Managing Scholarly Communication in the Web 2.0 Environment: The Role of University Libraries

Scholarly communication over the past 10 to 15 years has gained a tremendous momentum with the advent of Internet and the World Wide Web. The web has transformed the ways by which people search, find, use and communicate information. Innovations in web technology since 2005 have brought out an array of new services and facilities and an enhanced version of the web named Web 2.0. Web 2.0 facilitates a collaborative environment in which the information users can interact with the information. Web 2.0 enables its users to create, annotate, review, share re-use and represent the information in new ways thereby optimizing the information dissemination

physics of the Web

Science is search for the laws of underlying phenomena of the nature. Engineering constructs the nature as we wish. Interestingly the huge engineering infrastructure like world wide web has grown in such a complex structure such that we need to see the fundamental science behind the structure and behaviour of these networks. This talk covers the science behind the complex networks like web, biological, social etc. The talk aim to discuss the basic theories that govern the static as well as the dynamics of such interesting networks

Web 2.0 Technologies for Education

Page 1. Web 2.0 Technologies for Education G. Santhosh Kumar Dept. Of Computer Science Cochin University Page 2. What is Internet? CUSAT is linked to this Web through 10 Mbps leased line connectivity Page 3. Size of the Web? GYWA = Sorted on Google, Yahoo!, Windows Live Search (Msn Search) and Ask YGWA = Sorted on Yahoo!, Google, Windows Live Search (Msn Search) and Ask www.worldwidewebsize.com Page 4. The Machine is Us/ing Us ■ http://in.youtube.com/watch?v=NLlGopyXT_g&feature=channel Page 5. ..

Towards Web 3.0

Page 1. Towards Web 3.0... • Web 1.0 • Web 2.0 • Web 3.0 • Technology hype? • Internet as seen by our kids? Page 2. Random Trivia: Brazil has more Orkut users than citizens Page 3. The war is over. Platforms have won. Applications have lost Page 4. Page 5. Blogosphere • The blogosphere is made up of all blogs and their interconnections Page 6. Social bookmarking Page 7. Page 8. Page 9. Page 10. Page 11. Page 12. Page 13. Page 14. Towards Web 3.0 Page 15. Page 16. Wolfram Alpha Page 17. Page 18. Page 19. Page 20. Page 21. Page 22

eLearning and Semantic Web

Semantic Web: Software agents on the Semantic Web may use commonly agreed service language, which enables co-ordination between agents and proactive delivery of learning materials in the context of actual problems. The vision is that each user has his own personalized agent that communicates with other agents.

Adoption of Koha Open Source Software in Libraries: a Web Based Study

Since last few years open source integrated library systems gaining attention of library and information science professionals. This paper tries to identify the extent of adoption of Koha, an open source ILS in libraries around the world through a Web based study. The study found that Koha adoption in libraries is still at infancy

Web Service Composition Systems for the Web Service Challenge - A Detailed Review

This report gives a detailed discussion on the system, algorithms, and techniques that we have applied in order to solve the Web Service Challenges (WSC) of the years 2006 and 2007. These international contests are focused on semantic web service composition. In each challenge of the contests, a repository of web services is given. The input and output parameters of the services in the repository are annotated with semantic concepts. A query to a semantic composition engine contains a set of available input concepts and a set of wanted output concepts. In order to employ an offered service for a requested role, the concepts of the input parameters of the offered operations must be more general than requested (contravariance). In contrast, the concepts of the output parameters of the offered service must be more specific than requested (covariance). The engine should respond to a query by providing a valid composition as fast as possible. We discuss three different methods for web service composition: an uninformed search in form of an IDDFS algorithm, a greedy informed search based on heuristic functions, and a multi-objective genetic algorithm.

Towards semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.

Semantic web mining and the representation, analysis, and evolution of web space

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: Growing numbers of researchers work on improving the results of Web Mining by exploiting semantic structures in the Web, and they use Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The second aim of this paper is to use these concepts to circumscribe what Web space is, what it represents and how it can be represented and analyzed. This is used to sketch the role that Semantic Web Mining and the software agents and human agents involved in it can play in the evolution of Web space.

Semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.

Managing Quality Properties of Web Service Compositions

Web services from different partners can be combined to applications that realize a more complex business goal. Such applications built as Web service compositions define how interactions between Web services take place in order to implement the business logic. Web service compositions not only have to provide the desired functionality but also have to comply with certain Quality of Service (QoS) levels. Maximizing the users' satisfaction, also reflected as Quality of Experience (QoE), is a primary goal to be achieved in a Service-Oriented Architecture (SOA). Unfortunately, in a dynamic environment like SOA unforeseen situations might appear like services not being available or not responding in the desired time frame. In such situations, appropriate actions need to be triggered in order to avoid the violation of QoS and QoE constraints. In this thesis, proper solutions are developed to manage Web services and Web service compositions with regard to QoS and QoE requirements. The Business Process Rules Language (BPRules) was developed to manage Web service compositions when undesired QoS or QoE values are detected. BPRules provides a rich set of management actions that may be triggered for controlling the service composition and for improving its quality behavior. Regarding the quality properties, BPRules allows to distinguish between the QoS values as they are promised by the service providers, QoE values that were assigned by end-users, the monitored QoS as measured by our BPR framework, and the predicted QoS and QoE values. BPRules facilitates the specification of certain user groups characterized by different context properties and allows triggering a personalized, context-aware service selection tailored for the specified user groups. In a service market where a multitude of services with the same functionality and different quality values are available, the right services need to be selected for realizing the service composition. We developed new and efficient heuristic algorithms that are applied to choose high quality services for the composition. BPRules offers the possibility to integrate multiple service selection algorithms. The selection algorithms are applicable also for non-linear objective functions and constraints. The BPR framework includes new approaches for context-aware service selection and quality property predictions. We consider the location information of users and services as context dimension for the prediction of response time and throughput. The BPR framework combines all new features and contributions to a comprehensive management solution. Furthermore, it facilitates flexible monitoring of QoS properties without having to modify the description of the service composition. We show how the different modules of the BPR framework work together in order to execute the management rules. We evaluate how our selection algorithms outperform a genetic algorithm from related research. The evaluation reveals how context data can be used for a personalized prediction of response time and throughput.