Flexible knowledge representation and new similarity measure: Application on case based reasoning for waste treatment

In Case Based Reasoning the representation of a case and the similarity measures are two difficult steps in the conception of a system. Often, these steps are developed to resolve one kind of problem. However, in some of them such as recovery treatment processes generation, it is necessary for the system to be able to modify and adapt the representation of a case and the similarity measures with respect of the context and also the kind of solutions proposed. In this paper, authors introduce a new method to represent cases with a flexibility based on a structure in a connectionist model. This flexibility is needed due to the complexity of cases, the number of possible options and to ensure the durability of the system. In a second main contribution, authors introduce a method for the selection of source cases using abstraction, conceptualisation and inference mechanisms. Finally, authors test their system in a CBR developed on SWI-Prolog with different problems. The CBR is applied to find new recovery processes and try to estimate the new upgraded product generated

Knowledge capturing and usage of evolving cloud application topologies

In the last few years, the advent of Cloud Computing has contributed to a considerable increase of the number of service offerings from a variety of providers and every time more applications are being partially or fully deployed in the cloud. Nowadays, experts invest considerable time and effort towards taking the maximum advantage of the benefits that the employment of cloud technologies brings. Such a wide spectrum of cloud offerings, however, increase the number of complex tasks and aspects that must be taken into account when distributing the components of an application such as the quality of service, adaptation to market changes, etc. Furthermore, the distribution of the components and the configuration of cloud resources may evolve over time and there is a lack of tooling support when it comes to assist the developers in the decision-making tasks of selecting an appropriate application distribution topology. One of the methods that human-beings use to solve problems consists of recalling past experiences and how they solved them at that time. Under this and other considerations the Case-Based Reasoning paradigm was conceived, as a mechanism for solving tasks by recalling past similar problems and adapting their solutions to new situations. This work aims to develop the concepts and mechanisms that enable the capturing and usage of knowledge that the evolution of a system brings along. Specifically, this thesis attempts to identify a set of characteristics that accurately describe a cloud application and to define the models that should be used to identify the cases solved in the past and whose solutions may be useful to solve a new problem. To achieve this, the Case-Based Reasoning with Similarity Retrieval approach is employed, to identify applications with similar characteristics, retrieve their solutions and offer means to refine them in order to obtain a distribution topology that fulfills the requirements of a given application. Furthermore, a prototypical implementation of the approach of this thesis is executed and also employed to validate the concepts and principles that this work follows

Similarity Assessment for Generalizied Cases by Optimization Methods

Generalized cases are cases that cover a subspace rather than a point in the problem-solution space. Generalized cases can be represented by a set of constraints over the case attributes. For such representations, the similarity assessment between a point query and generalized cases is a difficult problem that is addressed in this paper. The task is to find the distance (or the related similarity) between the point query and the closest point of the area covered by the generalized cases, with respect to some given similarity measure. We formulate this problem as a mathematical optimization problem and we propose a new cutting plane method which enables us to rank generalized cases according to their distance to the query

Similarity Assessment for Generalizied Cases by Optimization Methods

Generalized cases are cases that cover a subspace rather than a point in the problem-solution space. Generalized cases can be represented by a set of constraints over the case attributes. For such representations, the similarity assessment between a point query and generalized cases is a difficult problem that is addressed in this paper. The task is to find the distance (or the related similarity) between the point query and the closest point of the area covered by the generalized cases, with respect to some given similarity measure. We formulate this problem as a mathematical optimization problem and we propose a new cutting plane method which enables us to rank generalized cases according to their distance to the query