Signaling emotion in tagclouds

ABSTRACT In order to create more attractive tagclouds that get people interested in tagged content, we propose a simple but novel tagcloud where font size is determined by tag's entropy value, not the popularity to its content. Our method raises users' emotional interest in the content by emphasizing more emotional tags. Our initial experiments show that emotional tagclouds attract more attention than normal tagclouds at first look; thus they will enhance the role of tagcloud as a social signaller

Identificação de critérios para avaliação de ideias: um método utilizando folksonomias

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2016.As ferramentas de cocriação encontram uma rica fonte de conhecimento baseada nas interações sociais que ocorrem na Web. Essa interação coletiva é a principal característica dos Sistemas de apoio à inovação, em especial para os sistemas de gestão de ideias. Entretanto, para avaliar ideias, as soluções atuais limitam-se a métodos baseados em formulários com critérios pré-estabelecidos ou, então, por métricas de engajamento social. O contexto organizacional é crítico para o sucesso de uma ideia, porém, ao considerar apenas índices de popularidade, as avaliações não agregam semanticamente o conhecimento atribuído pelo usuário, bem como não determinam quais critérios foram ponderados pela comunidade. A fim de compreender este conhecimento coletivo, a presente pesquisa propõe um método de identificação e análise de critérios para a avaliação de ideias. O desenvolvimento desse artefato é baseado na metodologia da ciência do design e explora o conhecimento a partir de atribuições sociais por notas e tags, as folksonomias. Assim, no contexto do front end da Inovação, o método representa uma apropriação semântica e qualitativa dos critérios atribuídos pela comunidade. A verificação utiliza técnicas da mineração de folksonomias em uma base de dados representada por um modelo de hipergrafo. Como resultado, o método permite evidenciar um conjunto de características a serem consideradas pela organização como critérios de avaliação. Além disso, a solução constata que a popularidade não é uma medida de consenso da comunidade, portanto sub comunidades auferem medidas mais precisas em suas atribuições; e a flexibilização temporal, própria das interações sociais, colaboram na recomendação de ideias baseada em tendências e no contexto organizacional.Abstract : Co-creation tools meet a rich source of knowledge on social interactions that occurs on the Web. This collective interaction is the main characteristic of innovation support systems, especially idea management systems. However, in order to evaluate ideas, current solutions are limited to methods based on forms with pre-established criteria or metrics of social engagement. The organizational context is critical to the success of an idea. Nevertheless, when considering just popularity ratings, the evaluations do not semantically aggregate the knowledge attributed by the user. It also does not determine what criteria was weighted by the community. In order to understand this collective knowledge, the present research proposes a method for identification and analysis of criteria in idea evaluation. The development of this artefact is based on the design science research methodology, and it explores the knowledge from social attributions using grades and tags, also known as folksonomy. Therefore, within the front end of innovation, the method represents a semantic, qualitative appropriation of criteria attributed by the community. The artefact was verified using folksonomy mining techniques in a database represented by a hypergraph model. As a result, the method allows to visualize a set of characteristics to be considered as evaluation criteria by any organization. In addition, the results showed that popularity is not a community s consensus measure. Therefore, sub communities get more precise measurements in their attributes; and temporal flexibility, which is specific to social interactions, collaborate on the idea recommendation based on trends and organizational context

Data and Text Mining Techniques for In-Domain and Cross-Domain Applications

In the big data era, a wide amount of data has been generated in different domains, from social media to news feeds, from health care to genomic functionalities. When addressing a problem, we usually need to harness multiple disparate datasets. Data from different domains may follow different modalities, each of which has a different representation, distribution, scale and density. For example, text is usually represented as discrete sparse word count vectors, whereas an image is represented by pixel intensities, and so on. Nowadays plenty of Data Mining and Machine Learning techniques are proposed in literature, which have already achieved significant success in many knowledge engineering areas, including classification, regression and clustering. Anyway some challenging issues remain when tackling a new problem: how to represent the problem? What approach is better to use among the huge quantity of possibilities? What is the information to be used in the Machine Learning task and how to represent it? There exist any different domains from which borrow knowledge? This dissertation proposes some possible representation approaches for problems in different domains, from text mining to genomic analysis. In particular, one of the major contributions is a different way to represent a classical classification problem: instead of using an instance related to each object (a document, or a gene, or a social post, etc.) to be classified, it is proposed to use a pair of objects or a pair object-class, using the relationship between them as label. The application of this approach is tested on both flat and hierarchical text categorization datasets, where it potentially allows the efficient addition of new categories during classification. Furthermore, the same idea is used to extract conversational threads from an unregulated pool of messages and also to classify the biomedical literature based on the genomic features treated

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

The global intelligent file system framework.

"Since its inception the Internet has grown rapidly in both size and importance in our everyday lives. The Internet today is the preliminary model of what is commonly called the global information infrastructure. However, at the moment this "infrastructure" is considered to be an addition to our computer, and is not an integrated part of a file system which is essentially a "local information infrastructure" of a computer. Advancements in the sizes of disks in computers, network bandwidth and the types of media available mean users now keep large amounts of files in their personal data storage spaces, with little or no additional support for the organisation, searching or sharing of this data. The hierarchical model of file system storage is no longer the most effective way of organising and categorising files and information. Relying largely on the user, rather than the computer, being efficient and organised its inflexible nature renders it unsuitable for the meaningful coordination of an increasing bulk of divergent file types that users deal with on a daily basis. The work presented in this thesis describes a new paradigm for file storage, management and retrieval. Providing globally integrated document emplacement and administration, the GIFS (Global Intelligent File System) framework offers the necessary architecture for transparently directing the storage, access, sharing, manipulation, and security of files across interconnected computers. To address the discrepancy between user actions and computer actions, GIFS provides each user with a "Virtual Secretary" to reduce the cognitive workload and remove the time-consuming task of information organisation from the user. The Secretary is supported by a knowledge base and a collection of intelligent agents, which are programs that manage and process the data collected, and work behind the scenes aiding gradual proliferation of knowledge. The Virtual Secretary is responsible for providing fast and accurate assistance to aid users who wish to create, store, retrieve, share, secure and collaborate on their files. Through both system prototyping and performance simulation it is demonstrated that it is desirable as well as feasible to deploy a knowledge base in supporting an intelligent user interface that acts like a human assistant who handles paperwork, looks after filing, security and so on. This work provides the contribution of a new framework and architecture to the field of files systems and document management as well as focusing on reducing the burden placed upon users through everyday usage of computer systems. Such a framework has the potential to be evolved into a highly intelligent assistant to a user over a period of service and the introduction of additional agents, and provides the basis for advancements in file system and organisational technologies.