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Mining association rules is a well known framework for extracting useful knowledge from databases. Despite their proven applicability there exist other approaches that also search for novel and useful information such us peculiarities, infrequent rules, exceptions or anomalous rules. The common feature of these proposals is the low support of such type of rules. So there is a necessity of finding efficient algorithms for extracting them.

The principal objective of this paper is providing a unified framework for dealing with such kind of rules. In our case, we take advantage of an existing logic approach called GUHA. This model was first presented in the middle sixties by Hájek et al. and then has been developed by Rauch and others in the last decade.

Following this line, this paper also offers some interesting issues. First, it provides a deep analysis of semantics and formulation of exception and anomalous rules. Second, we define the so called double rules as a new type of rules which in conjunction with exceptions and anomalies will describe in more detail the relationship between two sets of items. Third, we give new approaches for mining them and we propose an algorithm with reasonably good performance.

Over the past few decades, knowledge management (KM) has become well-established in many fields, particularly in business. Several KM models have been at the forefront of promoting KM in businesses and organisations. However, the applicability of these traditional KM models to the global health field is limited by their focus on KM processes and activities with few linkages to intended outcomes. This paper presents the new Knowledge Management for Global Health (KM4GH) Logic Model, a practical tool that helps global health professionals plan ways in which resources and specific KM activities can work together to achieve desired health program outcomes. We test the validity of this model through three case studies of global and field-level health initiatives: an SMS-based mobile phone network among community health workers (CHWs) and their supervisors in Malawi, a global electronic Toolkits platform that provides health professionals access to health information resources, and a netbook-based eHealth pilot among CHWs and their clients in Bangladesh. The case studies demonstrate the flexibility of the KM4GH Logic Model in designing various KM activities while defining a common set of metrics to measure their outcomes, providing global health organisations with a tool to select the most appropriate KM activities to meet specific knowledge needs of an audience. The three levels of outcomes depicted in the model, which are grounded in behavioural theory, show the progression in the behaviour change process, or in this case, the knowledge use process, from raising awareness of and using the new knowledge to contributing to better health systems and behaviours of the public, and ultimately to improving the health status of communities and individuals. The KM4GH Logic Model makes a unique contribution to the global health field by helping health professionals plan KM activities with the end goal in mind.

Since previous frameworks and methods for evaluation of national R&D have mainly focused on short-term R&D outputs, long-term R&D outcomes have been neglected so that they have failed to be fully utilized and spread. Thus, it is imperative to mirror long-term R&D outcomes in performance evaluation and utilize them through continuous management and monitoring. In response, this paper presents a model of R&D performance monitoring, evaluation, and management system (RPMEMS) for national R&D. The conceptual model of RPMES is presented with its scope, structure, role, and process. It is based on the logic model used in the planning and evaluation of public programs. This study also proposes two methods for relative comparison in the context of RPMEMS: analytic hierarchy process (AHP) and data envelopment analysis (DEA). The proposed model of RPMEMS and methods for relative comparison are expected to be effectively implemented and operated in the practice of management of national R&D.

The purpose of this study is to present the methodology by which public enterprises with different characteristics of mission and business can develop social value measurement models and establish the importance of each indicator. Incheon International Airport Corporation (IIAC), a case study company, is Korea’s leading public company and the third largest international freight-handling company. IIAC operates an international airport that is used by 68 million people annually and has collaborated with more than 600 partners. Firstly, this study reviewed the strategy/mission of IIAC and analyzed 68 recent innovation tasks. In the second step, social value stakeholders were defined. As a third step, this research developed a social value measurement model by extracting input, process, output, and outcome indicators from 68 innovation tasks using the Logic Model. As a fourth step, the social value measurement model was described to social value stakeholders and components and weights were determined using the AHP methodology.

In this study, a social value measurement model consisting of five indexes and forty indicators was developed. The upper five indexes are as follows: (1) job creation, (2) equal opportunities and social integration, (3) safety and environment, (4) win–win cooperation and regional development, and (5) ethical management. The study showed that safety and the environment were important more to partner firms and airport customers than to internal employees.

This study suggests a social value measurement model that can be used to determine how much social value performance public enterprises are generating, and how the resources should be optimized to maximize social value performance in future.