Frederico Araujo Durão

A systematic review on cloud computing

Cloud computing is an ascending technology that has introduced a new paradigm by rendering a rational computational model possible. It has changed the dynamics of IT consumption by means of a model that provides on-demand services over the Internet. Unlike the traditional hosting service, cloud computing services are paid for per usage and may expand or shrink based on demand. Such services are, in general, fully managed by cloud providers that require users nothing but a personal computer and an Internet access. In recent years, this model has attracted the attention of researchers, investors and practitioners, many of whom have proposed a number of applications, structures and fundamentals of cloud computing, resulting in various definitions, requirements and models. Despite the interest and advances in the field, issues such as security and privacy, service layer agreement, resource sharing, and billing have opened up new questions about the real gains of the model. Although cloud computing is based on a 50-year-old business model, evidence from this study indicates that cloud computing still needs to expand and overcome present limitations that prevent the full use of its potential. In this study, we critically review the state of the art in cloud computing with the aim of identifying advances, gaps and new challenges.

Extending a hybrid tag-based recommender system with personalization

Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. This paper proposes a semantic extension for a hybrid tag-based recommender system, which suggests similar Web pages based on the similarity of their tags. The semantic extension aims at discovering tag relations which are not considered in basic syntax similarity. With the goal of generating more semantically grounded recommendations, the proposal extends a hybrid tag-based recommender system with a semantic factor, which looks for tag relations in different semantic sources. In order to evaluate the benefits acquired with the semantic extension, we have compared the new findings with results from a previous experiment involving 38 people from 12 countries using data from del.icio.us.

Methodologies for improved tag cloud generation with clustering

Tag clouds are useful means for navigation in the social web systems. Usually the systems implement the tag cloud generation based on tag popularity which is not always the best method. In this paper we propose methodologies on how to combine clustering into the tag cloud generation to improve coverage and overlap. We study several clustering algorithms to generate tag clouds. We show that by extending cloud generation based on tag popularity with clustering we slightly improve coverage. We also show that if the cloud is generated by clustering independently of the tag popularity baseline we minimize overlap and increase coverage. In the first case we therefore provide more items for a user to explore. In the second case we provide more diverse items for a user to explore. We experiment with the methodologies on two different datasets: Delicious and Bibsonomy. The methodologies perform slightly better on bibsonomy due to its specific focus. The best performing is the hierarchical clustering.

Improving tag-based recommendation with the collaborative value of wiki pages for knowledge sharing

This exploratory study investigates how organisations can support knowledge transferring by exploiting social and community intelligence. In particular, this work analysis the potential of wiki technology as a tool for knowledge sharing in corporate wikis. Wikis are hypertext systems that support team-oriented collaborative work. Corporate wikis are especially turned to enhance internal knowledge sharing in enterprises. This research study sought to empirically determine the value of wiki pages that emerged from such collaboration in corporate wikis. As a research challenge, we evaluate how tag-based recommendations benefit from this value in a problem solving context. The recommendations are evaluated on their capability of transferring knowledge and help users to solve tasks. In this sense, we create a problem solving scenario where users need to use the recommendations to get their tasks solved. Meanwhile, we attempt to support users individually to find their own solutions, our recommendations are intended to enhance the overall organisation’s problem solving capacity. Results from an experiment with 63 participants show that more successful recommendations can be obtained if the collaborative value of pages is considered. In essence, this work demonstrates how the value of wiki pages can produce significant quality support in assisting individuals to get their problems solved and sharing knowledge in collaborative spaces. In addition to this evaluation, professionals from software companies were interviewed about the usefulness and adoption of the recommendation model in their corporate wikis.

How people care about their personal data released on social media

Content sharing services have become immensel popular on the Web. More than 1 billion people use this kind o services to communicate with friends and exchange all sorts o information. In this new context, privacy guarantees are essential guarantees about the potential release of data to unintended recipients and the use of user data by the service provider Although the general public is concerned about privacy question related to unintended audiences, data usage by service provider is still misunderstood. In order to further explore this level o misunderstanding, this work presents the results of a surve: conducted among 900 people with the aim of discovering hov people care about the use of their personal data by servic providers in terms of social media. From the results, we found that: (i) in general people do not read license terms and do no know very much about service policies, and when presented with these policies people do not agree with them; (ii) a good number of people would support alternative models such as paying for privacy or selling their personal data; and (iii) there are some differences between generations in relation to how they care about their data.

Towards effective group recommendations for microblogging users

In this paper we propose a group recommendation model that infers the group preferences based on the actions of its members. Such recommendations can be useful when individuals are working together in areas that require relevant up-to-date news information to support decisions. To test our model, we used Twitter, a microblogging service, as a platform to recommend links to news articles. To evaluate our model, we compared the group satisfaction with different strategies of group recommendation. Results show that our model obtained an average group rating of 3.58 out of 5 over the recommendations given to the group. This represents an improvement of approximately 25% over the best performing strategy we tested. We also analyzed the impact of different actions on Twitter and of a time decay parameter on group satisfaction

SimSpectrum: a similarity based spectral clustering approach to generate a tag cloud

Tag clouds are means for navigation and exploration of information resources on the web provided by social Web sites. The most used approach to generate a tag cloud so far is based on popularity of tags among users who annotate by those tags. This approach however has several limitations, such as suppressing number of tags which are not used often but could lead to interesting resources as well as tags which have been suppressed due to the default number of tags to present in the tag cloud. In this paper we propose the SimSpectrum: a similarity based spectral clustering approach to generate a tag cloud which improves the current state of the art with respect to these limitations. Our approach is based on finding to which extent the tags are related by a similarity calculus. Based on the results from similarity calculation, the spectral clustering algorithm finds the clusters of tags which are strongly related and are loosely related to the other tags. By doing so, we can cover part of the tags which are discarded by traditional tag cloud generation approaches and therefore, present the user with more opportunities to find related interesting web resources. We also show that in terms of the metrics that capture the structural properties of a tag cloud such as coverage and relevance our method has significant results compared to the baseline tag cloud that relies on tag popularity. In terms of the overlap measure, our method shows improvements against the baseline approach. The proposed approach is evaluated using MedWorm medical article collection.

Recommending Open Linked Data in Creativity Sessions using Web Portals with Collaborative Real Time Environment

In this paper we describe a concept of the recommender system for col- laborative real time web based editing in the context of creativity sessions. The col- laborative real time editing provides creativity teams of which members are physically distributed with an emulation of the synchronous collaboration where presence of the team members is required simultaneously (e.g., brainstorming, meetings). The concept of recommendation is based on matchmaking the currently performed activities at the user interface and external linked open data provided through SPARQL endpoints. The real time propagation of the changes in editor and recommendation is achieved by reverse AJAX and observer pattern. An experiment in the area of the creativity domain shows that the recommendation in collaborative real time editing activities are useful in task performance, guidance, and inspiration.

RESTful, resource-oriented architectures: a model-driven approach

RESTful Web services have opened the door to clients to use Web sites in ways the original designers never imagined giving rise to the mashup phenomenon. The main advantage of the model based approach in Web engineering is that the models specify sort of contract the Web application adheres to and promises to deliver. Similarly, in RESTful scenario, mashup components responsible for delivering composite functionalities out of RESTful components could benefit from such contracts in search, automatic mashup, and other scenarios. Such scenarios ground the need for taking RESTful Web services in existing Web methods. This paper proposes the Application Facade Component Model in existingWeb methods to support RESTful, resource-oriented architectures generation. Amazon Simple Storage Service is used as the running example and proof of concept to show advantages of such approach.

Social and Behavioral Aspects of a Tag-Based Recommender System

Collaborative tagging has emerged as a useful means to organize and share resources on the Web. Recommender systems have been utilized tags for identifying similar resources and generate personalized recommendations. In this paper, we analyze social and behavioral aspects of a tag-based recommender system which suggests similar Web pages based on the similarity of their tags. Tagging behavior and language anomalies in tagging activities are some aspects examined from an experiment involving 38 people from 12 countries.