Abstract
The problem of discovering previously unknown frequent patterns in time series, also called motifs, has been recently introduced. A motif is a subseries pattern that appears a significant number of times. Results demonstrate that motifs may provide valuable insights about the data and have a wide range of applications in data mining tasks. The main motivation for this study was the need to mine time series data from protein folding/unfolding simulations. We propose an algorithm that extracts approximate motifs, i.e. motifs that capture portions of time series with a similar and eventually symmetric behavior. Preliminary results on the analysis of protein unfolding data support this proposal as a valuable tool. A.dditional experiments demonstrate that the application of utility of our algorithm is not limited to this particular problem. Rather it can be an interesting tool to be applied in many real world problems.

Abstract

Abstract
We propose an approach to subgroup discovery using distribution rules (a kind of association rules with a probability distribution on the consequent) for numerical properties of interest. The objective interest of the subgroups is measured through statistical goodness of fit tests. Their subjective interest can be assessed by the data analyst through a visual interactive subgroup browsing procedure.

Abstract
In this paper we introduce distribution rules, a kind of association rules with a distribution on the consequent. Distribution rules are related to quantitative association rules but can be seen as a more fundamental concept, useful for learning distributions. We formalize the main concepts and indicate applications to tasks such as frequent pattern discovery, sub group discovery and forecasting. An efficient algorithm for the generation of distribution rules is described. We also provide interest measures, visualization techniques and evaluation.

Abstract
Many software systems can be viewed as organizational Systems, where the different components are seen as autonomous entities, interacting with each other, collaborating toward system's aims. In such systems we may not have full control over the behavior of all its components. Normative specification of an organizational system, provides a way of describing the norms that regulate the behavior of a system and of its components, stating how they are expected to behave, assuming however, that they may deviate from that ideal behavior. In this paper we use an action and deontic modal logic for the normative specification of organizational systems. This logical framework allows us to describe expected behavior of agents, detect non-ideal behavior and identify the agents that, direct or indirectly, are responsible for it. We argue that normative specification can be an useful tool to increase trust and security in complex computational systems and propose a responsibility-based trust concept.

Abstract
The computational requirements of a full physically-based global illumination solution are significant, currently precluding its solution on even a powerful modern PC in reasonable let alone real time. A key factor to consider if we are ever to achieve so-called "Realism in Real-Time", is that we are computing images for humans to look at. Although the human visual system is very good, it is by no means perfect. By understanding what the human does, or perhaps more importantly, does not see, enables us to save significant computation effort without any loss of perceptual quality of the resultant image. This paper describes the novel techniques of selective rendering which allow us to direct computational resources to those areas of high perceptual importance while avoiding computing any detail which will not be perceived by the viewer. Such selective rendering methods offer us the real possibility of achieving high fidelity graphics of complex scenes at interactive rates.