Clustering analysis (CA) techniques consist in, given a set of objects, estimating dense regions of points separated by sparse regions, according to the dimensions that describe these objects. Independently from the data nature – structured or non-structured -, we look for homogenous clouds of points, that define clusters, from which we want to extract some meaning. In other words, when doing CA, the analyst is searching for underlying structures in a multidimensional space for what one could assign some meaning. Grossly, to carry a CA application, two main activities are involved: generating clusters configurations by means of an algorithm and interpreting these configurations in order to approximate a solution that could contribute with the CA application objective. Generating a clusters configuration is typically a computational task, while the interpretation task brings a strong burden of subjectivity. Many approaches are presented in the literature for generating clusters configuration. Unfortunately, the interpretation task has not received so much attention, possibly due to the difficulty in modeling something that is subjective in nature. In this chapter a method to guide the interpretation of a clusters configuration is proposed. The inherent subjectivity is approached directly by describing the process with the apparatus of the Ontology of Language. The main contribution of this chapter is to provide a sound conceptual basis to guide the analyst in extracting meaning from the patterns found in a set of data, no matter we are talking about data bases, a set of free texts, or a set of web pages.

The clusters' analysis process comprises two broad activities: generation of a clusters set and extracting meaning from these clusters. The first one refers to the application of algorithms to estimate high density areas separated by lower density areas from the observed space. In the second one the analyst goes inside the clusters trying to figure out some sense from them. The whole activity requires previous knowledge and a considerable burden of subjectivity. In previous works, some alternatives were proposed to take into account the background knowledge when creating the clusters. However, the subjectivity of the interpretation activity continues to be a challenge. Beyond soundness domain knowledge from specialists, a consensual interpretation depends on conversational competences for which no support has been provided. We propose a method for cluster interpretation based on the categories existing in the Ontology of Language, aiming to reduce the gap between a cluster configuration and the effective extraction of meaning from them.

Abstract. Knowledge discovery from databases, in the descriptive approach, includes clustering analysis (CA) as an alternative to estimate how a set of objects is organized in the space of their dimensions. The main objective in this task is to find "natural" groups that could exhibit some meaning. Considering the strong subjectivity that underlies this process, an important issue refers to the relationships among the CA players when looking for a model that could adjust the data. In this work, a model for actions coordination that provides an order to drive the relationships among CA players is presented. This model is presented as a conceptual contribution towards the construction of a computational environment to support effective conversations in a subjective context.

In the last decades many research efforts have been devoted to improve electronic business among partner enterprises. The well succeeded results of these efforts led to the widespread use of tools like eCO-Framework (Macro-Economic Framework), EDI (Electronic Document Interchange) and SWIFT (Society for Worldwide Interbank Financial Telecommunication). More recently, the ebXML standard has been developed to expand the B2B (Business to Business) practice, assuring security at a low cost and enabling the commerce among small and medium businesses. However, ebXML provides only physical connection among the parts, lacking support for a business protocol capable of helping a negotiation. A well succeeded negotiation requires a dialog, involving customers and suppliers, product specification, requests, offers, requirements, all in a cycle of successive refinement of expectations. This paper aims at contributing to bridge this gap by means of a conceptual model for negotiation based on the client satisfaction cycle of Flores. Additionally, we demonstrate the electronic commerce route among the enterprises, describe some patterns that led to the arising of ebXML and that are part of its current core, specify the ebXML and propose some trends for the B2B commerce.

A pesquisa investiga a importância das conversas e seu potencial de contribuição para as rotinas de produção da notícia em ambientes de redações jornalísticas. Tendo como base a Ontologia da Linguagem, inicialmente caracterizou-se como descritiva, aplicada e exploratória. Ao desenvolver o instrumento de análise, denominado Matriz de Sentidos, a pesquisa passou a ser considerada também metodológica, tendo como base a Teoria Fundamentada nos Dados (TFD). Uma das contribuições deste trabalho está em situar a importância das conversas, dando-lhes visibilidade formal, teórica, filosófica e metodológica no processo de produção das notícias. A Matriz de Sentidos contribuiu para a explicação de padrões de comportamentos e se apresenta como um instrumento que pode ser customizado e replicado para outros contextos em que as conversações possuam um papel relevante.