Tiago Guimarães

Model repair and transformation with Echo

Models are paramount in model-driven engineering. In a software project many models may coexist, capturing different views of the system or different levels of abstraction. A key and arduous task in this development method is to keep all such models consistent, both with their meta-models (and the respective constraints) and among themselves. This paper describes Echo, a tool that aims at simplifying this task by automating inconsistency detection and repair using a solver based engine. Consistency between different models can be specified by bidirectional model transformations, and is guaranteed to be recovered by minimal updates on the inconsistent models. The tool is freely available as an Eclipse plugin, developed on top of the popular EMF framework, and supports constraints and transformations specified in the OMG standard languages OCL and QVT-R, respectively.

Target Oriented Relational Model Finding

Model finders are becoming useful in many software engineering problems. Kodkod [19] is one of the most popular, due to its support for relational logic (a combination of first order logic with relational algebra operators and transitive closure), allowing a simpler specification of constraints, and support for partial instances, allowing the specification of a priori (exact, but potentially partial) knowledge about a problems solution. However, in some software engineering problems, such as model repair or bidirectional model transformation, knowledge about the solution is not exact, but instead there is a known target that the solution should approximate. In this paper we extend Kodkods partial instances to allow the specification of such targets, and show how its model finding procedure can be adapted to support them (using both PMax-SAT solvers or SAT solvers with cardinality constraints). Two case studies are also presented, including a careful performance evaluation to assess the effectiveness of the proposed extension.

Exploring scenario exploration

Model finders are very popular for exploring scenarios, helping users validate specifications by navigating through conforming model instances. To be practical, the semantics of such scenario exploration operations should be formally defined and, ideally, controlled by the users, so that they are able to quickly reach interesting scenarios.

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.

Step towards Multiplatform Framework for Supporting Pediatric and Neonatology Care Unit Decision Process

Abstract Children are an especially vulnerable population, particularly in respect to drug administration. It is estimated that neonatal and pediatric patients are at least three times more vulnerable to damage due to adverse events and medication errors than adults are. With the development of this framework, it is intended the provision of a Clinical Decision Support System based on a prototype already tested in a real environment. The framework will include features such as preparation of Total Parenteral Nutrition prescriptions, table pediatric and neonatal emergency drugs, medical scales of morbidity and mortality, anthropometry percentiles (weight, length/height, head circumference and BMI), utilities for supporting medical decision on the treatment of neonatal jaundice and anemia and support for technical procedures and other calculators and widespread use tools. The solution in development means an extension of INTCare project. The main goal is to provide an approach to get the functionality at all times of clinical practice and outside the hospital environment for dissemination, education and simulation of hypothetical situations. The aim is also to develop an area for the study and analysis of information and extraction of knowledge from the data collected by the use of the system. This paper presents the architecture, their requirements and functionalities and a SWOT analysis of the solution proposed.

Pervasive Business Intelligence in Misericordias – A Portuguese Case Study

The healthcare system is one of the main pillars of any society. However, it carries with it an enormous economic weight. Portugal, alongside with many others, is a country in economic and social restructuration and consequently, the need to increase the efficiency of resource management and services is imperative. With the proven effectiveness of Business Intelligence (BI) in many organisations, the urge to implement such tools in Healthcare arises, specifically in the healthcare of Misericordia. In addition to the results, it presents a critical analysis of the implementation and the process followed for the development and usage of KPIs. In this work, some concepts associated with the use of BI in Misericordias were addressed, and the architecture of the developed solution was designed. It is also important to emphasise that the solution presented is pervasive, available anywhere at any time. Through this work, it was possible to gather all the data into a single structure (Data Mart), to identify a set of aspects that can be improved and to have a generalised view of the state of operation of the organisation, as far as health care is concerned. The developed includes ten KPIs in the area of Surgery Production and Waiting List Surgery. The dashboards can be analysed in several dimensions: date, specialities, physicians, service, diagnosis, location and time.

Data Mining Classification Models for Industrial Planning

The data mining models are an excellent tool to help companies that live from the sale of items they produce. With these models combined with Lean Production, it becomes easier to remove waste and optimize industrial production. This project is based on the phases of the methodology CRISP-DM. Several methods were applied to this data namely, average, mean and standard deviation, quartiles and Sturges rule. Classification Techniques were used in order to understand which model has the best probability of hitting the correct result. After performing the tests, model M1 was the one with the best chance to accomplish a great level of classification having 99.52% of accuracy.

An Overview of the Quality of Service in Bluetooth Communications in Healthcare

Currently, the general public requires devices getting faster and great performance, that is, devices ensuring a better quality of service. One way to achieve these goals is through the use of devices supported by the mobile computing with tools to help the search for information. Bluetooth technology is an open standard for wireless communication allowing the transmission of data and information between electronic devices within walking distance, with minimum resource expenditures, safe and rapid transition of data. So, the Bluetooth technology was initially designed to support simple network devices and personal devices such as mobile phones, PDAs and computers, but quickly it were discovered other applications in several areas. In this article, it will be performed a literature review on the topic, with the goal to understand how the Bluetooth technology can benefit increases in the Quality of Service and the presentation of some actual and potential biomedical applications.