Coherent Integration of Databases by Abductive Logic Programming

We introduce an abductive method for a coherent integration of independent data-sources. The idea is to compute a list of data-facts that should be inserted to the amalgamated database or retracted from it in order to restore its consistency. This method is implemented by an abductive solver, called Asystem, that applies SLDNFA-resolution on a meta-theory that relates different, possibly contradicting, input databases. We also give a pure model-theoretic analysis of the possible ways to `recover' consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible. This allows us to characterize the `recovered databases' in terms of the `preferred' (i.e., most consistent) models of the theory. The outcome is an abductive-based application that is sound and complete with respect to a corresponding model-based, preferential semantics, and -- to the best of our knowledge -- is more expressive (thus more general) than any other implementation of coherent integration of databases

Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation

A human computation system can be viewed as a distributed system in which the processors are humans, called workers. Such systems harness the cognitive power of a group of workers connected to the Internet to execute relatively simple tasks, whose solutions, once grouped, solve a problem that systems equipped with only machines could not solve satisfactorily. Examples of such systems are Amazon Mechanical Turk and the Zooniverse platform. A human computation application comprises a group of tasks, each of them can be performed by one worker. Tasks might have dependencies among each other. In this study, we propose a theoretical framework to analyze such type of application from a distributed systems point of view. Our framework is established on three dimensions that represent different perspectives in which human computation applications can be approached: quality-of-service requirements, design and management strategies, and human aspects. By using this framework, we review human computation in the perspective of programmers seeking to improve the design of human computation applications and managers seeking to increase the effectiveness of human computation infrastructures in running such applications. In doing so, besides integrating and organizing what has been done in this direction, we also put into perspective the fact that the human aspects of the workers in such systems introduce new challenges in terms of, for example, task assignment, dependency management, and fault prevention and tolerance. We discuss how they are related to distributed systems and other areas of knowledge.Comment: 3 figures, 1 tabl

Team Learning: A Theoretical Integration and Review

With the increasing emphasis on work teams as the primary architecture of organizational structure, scholars have begun to focus attention on team learning, the processes that support it, and the important outcomes that depend on it. Although the literature addressing learning in teams is broad, it is also messy and fraught with conceptual confusion. This chapter presents a theoretical integration and review. The goal is to organize theory and research on team learning, identify actionable frameworks and findings, and emphasize promising targets for future research. We emphasize three theoretical foci in our examination of team learning, treating it as multilevel (individual and team, not individual or team), dynamic (iterative and progressive; a process not an outcome), and emergent (outcomes of team learning can manifest in different ways over time). The integrative theoretical heuristic distinguishes team learning process theories, supporting emergent states, team knowledge representations, and respective influences on team performance and effectiveness. Promising directions for theory development and research are discussed

Argumentation for Knowledge Representation, Conflict Resolution, Defeasible Inference and Its Integration with Machine Learning

Modern machine Learning is devoted to the construction of algorithms and computational procedures that can automatically improve with experience and learn from data. Defeasible argumentation has emerged as sub-topic of artificial intelligence aimed at formalising common-sense qualitative reasoning. The former is an inductive approach for inference while the latter is deductive, each one having advantages and limitations. A great challenge for theoretical and applied research in AI is their integration. The first aim of this chapter is to provide readers informally with the basic notions of defeasible and non-monotonic reasoning. It then describes argumentation theory, a paradigm for implementing defeasible reasoning in practice as well as the common multi-layer schema upon which argument-based systems are usually built. The second aim is to describe a selection of argument-based applications in the medical and health-care sectors, informed by the multi-layer schema. A summary of the features that emerge from the applications under review is aimed at showing why defeasible argumentation is attractive for knowledge-representation, conflict resolution and inference under uncertainty. Open problems and challenges in the field of argumentation are subsequently described followed by a future outlook in which three points of integration with machine learning are proposed

Improving the geospatial consistency of digital libraries metadata

Consistency is an essential aspect of the quality of metadata. Inconsistent metadata records are harmful: given a themed query, the set of retrieved metadata records would contain descriptions of unrelated or irrelevant resources, and may even not contain some resources considered obvious. This is even worse when the description of the location is inconsistent. Inconsistent spatial descriptions may yield invisible or hidden geographical resources that cannot be retrieved by means of spatially themed queries. Therefore, ensuring spatial consistency should be a primary goal when reusing, sharing and developing georeferenced digital collections. We present a methodology able to detect geospatial inconsistencies in metadata collections based on the combination of spatial ranking, reverse geocoding, geographic knowledge organization systems and information-retrieval techniques. This methodology has been applied to a collection of metadata records describing maps and atlases belonging to the Library of Congress. The proposed approach was able to automatically identify inconsistent metadata records (870 out of 10,575) and propose fixes to most of them (91.5%) These results support the ability of the proposed methodology to assess the impact of spatial inconsistency in the retrievability and visibility of metadata records and improve their spatial consistency

The Psychosocial Work Environment, Employee Mental Health and Organizational Interventions: Improving Research and Practice by Taking a Multilevel Approach

Although there have been several calls for incorporating multiple levels of analysis in employee health and wellbeing research, studies examining the interplay between individual, workgroup, organizational and broader societal factors in relation to employee mental health outcomes remain an exception rather than the norm. At the same time, organizational intervention research and practice also tends to be limited by a single-level focus, omitting potentially important influences at multiple levels of analysis. The aims of this conceptual paper are to help progress our understanding of work-related determinants of employee mental health by: (i) providing a rationale for routine multilevel assessment of the psychosocial work environment; (ii) discussing how a multilevel perspective can improve related organizational interventions and (iii) highlighting key theoretical and methodological considerations relevant to these aims. We present five recommendations for future research, relating to using appropriate multilevel research designs, justifying group level constructs, developing group-level measures, expanding investigations to the organizational level, and developing multilevel approaches to intervention design, implementation and evaluation

Guidance on the integrated assessment of complex health technologies: the INTEGRATE-HTA model

Challenges in assessments of health technologies In recent years there have been major advances in the development of health technology assessment (HTA). However, HTA still has certain limitations when assessing technologies which are complex, i.e. consist of several interacting components, target different groups or organizational levels, have multiple and variable outcomes, and/or permit a certain degree of flexibility or tailoring (Craig et al., 2008), fi are context-dependent - current HTA usually focusses on the technology, not on the system within which it is used, fi perform differently depending on the way they are implemented, fi have different effects on different individuals. Furthermore, HTA usually assesses and appraises aspects side-by-side, while decision-making needs an integrated perspective on the value of a technology. In the EU-funded INTEGRATE-HTA project, we developed concepts and methods to deal with these challenges, which are described in six guidances. Because of the interactions, an integrated assessment needs to start from the beginning of the assessment. This guidance provides a systematic five-step-process for an integrated assessment of complex technologies (the INTEGRATE-HTA Model). Purpose and scope of the guidance The aim of the INTEGRATE-HTA project is to provide concepts and methods that enable a patient-centred, comprehensive, and integrated assessment of complex health technologies. The purpose of this guidance is to structure the overall HTA-process. The INTEGRATE-HTA Model outlines an integrated scoping process, a coordinated application of assessment methods for different aspects and an integrated and structured decision-making process. It is intended for HTA agencies, HTA researchers and those engaged in the evaluation of complex health technologies. As it links the assessment to the decision-making process, it also addresses HTA commissioners and other stakeholders using or planning HTAs. While all technologies are arguably complex, some are more complex than others. Applying this guidance might lead to a more thorough and therefore more time-consuming process. Depending on the degree of complexity, one might choose to follow the whole process as described in this guidance, or only focus on certain steps. The guidance provides an operational definition to assess the complexity of technologies which can be used to identify specific aspects that will need more attention than others. What the guidance does not provide is a post-hoc solution for assessments that have already been completed. | 6 Development of the guidance The INTEGRATE-HTA Model presented in this guidance was developed based on a systematic literature search on approaches for integration, on the experiences of traditional HTAs, as well as on the other methodological guidances developed in the INTEGRATE-HTA project. It was tested in a case study on palliative care and iteratively revised during the practical application. The guidance was again revised after internal and external peer-review. Application of this guidance For a comprehensive integrated assessment of a complex technology, we developed a five-step process, the INTEGRATE-HTA model. In Step 1, the HTA objective and the technology are defined with the support from a panel of stakeholders. An initial logic model is developed in Step 2. The initial logic model provides a structured overview of the technology, the context, implementation issues, and relevant patient groups. It then frames the assessment of the effectiveness, as well as economic, ethical, legal, and socio-cultural aspects in Step 3. In Step 4, a graphical overview of the assessment results, structured by the logic model, is provided. Step 5 is a structured decision-making process informed by the HTA (and is thus not formally part of the HTA, but follows it). fi Step 1: In step 1, the technology under assessment and the objective of the HTA are defined. Especially for complex technologies, such as palliative care, the definition of the technology alone is a challenge that must not be underestimated. It is recommended to do this based on a tentative literature review and with the support of stakeholder advisory panels (SAPs) which should comprise clinical experts, academics, patients, possibly their relatives and/or other caretakers, and the public. The setting of an objective considering all relevant aspects of complexity and structured by assessment criteria is important. The assessment criteria will usually reflect values of the stakeholders as well as the input from the theoretical, methodological and empirical literature. fi Step 2: In step 2, an initial logic model is developed (see Guidance on the use of logic models in health technology assessments of complex interventions). The model provides a structured overview on participants, interventions, comparators, and outcomes. Parallel to this, groups of patients that are distinguished by different preferences and treatment moderators (see Guidance for the assessment of treatment moderation and patients’ preferences) are identified. Specific context and implementation issues are also identified as part of the initial logic model (see Guidance for the Assessment of Context and Implementation in Health Technology Assessments (HTA) and Systematic Reviews of Complex Interventions). The product of this step is the logic model as a graphical representation of all aspects and their interactions that are relevant for the assessment of the complex technology. fi Step 3: In step 3, the logic model serves as a conceptual framework that guides the evidence assessment. Depending on the specific aspect (e.g. effectiveness, economic, ethical, socio-cultural, or legal aspects) different methods are available for the assessment (see Guidance for assessing effectiveness, economic aspects, ethical aspects, socio-cultural aspects and legal aspects in complex technologies). The outputs of step 3 are evidence reports and standardized evidence summaries for each assessment aspect (e.g. report on economics, report on ethical aspects, etc.). fi Step 4: In step 4, the assessment results of step 3 are structured using the logic model developed in step 2. Whereas the initial logic model in step 2 specifies what evidence is relevant, the extended logic model to assist decision-making in step 4 visualizes the assessment results as well as the interaction with respect to the HTA objectives. It also allows for the consideration of different scenarios depending on the variation in context, implementation and patient characteristics. 7 | fi Step 5: Step 5 involves a structured decision-making process and is not an integral part of the HTA in the narrow sense. Decision-making can be supported by applying quantitative e.g. MCDA- (Multi-criteria decision analysis) or qualitative decision support tools. Flexibility in the application of these tools by the decision committee is crucial, taking different decision settings and evidence needs into consideration. Conclusions In current HTA, different aspects are usually assessed and presented independent of each other. Context, implementation issues and patient characteristics are rarely considered. The INTEGRATE-HTA Model enables a coordinated assessment of all these aspects and addresses their interdependencies. The perspective of stakeholders such as patients and professionals with their values and preferences is integrated in the INTEGRATE-HTA Model to obtain HTA results that are meaningful for all relevant stakeholders. Finally, health policy makers obtain an integrated perspective of the assessment results to achieve fair and legitimate conclusions at the end of the HTA process. The application of the model will usually require more time and resources than traditional HTA. An initial assessment of the degree and the character of complexity of a technology might be helpful to decide whether or not the whole process or only specific elements will be applied