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Mining the Social Web


Mining the Social Web

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Maryam Jafari,Farzad Soleymani and Shahram Jamali Mining User Navigation Patterns from Web Access Log Files


Since the Web is the biggest and most widely known information source that is easily accessible and searchable, on the positive side, there is widespread participation in authoring content. Web consists of billions of interconnected documents (called Web pages) which are authored by millions of people. Web Mining aims to discover the informative knowledge or information from massive data sources available on the Web by using data mining or machine learning approaches. Web usage mining is one of the categories of Web mining which discovers user behavioral patterns from searches and accesses logs of user interactions with Websites. This book presents methods, approaches and techniques to perform three main tasks of Web usage mining that are called Preprocessing, Pattern Discovery, and Pattern Analysis. Another main part discussed in this book is Web server log file Analysis. Mainly this book is suitable for researchers who are interested in the techniques and applications of Web search, Web data management, Web mining and Web recommendation as well as Web usage mining for in-depth academic research and industrial development to gain rapid knowledge in related areas.

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Maryam Ramezani Using Data Mining for Facilitating User Contributions in the Social Semantic Web


Doctoral Thesis / Dissertation from the year 2011 in the subject Computer Science - Internet, New Technologies, grade: 1,0, Karlsruhe Institute of Technology (KIT), language: English, abstract: Social Web applications have emerged as powerful applications for Internet users allowing them to freely contribute to the Web content, organize and share information, and utilize the collective knowledge of others for discovering new topics, resources and newfriends. While social Web applications such as social tagging systems have many benefits, they also present several challenges due to their open and adaptive nature. The amount of user generated data can be extremely large and since there is not any controlled vocabulary or hierarchy, it can be very difficult for users to find the information that is of their interest.In addition, attackers may attempt to distort the system's adaptive behavior by inserting erroneous or misleading annotations, thus altering the way in which information is presented to legitimate users. This thesis utilizes data mining and machine learning techniques to address these problems. In particular, we design and develop recommender systems to aid the user in contributing to the Social Semantic Web. In addition, we study intelligent techniques tocombat attacks against social tagging systems. In our work, we first propose a framework that maps domain properties to recommendation technologies. This framework provides a systematic approach to find the appr...

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K. C. Srikantaiah and R. Venugopal Web Mining Algorithms


World Wide Web (WWW) is a collection of interconnected Web pages accessed via Internet offering information and services from all over the world. There is an explosive growth of information in the Web that poses a challenge to Web users to extract essential knowledge from it. Web Mining is a process of extracting knowledge from WWW using Data mining Techniques. The Web contains Semistructured data like Web pages and Web logs. Mining of Semistructured data involves, (i) extraction of required information from the Deep Web which is not accessible to the search engines, (ii) careful exploitation of extracted data for enhancing the services offered by Web tools such as finding synonyms for search keywords and (iii) ranking the Web pages obtained from the results. To improve the overall performance of the Web, Web logs are mined to derive patterns to predict user behaviour. These patterns can be analysed and used for many applications such as generation of prefetching rules, construction of community Web directories for personalization. These techniques improve availability of web objects, reduce latency and utilize bandwidth efficiently in accessing the Internet.

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Big Data Analytics in the Social and Ubiquitous Context. 5th International Workshop on Modeling Media, MSM 2014, Mining Environments, MUSE First Worksho...


Книга "Big Data Analytics in the Social and Ubiquitous Context. 5th International Workshop on Modeling Social Media, MSM 2014, 5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014, and First International Workshop on Ma".

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Zakir Laliwala, Abdulbasit Fazalmehmod Shaikh Web Crawling and Data Mining with Apache Nutch


Книга "Web Crawling and Data Mining with Apache Nutch".

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Antonia Feserer Using Social Semantic Web Data for Privacy Policies


Bachelor Thesis from the year 2009 in the subject Computer Science - Internet, New Technologies, grade: 1,7, University of Hannover (Knowledge Based Systems Institute), language: English, abstract: Social Web applications are steadily gaining popularity. At the same time, the open nature of such services leads to the exposure of an immense amount of personal data.Due to insucient access control on nowadays Social Web applications problems in terms of privacy arise. This thesis focuses on the need for more exible and negrained privacy restrictions. It analyses privacy problems of current Social Web applications and compares the privacy preferences such applications over. Based on this analysis, this thesis extends the well-known principle of policy-based accesscontrol, which is a exible and dynamic way to dene who can get access to what content based on user preferences. The presented extension accommodates policies to the requirements of the Social Web. In particular, it describes how to exploit Social Semantic Web data for privacy reasoning. This includes the retrieval of Socialand Semantic Web data from various information sources on the Web. It further includes its usage for the denition of privacy policies and its consideration during the policy evaluation. Consequently, using Social Semantic Web data for policy reasoning allows users to exactly dene which social relationships and properties a requester has to have in order to access a particular resource. These condition...

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H. Oinas-Kukkonen Humanizing the Web. Change and Social Innovation


Книга "Humanizing the Web. Change and Social Innovation".

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Kanhaiya Lal Semantic Web Based Data Cloud


Data mining is a treatment process to extract useful and interesting knowledge from large amount of data. The knowledge modes data mining discovered have a variety of different types. The common patterns are: association mode, classification model, class model, sequence pattern and so on. Mining association rules is one of the most important aspects in data mining. Association rules are dependency rules which predict occurrence of an item based on occurrences of other items. The process of building the Semantic Web is currently an area of high activity. Its structure has to be defined, and this structure then has to be filled with life. Cloud computing is a highly touted recent phenomenon. The cloud may move data or computation to improve responsiveness. Some clouds monitor their offerings for malicious activity Visualization. Hardware resources in clouds are usually Virtual; they are shared by multiple users to improve efficiency. This book deals technique of association rules mining in semantic web based data cloud. KEY FEATURES: Explains the basic knowledge of cloud computing, Data & web mining. Provides Concept of Association rules & Algorithm for mining Association Rules.

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Gregor Wiedemann Text Mining for Qualitative Data Analysis in the Social Sciences. A Study on Democratic Discourse Germany


Книга "Text Mining for Qualitative Data Analysis in the Social Sciences. A Study on Democratic Discourse in Germany".

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El Amir Eman Web Services Approach for GeoSpatial Data Mining


Geospatial Data Mining describes the combination of two key market intelligence software tools: Geographical Information Systems and Data Mining Systems. GIS and Data Mining are naturally synergistic technologies that can be synthesized to produce powerful market insight from a sea of disparate data. This book describes a research that developed a Spatial Data Mining Web Service. It integrates state of the art Geographic Information Systems and Data Mining Systems functionality in an open, highly extensible, internet-enabled plug-in architecture. within the book you will learn that one analysis can often lead into others, parameters for tools may change, criteria for analyses can evolve, or you may want to perform additional visual analysis on the results to make them more meaningful or easier to interpret.

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Nishant Baxi Social Bookmarking Success


The social bookmarking tool has a fairly critical impact in the web 2.0 platform. Being a piece of a web based bookmarking administration, For Example, burrow or reddit Enables the person to bookmark while surfing the web. The particular bookmarking booklet would have been the book of the moment.

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Richard Heimann, Nathan Danneman Social Media Mining with R


Книга "Social Media Mining with R".

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Marco Bonzanini Mastering Social Media Mining with Python


Книга "Mastering Social Media Mining with Python".

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Vikram Garg, Sharan Kumar Mastering Social Media Mining with R


Книга "Mastering Social Media Mining with R".

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Trends in Legal Knowledge. the Semantic Web and Regulation of Electronic Social Systems


Книга "Trends in Legal Knowledge. the Semantic Web and the Regulation of Electronic Social Systems".

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Isabell Stupak Online-Marketing. Wie kann Konsumentenverhalten uber verschiedene Kanale hinweg erfasst und gemessen werden.


Bachelorarbeit aus dem Jahr 2015 im Fachbereich BWL - Marketing, Unternehmenskommunikation, CRM, Marktforschung, Social Media, Note: 1,7, Universität Bremen, Sprache: Deutsch, Abstract: Unternehmen sind bestrebt, das Verhalten ihrer Konsumenten zu erfassen und zu analysieren, um dieses Wissen gewinnbringend für sich zu nutzen. Im Internet lässt sich eine erhebliche Masse verfügbarer Daten sammeln, die aussagekräftige Informationen über das Verhalten beinhalten können. Beschrieben durch die Big Data Problematik, ergeben sich jedoch Schwierigkeiten, mit dieser Fülle an Benutzerdaten umzugehen. In dieser Arbeit wurde das Data Mining als Verfahren angewandt, um Daten zu strukturieren und so informative Muster und Zusammenhänge zu finden, die Aussagen über die Nutzergruppen zulassen. Die Arbeit gibt einen Überblick darüber, welche Kontaktpunkte im Hinblick auf die Aspekte des Konsumentenverhaltens besonders informativ sind und welche spezifischen Erkenntnisse über die Nutzer daraus abgeleitet werden können. Dabei wurden das Web Content Mining sowie das Web Usage Mining auf Online Shops, Social Media und Suchmaschinen angewandt. Anhand dessen konnten Rückschlüsse auf das Informationssuchverhalten, die Intention, die Adoption sowie die Evaluation der Nutzer gezogen werden. Die Anwendung des Web Content Mining bestätigte die Eignung des Social Media, das Word-of-Mouth-Verhalten der Konsumenten hinsichtlich des Unternehmens zu analysieren. Dahingegen erwies sich der Online Shop, durch...

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