Proof of Concept for a Visual Analytics Dashboard for Transportation Network Analysis

This paper discusses the latest developments in the field of visual analytics, and the role of network analysis for transportation systems. Multilayer and multiplex based visualizations are considered reliable solutions for handling the information overload the decision makers are facing in the addressed domain. The existing tools matching these requirements are briefly reviewed. Then, a proof of concept for a dashboard is presented focusing on a transportation network analysis with multiple network measures and indices in a multiplex visualization

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

Introductory Editorial

The Open Journal of Big Data is a new open access journal published by RonPub, and RonPub is an academic publisher of online, open access, peer-reviewed journals. OJBD addresses aspects of Big Data, including new methodologies, processes, case studies, poofs-of-concept, scientific demonstrations, industrial applications and adoption. This editorial presents the two articles in this first issue. The first paper is on An Efficient Approach for Cost Optimization of the Movement of Big Data, which mainly focuses on the challenge of moving big data from one data center to other.The second paper is on Cognitive Spam Recognition Using Hadoop and Multicast-Update, which describes a method to make machines cognitively label spam using Machine Learning and the Naive Bayesian approach. OJBD has a rising reputation thanks to the support of research communities, which help us set up the First International Conference on Internet of Things and Big Data 2016 (IoTBD 2016), in Rome, Italy, between 23 and 25 April 2016

Tackling food marketing to children in a digital world: trans-disciplinary perspectives. Children’s rights, evidence of impact, methodological challenges, regulatory options and policy implications for the WHO European Region

There is unequivocal evidence that childhood obesity is influenced by marketing of foods and non-alcoholic beverages high in saturated fat, salt and/or free sugars (HFSS), and a core recommendation of the WHO Commission on Ending Childhood Obesity is to reduce children’s exposure to all such marketing. As a result, WHO has called on Member States to introduce restrictions on marketing of HFSS foods to children, covering all media, including digital, and to close any regulatory loopholes. This publication provides up-to-date information on the marketing of foods and non-alcoholic beverages to children and the changes that have occurred in recent years, focusing in particular on the major shift to digital marketing. It examines trends in media use among children, marketing methods in the new digital media landscape and children’s engagement with such marketing. It also considers the impact on children and their ability to counter marketing as well as the implications for children’s rights and digital privacy. Finally the report discusses the policy implications and some of the recent policy action by WHO European Member States

Real-time big data processing for anomaly detection : a survey

The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Lt