Blogs Search Engine Adopting RSS Syndication Using Fuzzy Logic

The rapid development of Internet increases the writers of blog sites. Sometimes these blog sites focused on solving some important problems. To find specific blogs are hard problem for the users because a lot of these blogs contain unuseful information such as online advertisements, notice and noise which minimize the rank of blog site. Furthermore to retrieve more relevant blogs is another problem which lowering the search performance. This study proposes blogs search engine adopting RSS syndication using Fuzzy logic. The blogs search engine consists of three main phases which are crawling using RSS feeds algorithm, indexing weblogs algorithm and searching technique with Fuzzy logic. In RSS crawling process RSS feeds need to be gathered to extract useful information such as title, links, publish time and description. Indexing weblogs use the links to retrieve the blogs sites for text processing and construct indexing database. In order to retrieve such information needed by any user, there is user interface to search for keyword with importance degree and compute the density of keyword from the indexing database. The rank of the pages is computed based on fuzzy weighted average value. A prototype is built using visual basic 2008 to validate the proposed blogs search engine. It is a windows application with http connection protocol. In system evaluation used two measurement performances which are precision and mean average precision. The parameters of precision determine based on respondents whom determine the total retrieved links and the total relevant links for the keyword search result. The number of keywords that used in testing system is five pairs keywords. The experimental results show that the mean average precision is 81.7% of the whole system performance. The percent of respondents is 80% who knows and uses the blogs and 20% don’t have knowledge. The execution time of the system based on respondents is 70% between 3-5 minute and 30% less than 3 minute. This percentage is good considering the rate of satisfaction for system is 80% satisfied and 20% strongly satisfied

A series of case studies to enhance the social utility of RSS

RSS (really simple syndication, rich site summary or RDF site summary) is a dialect of XML that provides a method of syndicating on-line content, where postings consist of frequently updated news items, blog entries and multimedia. RSS feeds, produced by organisations or individuals, are often aggregated, and delivered to users for consumption via readers. The semi-structured format of RSS also allows the delivery/exchange of machine-readable content between different platforms and systems. Articles on web pages frequently include icons that represent social media services which facilitate social data. Amongst these, RSS feeds deliver data which is typically presented in the journalistic style of headline, story and snapshot(s). Consequently, applications and academic research have employed RSS on this basis. Therefore, within the context of social media, the question arises: can the social function, i.e. utility, of RSS be enhanced by producing from it data which is actionable and effective? This thesis is based upon the hypothesis that the fluctuations in the keyword frequencies present in RSS can be mined to produce actionable and effective data, to enhance the technology's social utility. To this end, we present a series of laboratory-based case studies which demonstrate two novel and logically consistent RSS-mining paradigms. Our first paradigm allows users to define mining rules to mine data from feeds. The second paradigm employs a semi-automated classification of feeds and correlates this with sentiment. We visualise the outputs produced by the case studies for these paradigms, where they can benefit users in real-world scenarios, varying from statistics and trend analysis to mining financial and sporting data. The contributions of this thesis to web engineering and text mining are the demonstration of the proof of concept of our paradigms, through the integration of an array of open-source, third-party products into a coherent and innovative, alpha-version prototype software implemented in a Java JSP/servlet-based web application architecture

A series of case studies to enhance the social utility of RSS

RSS (really simple syndication, rich site summary or RDF site summary) is a dialect of XML that provides a method of syndicating on-line content, where postings consist of frequently updated news items, blog entries and multimedia. RSS feeds, produced by organisations or individuals, are often aggregated, and delivered to users for consumption via readers. The semi-structured format of RSS also allows the delivery/exchange of machine-readable content between different platforms and systems. Articles on web pages frequently include icons that represent social media services which facilitate social data. Amongst these, RSS feeds deliver data which is typically presented in the journalistic style of headline, story and snapshot(s). Consequently, applications and academic research have employed RSS on this basis. Therefore, within the context of social media, the question arises: can the social function, i.e. utility, of RSS be enhanced by producing from it data which is actionable and effective? This thesis is based upon the hypothesis that the fluctuations in the keyword frequencies present in RSS can be mined to produce actionable and effective data, to enhance the technology's social utility. To this end, we present a series of laboratory-based case studies which demonstrate two novel and logically consistent RSS-mining paradigms. Our first paradigm allows users to define mining rules to mine data from feeds. The second paradigm employs a semi-automated classification of feeds and correlates this with sentiment. We visualise the outputs produced by the case studies for these paradigms, where they can benefit users in real-world scenarios, varying from statistics and trend analysis to mining financial and sporting data. The contributions of this thesis to web engineering and text mining are the demonstration of the proof of concept of our paradigms, through the integration of an array of open-source, third-party products into a coherent and innovative, alpha-version prototype software implemented in a Java JSP/servlet-based web application architecture

Expanding perspective on open science: communities, cultures and diversity in concepts and practices

Twenty-one years ago, the term ‘electronic publishing’ promised all manner of potential that the Web and network technologies could bring to scholarly communication, scientific research and technical innovation. Over the last two decades, tremendous developments have indeed taken place across all of these domains. One of the most important of these has been Open Science; perhaps the most widely discussed topic in research communications today. This book presents the proceedings of Elpub 2017, the 21st edition of the International Conference on Electronic Publishing, held in Limassol, Cyprus, in June 2017. Continuing the tradition of bringing together academics, publishers, lecturers, librarians, developers, entrepreneurs, users and all other stakeholders interested in the issues surrounding electronic publishing, this edition of the conference focuses on Open Science, and the 27 research and practitioner papers and 1 poster included here reflect the results and ideas of researchers and practitioners with diverse backgrounds from all around the world with regard to this important subject. Intended to generate discussion and debate on the potential and limitations of openness, the book addresses the current challenges and opportunities in the ecosystem of Open Science, and explores how to move forward in developing an inclusive system that will work for a much broader range of participants. It will be of interest to all those concerned with electronic publishing, and Open Science in particular