with the same computing infrastructure, our system can serve more users, more queries and more documents. Speaker: Diego Puppin
Views: 10522 GoogleTechTalks
Ugc net computer science December 2018 syllabus
 
10:06
How to prepare for ugc net computer science December 2018 aper II Syllabus 1. Discrete Structures Sets, Relations, Functions. Pigeonhole Principle, Inclusion-Exclusion Principle, Equivalence and Partial Orderings, Elementary Counting Techniques, Probability. Measure (s) for information and Mutual information. Computability: Models of computation-Finite Automata, Pushdown Automata, Non – determinism and NFA, DPDA and PDAs and Languages accepted by these structures. Grammars, Languages, Non – computability and Examples of non – computable problems. Graph : Definition, walks, paths, trails, connected graphs, regular and bipartite graphs, cycles and circuits. Tree and rooted tree. Spanning trees. Eccentricity of a vertex radius and diameter of a graph. Central Graphs. Centres of a tree. Hamiltonian and Eulerian graphs, Planar graphs. Groups : Finite fields and Error correcting / detecting codes. 2. Computer Arithmetic Propositional (Boolean) Logic, Predicate Logic, Well – formed – formulae (WFF), Satisfiability and Tautology. Logic Families: TTL, ECL and C – MOS gates. Boolean algebra and Minimization of Boolean functions. Flip-flops – types, race condition and comparison. Design of combinational and sequential circuits. Representation of Integers : Octal, Hex, Decimal, and Binary. 2′s complement and 1′s complement arithmetic. Floating point representation. 3. Programming in C and C++ Programming in C: Elements of C – Tokens, identifiers, data types in C. Control structures in C. Sequence, selection and iteration(s). Structured data types in C-arrays, struct, union, string, and pointers. O – O Programming Concepts: Class, object, instantiation. Inheritance, polymorphism and overloading. C++ Programming: Elements of C++ – Tokens, identifiers. Variables and constants, Datatypes, Operators, Control statements. Functions parameter passing. Class and objects. Constructors and destructors. Overloading, Inheritance, Templates, Exception handling. 4. Relational Database Design and SQL E-R diagrams and their transformation to relational design, normalization – INF, 2NF, 3NF, BCNF and 4NF. Limitations of 4NF and BCNF. SQL: Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL) commands. Database objects like-Views, indexes, sequences, synonyms, data dictionary. 5. Data and File structures Data, Information, Definition of data structure. Arrays, stacks, queues, linked lists, trees, graphs, priority queues and heaps. File Structures: Fields, records and files. Sequential, direct, index-sequential and relative files. Hashing, inverted lists and multi – lists. B trees and B+ trees. 6. Computer Networks Network fundamentals : Local Area Networks (LAN), Metropolitan Area Networks (MAN), Wide Area Networks (WAN), Wireless Networks, Inter Networks. Reference Models: The OSI model, TCP / IP model. Data Communication: Channel capacity. Transmission media-twisted pair, coaxial cables, fibre – optic cables, wireless transmission-radio, microwave, infrared and millimeter waves. Lightwave transmission. Thelephones – local loop, trunks, multiplexing, switching, narrowband ISDN, broadband ISDN, ATM, High speed LANS. Cellular Radio. Communication satellites-geosynchronous and low-orbit. Internetworking: Switch / Hub, Bridge, Router, Gateways, Concatenated virtual circuits, Tunnelling, Fragmentation, Firewalls. Routing: Virtual circuits and datagrams. Routing algorithms. Conjestion control. Network Security: Cryptography-public key, secret key. Domain Name System ( DNS ) – Electronic Mail and Worldwide Web ( WWW ). The DNS, Resource Records, Name servers. E-mail-architecture and Serves. 7. System Software and Compilers Assembly language fundamentals ( 8085 based assembly language programming ). Assemblers-2-pass and single-pass. Macros and macroprocessors. Loading, linking, relocation, program relocatability. Linkage editing. Text editors. Programming Environments. Debuggers and program generators. Compilation and Interpretation. Bootstrap compilers. Phases of compilation process. Lexical analysis. Lex package on Unix system. Context free grammars. Parsing and parse trees. Representation of parse ( derivation ) trees as rightmost and leftmost derivations. Bottom up parsers-shift-reduce, Main concepts in Geographical Information System (GIS), E – cash, E – Business, ERP packages. Data Warehousing: Data Warehouse environment, architecture of a data warehouse methodology, analysis, design, construction and administration. Data Mining: Windows Programming: Introduction to Windows programming – Win32, Microsoft Foundation Classes (MFC), Documents and views, Resources, Message handling in windows. Simple Applications (in windows) : Scrolling, splitting views, docking toolbars, status bars, common dialogs. Advanced Windows Programming:
Views: 3714 Nisha Mittal
KDD2016 paper 12
 
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Title: Lexis: An Optimization Framework for Discovering the Hierarchical Structure of Sequential Data Authors: Payam Siyari*, Georgia Institute of Technology Bistra Dilkina, Georgia Institute of Technology\ Constantine Dovrolis, Georgia Institute of Technology Abstract: Data represented as strings abounds in biology, linguistics, document mining, web search and many other fields. Such data often have a hierarchical structure, either because they were artificially designed and composed in a hierarchical manner or because there is an underlying evolutionary process that creates repeatedly more complex strings from simpler substrings. We propose a framework, referred to as “Lexis”, that produces an optimized hierarchical representation of a given set of “target” strings. The resulting hierarchy, “Lexis-DAG”, shows how to construct each target through the concatenation of intermediate substrings, minimizing the total number of such concatenations or DAG edges. The Lexis optimization problem is related to the smallest grammar problem. After we prove its NP-Hardness for two cost formulations, we propose an efficient greedy algorithm for the construction of Lexis-DAGs. We also consider the problem of identifying the set of intermediate nodes (substrings) that collectively form the “core” of a Lexis-DAG, which is important in the analysis of Lexis-DAGs. We show that the Lexis framework can be applied in diverse applications such as optimized synthesis of DNA fragments in genomic libraries, hierarchical structure discovery in protein sequences, dictionary-based text compression, and feature extraction from a set of documents. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
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