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The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.

This paper aims to discuss large‐scale operational aspects for network service maps and to present technical solutions and evaluation results.

The paper reviews related work on service discovery specifically for wireless networks and services in, introduces network service maps and their operation in detail and describes their implementation and addresses two specific usage scenarios, based upon which it evaluates its approach through measurements and simulations.

The paper finds that, in the presence of today's ever‐increasing WLAN services, better support for service selection and automated service configuration is an urgent need to enable people to access these new services efficiently and thus increase their value and utilisation further. This is clearly reflected in different solutions being developed by service providers, roaming operators and device manufacturers that aim to facilitate WLAN usage.

The ubiquity of WLAN services, ranging from campus networks to commercial hotspots and community WLAN services, requires a new approach for automatically locating and using services. The paper presents a service selection infrastructure that is independent of a user's current network attachment and location in a way that users can obtain information of a specific network (or any other service) without being required to be connected to a particular one. Information about networks and services are distributed in a way that allows for using this information offline, e.g. when still looking for appropriate network access.

To provide an interesting approach for determining interval measures, through the analytic hierarchy process, for integration with social network analysis for knowledge mapping in organizations.

In order to develop improved organizational and business processes through knowledge management, a knowledge audit should be conducted to better understand the knowledge flows in the organization. An important technique to visualize these knowledge flows is the use of a knowledge map. Social network analysis can be applied to develop this knowledge map. Interval measures should be used in the social network analysis in order to determine the strength of the connections between individuals or departments in the organization. This paper applies the analytic hierarchy process to develop these interval measures, and integrates the values within the social network analysis to produce a meaningful knowledge map.

The analytic hierarchy process, when coupled with social network analysis, can be a useful technique for developing interval measures for knowledge‐mapping purposes.

The analytic hierarchy process may become tedious and arduous for use in large social network maps. More research needs to be conducted in this area for scalability.

As social network analysis is gaining more prominence in the knowledge management community, the analytic hierarchy process may be able to provide more valuable measures to determine the strengths of relationships between actors than simply using ordinal numbers.

Coupling the analytic hierarchy process with social network analysis provides a novel approach for future knowledge‐mapping activities.

The purpose of this paper is to explore the impact of configuration on supply network capability. It was believed that a configuration perspective might provide new insights on the capability and performance of supply networks, a gap in the literature, and provide a basis for the development of tools to aid their analysis and design.

The methodology involved the development of a configuration definition and mapping approach extending established strategic and firm level constructs to the network operational level. The resulting tools were tested and refined in a series of case studies across a range of sectors and value chain models. Supply network capability assessments, from the perspective of the focal firm, were then compared with their configuration profiles.

The configuration mapping tools were found to give new insights into the structure of supply networks and allow comparisons to be made across sectors and business models through the use of consistent and quantitative methods and common presentation. They provide the foundations for linking configuration to capability and performance, and contribute to supply network design and development by highlighting the intrinsic capabilities associated with different configurations.

Although multiple case networks have been investigated, the configuration exemplars remain suggestive models. The research suggests that a re‐evaluation of operational process excellence models is needed, where the link between process maturity and performance may require a configuration context.

Advantages of particular configurations have been identified with implications for supply network development and industrial policy.

The paper seeks to develop established strategic management configuration concepts to the analysis and design of supply networks by providing a robust operational definition of supply network configuration and novel tools for their mapping and assessment.

The need for stakeholder theory has been widely highlighted in the literature to develop solid strategies for a large organization. However, there is still a lack of user-friendly visualization tools and no unique approach exists to identify and engage stakeholders. This paper aims to propose a general methodology to co-design the sustainability ecosystem at the local scale, to explore it and to assess the impact of a large organization within the identified ecosystem.

The methodology consists of two main processes: identifying an ontological map of the sustainability topics network and designing the local sustainability stakeholders ecosystem. Both processes are based on a nodes identification phase and a nodes prioritization phase. The identification phase was achieved by engaging 160 citizens, for the topics network and nearly 40 relevant stakeholders, for the stakeholders’ ecosystem, with a collaborative participatory mapping process. The prioritization phase was conducted because of three indicators, i.e. the closeness, the betweenness and the eigenvector centrality.

Betweenness centrality results to be the best indicator to assess the importance of a stakeholder with respect to the whole network, while eigenvector centrality highlights the quality of the already engaged stakeholders of an organization, as it mainly depends on the number of links of the first order neighbors. On the contrary, the closeness centrality, when applied to a small network, seems to be not appropriate to assess the centrality of a stakeholder.

This approach revealed some criticalities in the mapping process, as in the weighting link procedure. Further investigations are needed to generalize the approach to a dynamic one, to allow real-time mapping and to develop a robust interconnection among centrality degrees and the power, interest and legitimacy concept of stakeholder theory.

Obtained results for a case study, i.e. the position of the University of Turin Green Office within the City of Turin sustainability ecosystem, are discussed showing how social network analysis centrality degrees can be used to quantitatively assess the role of an organization within a stakeholders’ ecosystem.

Centrality analysis allows identifying emergent topics/stakeholders within a network of words/actors that, at a first sight, should not be considered by decision-makers and managers.

A new methodology for stakeholder identification and prioritization is proposed exploiting online data visualization tools, participatory mapping and social network analysis.

The purpose of this paper is to visualize the collaboration network among regions in library science (LS) in China. Using various methods and tools of social network analysis and geographical visualization, results were obtained, showing the structure and patterns of research collaborations in topological and geographical views, as well as the geographical distributions of contribution.

The sample includes all studies published in the top journal in library science in China from 2006 to 2015. First, co-occurrence data representing collaborations among regions was extracted from author affiliations. Second, the topological network of collaboration was generated by applying social network analysis tools and descriptive statistics, network indicators of the collaboration network and research communities were calculated. Third, the topological network was projected into a geographical map with corresponding coordinates and distances using geographical tools. Finally, the topological network maps and the geographical maps were produced for visualization.

The levels of contribution are very unbalanced between regions, and overall research collaboration is low. Beijing, Hubei and several other areas are central and significant regions in China; other regions are mostly connected with central ones through direct collaborations. Research collaborations in LS research in China are mostly distributed in the east and south of China, being centralized in the “Beijing–Hubei–Shanghai” triangle zone, as well as within the triangle’s extending zones. Finally, there are three distinct research communities that connect closely within themselves and loosely between them. The Beijing community is relatively centralized in geography, while other communities are scattered.

This study applied various methods and tools of social network analysis and geographical mapping analysis to reveal the collaboration structure and patterns among regions in LS research in China. Visualized maps in topological and geographical views help shed new light on research efforts.

The purpose of this study is to map and analyze sustainability activities and relationships at the seven Claremont Colleges and graduate institutions using social network analysis (SNA) to inform sustainability planning and programming.

Online surveys and interviews were conducted among faculty, staff and students, and a network map was created and analyzed using network statistics to identify network characteristics.

The mapped sustainability network has 291 one- and bi-directional connections but with substantial differences among institutions. Pomona and Pitzer colleges have the highest number of sustainability-related courses because of their popular Environmental Analysis programs. The two graduate schools and Scripps College are comparatively isolated. Scripps’ network is small but highly interconnected and resilient. Pomona’s network is extensive but concentrated on a single node. Several other key actors were identified based on the number of nodes extending from or connecting to them. Several new sustainability initiatives were recently launched in response to the study.

SNA and mapping for campus sustainability can highlight network gaps and network vulnerabilities. To increase completeness, a representative and sufficiently large data sample is needed, requiring multiple, coordinated forms of contact. Interviews yield more detailed and comprehensive information than online surveys but are more time-consuming. Thus, the combination of electronic surveys and in-person interviews can be a successful strategy for maximizing information collection.

The case study was the first of its kind conducted at the Claremont Colleges and one of the first in higher education. It informs sustainability planning, coordination and integration efforts.

The implications of Manufacturing Automation Protocol (MAP) are examined from the perspective of the production manager. Due to the size of the financial investment required and the demands on technical resources of a MAP implementation, it is likely that the decision to employ MAP technology will be a corporate one. Under the heading of strategy, the integration of MAP into corporate and manufacturing strategies will be investigated. Financial justification of the capital investment will also be assessed as will the technical skills required to install a MAP‐based Computer Integrated Manufacturing (CIM) facility. The present position and possible future development of MAP and how the production manager may have to adjust to it will be addressed, from which, finally, conclusions will be drawn.

This article discusses how certain sensibilities and techniques from a network perspective can facilitate different levels of thinking about symbolic interaction in mediated contexts. The concept of network implies emergent structures that shift along with the people whose connections construct these webs of significance. A network sensibility resonates with contemporary social media contexts in that it focuses less on discrete objects and more on the entanglements among elements that may create meaning. From a methodological stance, this involves greater sensitivity to movement and connection, both in the phenomenon and in the researcher’s relationship to this flow. The goal is to embody the perspective of moving with and through the data, rather than standing outside it as if it can be observed, captured, isolated, and scrutinized outside the flow. Rather than reducing the scope, the practice of moving through and analyzing various elements of networks generates more data, more directions, and more layers of meaning. We describe various ways a network sensibility might engender more creative and ethically grounded approaches to studying contemporary cultures of information flow.

This paper aims to describe the popular trends and methods and ICT tools used for mapping and visualization of scientific domains as a research methodology which is attracting more and more interest from scientific information and science studies professionals. Science mapping or bibliometric mapping is a spatial representation of how disciplines, fields, specialties and individual documents or authors. The researchers analysed Bibexel, Pajek, VOSViewer, programmes used for processing and visualization of bibliographic and bibliometric data, within the framework of the implementation of IRNet research network project and activities and presented several examples of visualisation.

Among the main indicators of research effectiveness – bibliometric indicators – is a powerful information tool to support the development of science. Practical research methods were prepared using the programs: Bibexel (metadata analyses), program Pajek: graph editing and visualization of the graph structure of co-authorship – using the method of Kamada-Kawai and VOSviewer: a computer program for creating maps based on network data and for visualizing and exploring these maps. The main features of VOSviewer can be summarized for creating maps based on network data, visualizing and exploring maps.

The cited references and keywords are used to analyse in particular the intellectual base used by the research field or to analyse documents that cite the same references.

The authors of the paper, who are researchers of the European IRNet project and international research network, described and analysed certain bibliographic results of these activities using methods of mapping and visualization of scientific domains.