Toward a multidisciplinary model of context to support context-aware computing

Capturing, defining, and modeling the essence of context are challenging, compelling, and prominent issues for interdisciplinary research and discussion. The roots of its emergence lie in the inconsistencies and ambivalent definitions across and within different research specializations (e.g., philosophy, psychology, pragmatics, linguistics, computer science, and artificial intelligence). Within the area of computer science, the advent of mobile context-aware computing has stimulated broad and contrasting interpretations due to the shift from traditional static desktop computing to heterogeneous mobile environments. This transition poses many challenging, complex, and largely unanswered research issues relating to contextual interactions and usability. To address those issues, many researchers strongly encourage a multidisciplinary approach. The primary aim of this article is to review and unify theories of context within linguistics, computer science, and psychology. Summary models within each discipline are used to propose an outline and detailed multidisciplinary model of context involving (a) the differentiation of focal and contextual aspects of the user and application's world, (b) the separation of meaningful and incidental dimensions, and (c) important user and application processes. The models provide an important foundation in which complex mobile scenarios can be conceptualized and key human and social issues can be identified. The models were then applied to different applications of context-aware computing involving user communities and mobile tourist guides. The authors' future work involves developing a user-centered multidisciplinary design framework (based on their proposed models). This will be used to design a large-scale user study investigating the usability issues of a context-aware mobile computing navigation aid for visually impaired people

Identifying Social Computing Dimensions: A Multidimensional Scaling Study

Despite an increasing popularity, the impact and benefits of corporate social computing remain unclear. This paper aims at rigorously studying social computing tools as a new class of technology and provides a holistic definition and characterization. After a comprehensive literature review, we empirically explored the defining attributes and underlying dimensions of social computing as a whole using the multidimensional scaling (MDS) methodology. The study found that 13 representative exemplar tools differ over three dimensions: (i) their ability to support social interactions, social relations, and communities, (ii) their hedonic versus utilitarian focus, and (iii) their ability to support convergence versus conveyance of generated content. A Property Fitting (ProFit) study confirmed the interpretation of the dimensions. This provided a better understanding of this technology and allowed us to better theorize about the expected benefits and impacts of social computing on organizations, to offer guidelines for adoption and provide suggestions for future research

EGI: anOpen e-Infrastructure Ecosystem for the Digital European Research Area

Bringing the digital European Research Area (ERA) online means modernising Europe’s research infrastructure by promoting open science through the availability, accessibility and reuse of scientific data and results, the use of web- based tools that facilitate scientific collaboration and ensuring public access to research. As the European Grid Infrastructure (EGI) is the largest European distributed computing infrastructure providing 24/7 access to large scale computing, storage and data resources through a federation of national resource providers, it allows scientists from all disciplines to make the most out of the latest computing technologies for the benefit of their research. This paper describes the methodology and approach for defining EGI’s role in bringing this digital ERA online. The work presented defines the roles and functions of EGI as an open ICT ecosystem, required service redesign, the added value of EGI for the European research communities and demonstrates the role that EGI plays in contributing to the Europe 2020 strategy for social-economic impact

Semantic-based policy engineering for autonomic systems

This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise

Compressing networks with super nodes

Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network to be partitioned into a smaller network of 'super nodes', each super node comprising one or more nodes in the original network. To define the seeds of our super nodes, we apply the 'CoreHD' ranking from dismantling and decycling. We test our approach through the analysis of two common methods for community detection: modularity maximization with the Louvain algorithm and maximum likelihood optimization for fitting a stochastic block model. Our results highlight that applying community detection to the compressed network of super nodes is significantly faster while successfully producing partitions that are more aligned with the local network connectivity, more stable across multiple (stochastic) runs within and between community detection algorithms, and overlap well with the results obtained using the full network

Two betweenness centrality measures based on Randomized Shortest Paths

This paper introduces two new closely related betweenness centrality measures based on the Randomized Shortest Paths (RSP) framework, which fill a gap between traditional network centrality measures based on shortest paths and more recent methods considering random walks or current flows. The framework defines Boltzmann probability distributions over paths of the network which focus on the shortest paths, but also take into account longer paths depending on an inverse temperature parameter. RSP's have previously proven to be useful in defining distance measures on networks. In this work we study their utility in quantifying the importance of the nodes of a network. The proposed RSP betweenness centralities combine, in an optimal way, the ideas of using the shortest and purely random paths for analysing the roles of network nodes, avoiding issues involving these two paradigms. We present the derivations of these measures and how they can be computed in an efficient way. In addition, we show with real world examples the potential of the RSP betweenness centralities in identifying interesting nodes of a network that more traditional methods might fail to notice.Comment: Minor updates; published in Scientific Report

An integrated framework to classify healthcare virtual communities

Healthcare (HC) strives to improve service quality through its cost-effective social computing strategy. However, sudden rise in the count of virtual community of practices (VCoPs) introduced many choices for physicians; As a result, it is not surprising to observe current literature reporting lack of study to investigate ideas integration within and between VCoPs. VCoPs need to be categorized for HC physicians so they will be able to pin-point effective a VC to attain assistance from. This paper is one of the first investigative studies, in HC sector, that proposed a framework to classify and pin-point appropriate VCoPs, for physicians, after it reviewed and analyzed traditional and up-to-date theoretical, empirical and case study literature in the area of social computing, knowledge management (KM) and VCoPs. The implementation of this framework pinpointed professional VCoPs as most appropriate for physicians based on strict requirements, i.e. closed physician communities holding many participants, which are older than 5 years with high boundary crossing. This framework is also a “one-size-fit-all” formula to build an organizational VCoP, utilizable by other business sectors