A case studies approach to the analysis of profiling and framing structures for pervasive information systems

Model-Based/Driven Development (MDD) constitutes an approach to software design and development that potentially contributes to: concepts closer to domain and reduction of semantic gaps; automation and less sensitivity to technological changes; capture of expert knowledge and reuse. The widespread adoption of pervasive technologies as basis for new systems and applications, lead to the need of effectively design pervasive information systems that properly fulfil the goals they were designed for. This paper presents a profiling and framing structure approach for the development of Pervasive Information Systems (PIS). This profiling and framing structure allows the organization of the functionality that can be assigned to computational devices in a system and of the corresponding development structures and models, being. The proposed approach enables a structural approach to PIS development. The paper also presents two case studies that allowed demonstrating the applicability of the approach.Fundação para a Ciência e a Tecnologia (FCT

SPEM 2.0 extension for pervasive information systems

Pervasive computing is a research field of computing technology that aims to achieve a new computing paradigm. In this paradigm, the physical environment has a high degree of pervasiveness and availability of computers and other information technology (IT) devices, usually with communication capabilities. Pervasive Information Systems (PIS), composed by these kinds of devices, bring issues that challenge software development for them. Model-Driven Development (MDD), strongly focusing and relying on models, has the potential to allow: the use of concepts closer to the domain and the reduction of semantic gaps; higher automation and lower dependency to technological changes; higher capture of expert knowledge and reuse; an overall increased productivity. Along with the focus and use of models, software development processes are fundamental to efficient development efforts of successful software systems. For the description of processes, Software & Systems Process Engineering Meta-Model Specification (SPEM) is the current standard specification published by the Object Management Group (OMG). This paper presents an extension to SPEM (version 2.0) Base Plug-In Profile that includes stereotypes needed to support a suitable structural process organization for MDD approaches aiming to develop software for PIS. A case study is provided to evaluate the applicability of the extension

Profiling and framing structures for pervasive information systems development

Pervasive computing is a research field of computing technology that aims to achieve a new computing paradigm. Software engineering has been, since its existence, subject of research and improvement in several areas of interest. Model-Based/Driven Development (MDD) constitutes an approach to software design and development that potentially contributes to: concepts closer to domain and reduction of semantic gaps; automation and less sensitivity to technological changes; capture of expert knowledge and reuse. This paper presents a profiling and framing structure approach for the development of Pervasive Information Systems (PIS). This profiling and framing structure allows the organization of the functionality that can be assigned to computational devices in a system and of the corresponding development structures and models, being. The proposed approach enables a structural approach to PIS development. The paper also presents a case study that allowed demonstrating the applicability of the approach.Fundação para a Ciência e a Tecnologia (FCT

A reusable application framework for context-aware mobile patient monitoring systems

The development of Context-aware Mobile Patient Monitoring Systems (CaMPaMS) using wireless sensors is very complex. To overcome this problem, the Context-aware Mobile Patient Monitoring Framework (CaMPaMF) was introduced as an ideal reuse technique to enhance the overall development quality and overcome the development complexity of CaMPaMS. While a few studies have designed reusable CaMPaMFs, there has not been enough study looking at how to design and evaluate application frameworks based on multiple reusability aspects and multiple reusability evaluation approaches. Furthermore, there also has not been enough study that integrates the identified domain requirements of CaMPaMS. Therefore, the aim of this research is to design a reusable CaMPaMF for CaMPaMS. To achieve this aim, twelve methods were used: literature search, content analysis, concept matrix, feature modelling, use case assortment, domain expert review, model-driven architecture approach, static code analysis, reusability model approach, prototyping, amount of reuse calculation, and software expert review. The primary outcome of this research is a reusable CaMPaMF designed and evaluated to capture reusability from different aspects. CaMPaMF includes a domain model validated by consultant physicians as domain experts, an architectural model, a platform-independent model, a platform-specific model validated by software expert review, and three CaMPaMS prototypes for monitoring patients with hypertension, epilepsy, or diabetes, and multiple reusability evaluation approaches. This research contributes to the body of software engineering knowledge, particularly in the area of design and evaluation of reusable application frameworks. Researchers can use the domain model to enhance the understanding of CaMPaMS domain requirements, thus extend it with new requirements. Developers can also reuse and extend CaMPaMF to develop various CaMPaMS for different diseases. Software industries can also reuse CaMPaMF to reduce the need to consult domain experts and the time required to build CaMPaMS from scratch, thus reducing the development cost and time

Cognitive feedback as a tool for knowledge acquisition

Knowledge acquisition is often considered a 'bottleneck' in the development of expert systems. This study conducted a review of 14 knowledge acquisitions methods with a survey of knowledge types, task characteristics, and representation schemes. All of the knowledge acquisitions techniques are considered deficient in their ability to capture a representation of an expert's mental model and procedural knowledge. Cognitive feedback and the lens model, drawn from Egon Brunswik's probabilistic functionalism, are proposed as an alternate knowledge acquisition methodology. Cognitive feedback's theoretical underpinnings are explained as are the various uses to which it has been put. A summary of the many research studies conducted into the effectiveness of cognitive feedback is presented. An automated knowledge acquisition tool using cognitive feedback is proposed and illustrated with state transition diagrams and sample computer screens.http://archive.org/details/cognitivefeedbac1094534923Lieutenant, United States NavyApproved for public release; distribution is unlimited

Expert Elicitation for Reliable System Design

This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Towards automated knowledge-based mapping between individual conceptualisations to empower personalisation of Geospatial Semantic Web

Geospatial domain is characterised by vagueness, especially in the semantic disambiguation of the concepts in the domain, which makes defining universally accepted geo- ontology an onerous task. This is compounded by the lack of appropriate methods and techniques where the individual semantic conceptualisations can be captured and compared to each other. With multiple user conceptualisations, efforts towards a reliable Geospatial Semantic Web, therefore, require personalisation where user diversity can be incorporated. The work presented in this paper is part of our ongoing research on applying commonsense reasoning to elicit and maintain models that represent users' conceptualisations. Such user models will enable taking into account the users' perspective of the real world and will empower personalisation algorithms for the Semantic Web. Intelligent information processing over the Semantic Web can be achieved if different conceptualisations can be integrated in a semantic environment and mismatches between different conceptualisations can be outlined. In this paper, a formal approach for detecting mismatches between a user's and an expert's conceptual model is outlined. The formalisation is used as the basis to develop algorithms to compare models defined in OWL. The algorithms are illustrated in a geographical domain using concepts from the SPACE ontology developed as part of the SWEET suite of ontologies for the Semantic Web by NASA, and are evaluated by comparing test cases of possible user misconceptions