Geospatial data analysis in Russia’s geoweb

The chapter examines the role of geospatial data in Russia’s online ecosystem. Facilitated by the rise of geographic information systems and user-generated content, the distribution of geospatial data has blurred the line between physical spaces and their virtual representations. The chapter discusses different sources of these data available for Digital Russian Studies (e.g., social data and crowdsourced databases) together with the novel techniques for extracting geolocation from various data formats (e.g., textual documents and images). It also scrutinizes different ways of using these data, varying from mapping the spatial distribution of social and political phenomena to investigating the use of geotag data for cultural practices’ digitization to exploring the use of geoweb for narrating individual and collective identities online

Interconnectedness of complex systems of internet of things through social network analysis for disaster management

This visionary paper presents the Internet of Things paradigm in terms of interdependent dynamic dimensions of objects and their properties. Given that in its current state Internet of Things (IoT) has been viewed as a paradigm based on hierarchical distribution of objects, evaluation of the dynamic nature of the hierarchical structures faces challenges in its evaluation and analysis. Within this in mind, our focus is on the area of complex social networks and the dynamic social network construction within the context of IoT. This is by highlighting and addressing the tagging issues of the objects to the real-world domain such as in disaster management, these are in relation to their hierarchies and interrelation within the context of social network analysis. Specifically, we suggest to investigate and deepen the understanding of the IoT paradigm through the application of social network analysis as a method for interlinking objects -- and thus, propose ways in which IoT could be subsequently interlinked and analyzed through social network analysis approach - which provides possibilities for linking of the objects, while extends it into real-world domain. With this in mind, we present few applications and key characteristics of disaster management and the social networking analysis approach, as well as, foreseen benefits of its application in the IoT domain

Disaster management and profile modelling of IoT objects: conceptual parameters for interlinked objects in relation to social network analysis

In recent years, the emergence of ubiquitous and pervasive computing suggests the radical transformation of the Internet to incorporate physical objects. The transformation suggests the enabling of a new form of communication and interaction style that incorporates people, their smart devices and their physical objects through the utilization of distributed sensors spread in the environment. In this study, we propose extending Internet of Things (IoT) by modelling an IoT enabled smart environment as a whole, representing the dynamic communication and interaction among all objects (users, devices, physical objects and sensors). This is by recognizing and categorizing objects' properties in the form of a generic profile. We also reflect this in a disaster management context. That is by identifying which of the parameters of these smart objects are fixed, constant and persistent over time and which parameters are actually change over time, i.e. those characterized by their transient and dynamic nature. Thus, through the process of communication and interaction of the objects, we analyze parameters by demonstrating their static and/or dynamic properties as well as those supporting context-aware variables which are evident in disaster scenarios. To achieve these goals, we designed the persistent or temporal relationships to encompass internal information of smart-objects, along with their characteristics that actually depict their capacity to offer services to users by properties' matchmaking. The interlinked relationships represent a 'social network' providing a terrain of flexible scenarios that would lead to tailored parameters to fit user preferences. To enable communication among them in a dynamic dimension we utilized a distributed topology in which communication could occur indirectly between objects. Finally, we detailed a generic - but equally applicable for disaster management - case scenario in which we used graph theory to demonstrate how embedded intelligence to real-life objects will be able to assist the smart-resource pairing, thus improving resource discovery and harvesting process by taking into consideration user needs and preferences