Ekaterina Olshannikova

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

AbstractThis paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.

Conceptualizing Big Social Data

The popularity of social media and computer-mediated communication has resulted in high-volume and highly semantic data about digital social interactions. This constantly accumulating data has been termed as Big Social Data or Social Big Data, and various visions about how to utilize that have been presented. However, as relatively new concepts, there are no solid and commonly agreed definitions of them. We argue that the emerging research field around these concepts would benefit from understanding about the very substance of the concept and the different viewpoints to it. With our review of earlier research, we highlight various perspectives to this multi-disciplinary field and point out conceptual gaps, the diversity of perspectives and lack of consensus in what Big Social Data means. Based on detailed analysis of related work and earlier conceptualizations, we propose a synthesized definition of the term, as well as outline the types of data that Big Social Data covers. With this, we aim to foster future research activities around this intriguing, yet untapped type of Big Data.

Toward trusted, social-aware D2D connectivity: bridging across the technology and sociality realms

Driven by the unprecedented increase of mobile data traffic, D2D communications technology is rapidly moving into the mainstream of the 5G networking landscape. While D2D connectivity originally emerged as a technology enabler for public safety services, it is likely to remain at the heart of the 5G ecosystem by spawning a wide diversity of proximate applications and services. In this work, we argue that the widespread adoption of the direct communications paradigm is unlikely without embracing the concepts of trust and social-aware cooperation between end users and network operators. However, such adoption remains conditional on identifying adequate incentives that engage humans and their connected devices in a plethora of collective activities. To this end, the mission of our research is to advance the vision of social-aware and trusted D2D connectivity, as well as to facilitate its further adoption. We begin by reviewing the various types of underlying incentives with the emphasis on sociality and trust, discuss these factors specifically for humans and for networked devices (machines), and also propose a novel framework allowing construction of much needed incentive-aware D2D applications. Our supportive system-level performance evaluations suggest that trusted and social-aware direct connectivity has the potential to decisively augment network performance. We conclude by outlining the future perspectives of its development across the research and standardization sectors.

Towards Big Data Visualization for Augmented Reality

This article attempts to summarize challenges in visualization methods for existing Big Data and beyond. Moreover, it classifies types of data, analytical methods, visualization techniques and tools, known up to date, with a particular emphasis placed on surveying evolution of visualization techniques over the past years. Following the identified shortcomings of the existing techniques, the role of human eyes physical limitations on perception of the large amounts of information is evaluated and presented along with discussion on Augmented Realty technology and its capabilities for visualization of Big Data and beyond. Based on the results, a novel approach is proposed -- it allows one to obtain represented information segment in a short period of time without any significant data losses by placing the most essential data in the central area of the human eye visual field. This article also discusses impact of new technologies, such as Augmented Reality displays and helmets, on the Big Data visualization.

Using genetic algorithm for advanced municipal waste collection in Smart City

The Internet of Things (IoT), as expected infrastructure for envisioned concept of Smart City, brings new possibilities for the city management. IoT vision introduces promising and economical solutions for massive data collection and its analysis which can be applied in many domains and so make them operating more efficiently. In this paper, we are discussing one of the most challenging issues - municipal waste-collection within the Smart City. To optimize the logistic procedure of waste collection, we use own genetic algorithm implementation. The presented solution provides calculation of more efficient garbage-truck routes. As an output, we provide a set of simulations focused on mentioned area. All our algorithms are implemented within the integrated simulation framework which is developed as an open source solution with respect to future modifications.

Next2You: a proximity-based social application aiming to encourage interaction between nearby people

This paper presents the design and concept evaluation of Next2You, a proximity-based social mobile application that uses gamification, progressive disclosure and light-weight interactions to encourage interaction between people who are regularly within a close proximity of each other. The application aims to break the current norm of matching and introducing people based on similar interests or commonalities. We conducted focus groups to evaluate the application concept. We report findings of the user study contributing to the understanding of the potential and challenges of gamified proximity-based social applications.

Dynamic Social Trust Associations over D2D Communications: An Implementation Perspective

Network-assisted device-to-device (D2D) connectivity is a next-generation wireless technology that facilitates direct user contacts in physical proximity while taking advantage of the flexible and ubiquitous control coming from the cellular infrastructure. This novel type of user interactions creates challenges in constructing meaningful proximity-based applications and services that would enjoy high levels of user adoption. Accordingly, to enable such adoption a comprehensive understanding of user sociality and trust factors is required together with respective technology enablers for secure D2D communications, especially when cellular control is not available at all times. In this paper, we study an important aspect of secure communications over proximity-based direct links, with a primary emphasis on developing the corresponding proof-of-concept implementation. Our developed prototype offers rich functionality for dynamic management of security functions in proximate devices, whenever a new device joins the secure group of users or an existing one leaves it. To evaluate the behavior of our implemented application, we characterize its performance in terms of computation and transmission delays from the user perspective.

Visualizing Big Data

This chapter provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.

Dynamic Trust Associations Over Socially-Aware D2D Technology: A Practical Implementation Perspective

Today, direct contacts between users are being facilitated by the network-assisted device-to-device (D2D) technology, which employs the omnipresent cellular infrastructure for the purposes of control to facilitate advanced mobile social applications. Together with its undisputed benefits, this novel type of connectivity creates new challenges in constructing meaningful proximity-based services with high levels of user adoption. They call for a comprehensive investigation of user sociality and trust factors jointly with the appropriate technology enablers for secure and trusted D2D communications, especially in the situations where cellular control is not available or reliable at all times. In this paper, we study the crucial aspects of social trust associations over proximity-based direct communications technology, with a primary focus on developing a comprehensive proof-of-concept implementation. Our recently developed prototype delivers rich functionality for dynamic management of security functions in proximate devices, whenever a new device joins a secure group of users or an existing one leaves it. To characterize the behavior of our implemented demonstrator, we evaluate its practical performance in terms of computation and transmission delays from the user perspective. In addition, we outline a research roadmap leveraging our technology-related findings to construct a holistic user perspective behind dynamic, social-aware, and trusted D2D applications and services.

Collaborative Video Challenges: A Playful Concept of Proximity-Based Social Interaction

Mobile proximity-based networking technologies like Wi-Fi Direct enable applications that allow interaction between co-located users and device-to-device transfer of large amounts of data. To explore new ways of utilizing such enablers, we designed a concept that allows users to create collaborative video challenges, and further spread them device-to-device via Wi-Fi direct. The concept offers means for proximity-based playful social interaction, mediated by collaboratively created stories consisting of several clips. We present a preliminary user study to gather early feedback on the concept and to identify main hindrances. Our analysis shows promising results: the concept is considered as fun and playful, and there is high interest to follow how the challenges proceed. We discuss various opportunities and challenges as well as present ideas for further research.