myEACBR - myCBR as explanation-aware Protégé plugin

Explanation, trust, and transparency are concepts that are strongly tied in with users' confidence in, and acceptance of computerised systems. Case-based reasoning (CBR) systems lend themselves easily to generate explanations, as they typically organise and represent knowledge in a way that makes it possible to reason about and thereby generate explanations. The work presented here is a first step towards making a CBR engine explanation-aware. We demonstrate how a plugin for Protégé and myCBR can facilitate explanations for the retrieval phase of a CBR system

A Comparative Review on Computational Modeling Paradigms. A Study on Case-Based Modeling and Political Terrorism

We review the advances in Case-Based Computational Modeling on Political Analysis issues. Starting in early „70s, the research on political terrorism has been challenged by the latest advances of terrorism computational modeling research. Nowadays Political Analysis community has a wider perspective over the terrorism research aims, methodology and instruments. Widening up this perspective is not a matter of political analysis and research only, it is as well a long-term effect of an interdisciplinary style which has been adopted within the area by acknowledging the scientific advances and support of the Computational Modeling and Simulation as a specific scientific research method. Computational Modeling includes several research frameworks. The Case-Based Modeling is analysed and evaluated on a comparative basis with Agent-Based Modeling in a study on political terrorism phenomena

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Retrieval, reuse, revision and retention in case-based reasoning

El original está disponible en www.journals.cambridge.orgCase-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention.Peer reviewe

Lakewide and Nearshore Microbial Water Quality Modelling in Lake St. Clair

Lake St. Clair is a freshwater lake in the Lake Huron to Erie corridor in the Great Lakes Basin. Millions of people in Canada and the United States rely on that water source for drinking, fishing and recreational purposes. Lake St. Clair’s watershed is heavily impacted by human activity, which can result in contamination of its waters by fecal matter of human or animal origin containing waterborne pathogens, and thus pose a direct threat to human health. Common sources of such pollution include combined sewer overflows, wastewater treatment plant bypasses, and agricultural application of manure derived from animal fecal waste. Several such sources are present in Windsor Essex County (WEC), Ontario, Canada, which is located along the southern edge of Lake St. Clair. Two popular public beaches and drinking water intakes are located in the nearshore region adjacent to the southern edge. Fecal microbial pollution is currently monitoring using fecal indicator bacteria (FIB), such as Escherichia coli (E. coli). Monitoring methods have several limitations including their inability to predict water quality in real-time or in advance, or to identify potential sources of contamination for more effective management. Mathematical models are tools that can be very effective and complementary to monitoring in overcoming its limitations. Model predictions can be real-time or near real-time and also help to identify or exonerate potential sources of microbial pollution. In the current study, two types of modelling approaches that are commonly being used in the assessment of microbial contamination in beach waters and lakes were investigated: statistical modelling based on multiple linear regression (MLR) and hydrodynamic-ecological modelling. The statistical MLR models developed for Sandpoint Beach in Lake St. Clair showed higher accuracy in the range 64-78%, for predicting both exceedance and non-exceedance of the applicable standard, as compared to 54% accuracy obtained using the current method based on E. coli measurements. Amongst the MLR models developed, an increase of about 5-14% in model performance was observed when qualitative sky weather condition was included. Results with mechanistic structured grid high-resolution AEM3D model developed for Lake St. Clair showed that four major tributaries (Thames, Sydenham, St. Clair and Clinton River) are unlikely to be responsible for the E. coli exceedances of provincial guideline observed at Sandpoint Beach. Amongst the major tributaries, predicted E. coli concentrations were dominated by the contribution of St. Clair River for most of Lake St. Clair. The maximum predicted E. coli concentration from the combined input of the major tributaries was less than 100 CFU/100 ml for most of the lake and less than 10 CFU/100 ml at Sandpoint Beach. Predicted E. coli were significantly affected by varying water temperature and sunlight result in the temporal and diurnal dynamics of microbial water quality in Lake St. Clair. About 12–148% differences in predicted E. coli concentrations were observed at six drinking water intakes located in Lake St. Clair when time-variable decay rates were used instead of a constant decay rate. Also, on average nighttime E. coli predictions were 21–68% higher at these water intakes, as compared to daytime levels. Results from the AEM3D model showed that while the flow contribution of eight smaller tributaries in Windsor Essex Region to the lake is insignificant (less than 0.2%), their contribution to the adjacent nearshore region along the southern edge of Lake St. Clair could be quite significant. Within about 1 km from the shoreline of this nearshore region, flow contributions from the small tributaries were estimated in the range between 18-35%, while their contribution to E. coli concentration was estimated to be more than 80%. Results with mechanistic unstructured grid TUFLOW-FV/AED2+ lakewide model and with a finer mesh nested model over a 2 km region surrounding Belle River showed differences of up to a factor of four in predicted E. coli concentrations at adjacent Lakeview Park West Beach (LP Beach). The differences reduced to a factor of up to 1.3 at nearby Lakeshore WTP intake located about one km away from shore. While the average contribution of the Belle River to E. coli concentrations at Lakeshore WTP intake was predicted to be \u3c20%, the contribution increased to \u3e80% when higher concentrations (10-35 CFU/ 100 ml) were predicted. The results also indicate that the construction of the marina may have contributed to some increase in E. coli concentrations at LP Beach from the external sources considered. However, construction of a new 150 m jetty in 2018, in place of the 25 m jetty separating Belle River from LP Beach, is expected to reduce the E. coli concentrations at LP Beach from the same sources by about 80%

Automatic Geospatial Data Conflation Using Semantic Web Technologies

Duplicate geospatial data collections and maintenance are an extensive problem across Australia government organisations. This research examines how Semantic Web technologies can be used to automate the geospatial data conflation process. The research presents a new approach where generation of OWL ontologies based on output data models and presenting geospatial data as RDF triples serve as the basis for the solution and SWRL rules serve as the core to automate the geospatial data conflation processes