Web 2.0 and folksonomies in a library context

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2011 ElsevierLibraries have a societal purpose and this role has become increasingly important as new technologies enable organizations to support, enable and enhance the participation of users in assuming an active role in the creation and communication of information. Folksonomies, a Web 2.0 technology, represent such an example. Folksonomies result from individuals freely tagging resources available to them on a computer network. In a library environment folksonomies have the potential of overcoming certain limitations of traditional classification systems such as the Library of Congress Subject Headings (LCSH). Typical limitations of this type of classification systems include, for example, the rigidity of the underlying taxonomical structures and the difficulty of introducing change in the categories. Folksonomies represent a supporting technology to existing classification systems helping to describe library resources more flexibly, dynamically and openly. As a review of the current literature shows, the adoption of folksonomies in libraries is novel and limited research has been carried out in the area. This paper presents research into the adoption of folksonomies for a University library. A Web 2.0 system was developed, based on the requirements collected from library stakeholders, and integrated with the existing library computer system. An evaluation of the work was carried out in the form of a survey in order to understand the possible reactions of users to folksonomies as well as the effects on their behavior. The broad conclusion of this work is that folksonomies seem to have a beneficial effect on users’ involvement as active library participants as well as encourage users to browse the catalogue in more depth

Classifying Web 2.0 Supported Applications By Pattern Of Usage: Functional & Technical ISSUES

The rapid evolution of Internet technologies have witnessed new Web elements, such as blogs, wikis, social networking, social bookmarking, and other related applications referred to as Web 2.0. Web 1.0 paradigm was related with passive, just receptive users, whereas Web 2.0 paradigm relies mainly on user participation and user-generated content. In Web 2.0 applications users are invited to comment, share, edit, classify, as well as remix data from multiple sources. Although there are several Web 2.0 applications in the market there is still lack of a profound approach guiding the analysis, design and development of such applications. This paper suggests classifying Web 2.0 tools by “Pattern of Usage” or in other words the functionalities that characterize their specific features. By reviewing several literatures we extracted multiple attributes related to functionalities of Web 2.0 tools. These have been crystallised into 7 patterns of usage that include; Inter-connectivity, Content authoring, Content tagging & rating, Content aggregation & syndication, Content remixing, Content streaming and File sharing. By interlinking functionality/ usage with underlying technologies, techniques and architecture we provided insight into design and technical requirements for Web 2.0 supported applications. Furthermore we broke down the patterns into basic, elementary to include Inter-connectivity, File sharing and Content remixing, and secondary, supportive to include the other four patterns. This would provide the technical core for any development methodology targeted at Web 2.0 applications

Building and exploiting context on the web

[no abstract

Changing Higher Education Learning with Web 2.0 and Open Education Citation, Annotation, and Thematic Coding Appendices

Appendices of citations, annotations and themes for research conducted on four websites: Delicious, Wikipedia, YouTube, and Facebook

ABSTRACTS OF POSTERS

N/

Tag disambiguation based on social network information

Within 20 years the Web has grown from a tool for scientists at CERN into a global information space. While returning to its roots as a read/write tool, its entering a more social and participatory phase. Hence a new, improved version called the Social Web where users are responsible for generating and sharing content on the global information space, they are also accountable for replicating the information. This collaborative activity can be observed in two of the most widely practised Social Web services such as social network sites and social tagging systems. Users annotate their interests and inclinations with free form keywords while they share them with their social connections. Although these keywords (tag) assist information organization and retrieval, theysuffer from polysemy.In this study we employ the effectiveness of social network sites to address the issue of ambiguity in social tagging. Moreover, we also propose that homophily in social network sites can be a useful aspect is disambiguating tags. We have extracted the ‘Likes’ of 20 Facebook users and employ them in disambiguation tags on Flickr. Classifiers are generated on the retrieved clusters from Flickr using K-Nearest-Neighbour algorithm and then their degree of similarity is calculated with user keywords. As tag disambiguation techniques lack gold standards for evaluation, we asked the users to indicate the contexts and used them as ground truth while examining the results. We analyse the performance of our approach by quantitative methods and report successful results. Our proposed method is able classify images with an accuracy of 6 out of 10 (on average). Qualitative analysis reveal some factors that affect the findings, and if addressed can produce more precise results

Social and Semantic Contexts in Tourist Mobile Applications

The ongoing growth of the World Wide Web along with the increase possibility of access information through a variety of devices in mobility, has defi nitely changed the way users acquire, create, and personalize information, pushing innovative strategies for annotating and organizing it. In this scenario, Social Annotation Systems have quickly gained a huge popularity, introducing millions of metadata on di fferent Web resources following a bottom-up approach, generating free and democratic mechanisms of classi cation, namely folksonomies. Moving away from hierarchical classi cation schemas, folksonomies represent also a meaningful mean for identifying similarities among users, resources and tags. At any rate, they suff er from several limitations, such as the lack of specialized tools devoted to manage, modify, customize and visualize them as well as the lack of an explicit semantic, making di fficult for users to bene fit from them eff ectively. Despite appealing promises of Semantic Web technologies, which were intended to explicitly formalize the knowledge within a particular domain in a top-down manner, in order to perform intelligent integration and reasoning on it, they are still far from reach their objectives, due to di fficulties in knowledge acquisition and annotation bottleneck. The main contribution of this dissertation consists in modeling a novel conceptual framework that exploits both social and semantic contextual dimensions, focusing on the domain of tourism and cultural heritage. The primary aim of our assessment is to evaluate the overall user satisfaction and the perceived quality in use thanks to two concrete case studies. Firstly, we concentrate our attention on contextual information and navigation, and on authoring tool; secondly, we provide a semantic mapping of tags of the system folksonomy, contrasted and compared to the expert users' classi cation, allowing a bridge between social and semantic knowledge according to its constantly mutual growth. The performed user evaluations analyses results are promising, reporting a high level of agreement on the perceived quality in use of both the applications and of the speci c analyzed features, demonstrating that a social-semantic contextual model improves the general users' satisfactio

Improving Usability And Scalability Of Big Data Workflows In The Cloud

Big data workflows have recently emerged as the next generation of data-centric workflow technologies to address the five “V” challenges of big data: volume, variety, velocity, veracity, and value. More formally, a big data workflow is the computerized modeling and automation of a process consisting of a set of computational tasks and their data interdependencies to process and analyze data of ever increasing in scale, complexity, and rate of acquisition. The convergence of big data and workflows creates new challenges in workflow community. First, the variety of big data results in a need for integrating large number of remote Web services and other heterogeneous task components that can consume and produce data in various formats and models into a uniform and interoperable workflow. Existing approaches fall short in addressing the so-called shimming problem only in an adhoc manner and unable to provide a generic solution. We automatically insert a piece of code called shims or adaptors in order to resolve the data type mismatches. Second, the volume of big data results in a large number of datasets that needs to be queried and analyzed in an effective and personalized manner. Further, there is also a strong need for sharing, reusing, and repurposing existing tasks and workflows across different users and institutes. To overcome such limitations, we propose a folksonomy- based social workflow recommendation system to improve workflow design productivity and efficient dataset querying and analyzing. Third, the volume of big data results in the need to process and analyze data of ever increasing in scale, complexity, and rate of acquisition. But a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. We propose a NoSQL collectional data model that addresses this limitation. Finally, the volume of big data combined with the unbound resource leasing capability foreseen in the cloud, facilitates data scientists to wring actionable insights from the data in a time and cost efficient manner. We propose BARENTS scheduler that supports high-performance workflow scheduling in a heterogeneous cloud-computing environment with a single objective to minimize the workflow makespan under a user provided budget constraint