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Many vehicles are connected to the Internet, and big data are continually created. Various studies have been conducted involving the development of artificial intelligence, machine learning technology, and big data frameworks. The analysis of smart mobility big data is essential and helps to address problems that arise as society faces increased future mobility. In this paper, we analyze application issues such as personal information leakage and data visualization due to increased data exchange in detail, as well as approaches focusing on analyses exploiting machine learning and architecture research exploiting big data frameworks, such as Apache Hadoop, Apache Spark, and Apache Kafka. Finally, future research directions and open challenges are discussed. [ABSTRACT FROM AUTHOR]

Everyone, knowingly or not, is in a position to generate data at all times in our lives. Mainly in the professional life, where the production of data is addressed and aimed at achieving a series of objectives, and in social and personal life where the production of data, direct and indirect, is equally important although not always clearly identifiable. Always and in any case, the representation of our actions in digital contents or the analysis of our behaviour as consumers, just to give two examples, are a common denominator of our relationship with the digital framework which has a direct impact at the level of relationship with companies or with public administrations. In the business, the most appropriate example today is that of Industry 4.0 which with the virtualization of the processes and products themselves is allowing companies to explore novel paradigms of innovation that were once unthinkable. [ABSTRACT FROM AUTHOR]

Social big data is currently an emergent issue, especially for recommender systems. In particular, with respect to social big data, various data mining techniques have been applied in group recommender systems. However, three social phenomena (i.e., social influence, emotional contagion, and conformity) have not been applied enough in existing studies. In this paper, a novel method for a group recommendation is proposed based upon the affective social network from social big data. In this regard, to explore and measure the social phenomena, a variety of similarity measures were applied in a content-based recommendation. Moreover, the proposed method has a computational complexity of O(N2), where N is the number of users in a group. Therefore, it is appropriate for big data environments, since the N is generally small for user groups. This study’s results revealed that the Mahalanobis distance was suitable for the affective group recommendation. Moreover, the proposed method outperformed the other group recommender systems, those with large groups. [ABSTRACT FROM AUTHOR]

The imbalanced data problem occurs when the number of representative instances for classes of interest is much lower than for other classes. The influence of imbalanced data on classification performance has been discussed in some previous research as a challenge to be studied. In this paper, we propose a method to solve the imbalanced data problem by focusing on preprocessing, including: i) sampling techniques (i.e., under-sampling, over-sampling, and hybrid-sampling) and ii) the instance weighting method to increase the number of features in minority classes and to reduce comprehensive coverage in majority classes. The experimental results show that the noisy data is reduced, making a smaller sized dataset, and training time decreases significantly. Moreover, distinct properties of each class are examined effectively. Refined data is used as input for Naive Bayes and support vector machine classifiers for the targets of the training process. The proposed methods are evaluated based on the number of non-geotagged resources that are labeled correctly with their geo-locations. In comparison with previous research, the proposed method achieves accuracy of 84%, whereas previous results were 75%. [ABSTRACT FROM AUTHOR]

Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media and big data paradigms. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including information dissemination, human emotion detection system and big data.

An introduction is presented in which the editor discusses the articles in the issue which includes big data technologies and its applications, especially in social networking, traffic flow management, and computing of multimodal data.

An introduction is presented in which the editor discusses various reports within the issue on topics including the 5th International Conference on Scalable Information Systems, big data, and the ranking of academic publications.

• The paper presents the definition and challenges of knowledge graph fusion. • Notable techniques for handling knowledge graph fusion have been reviewed. • Various applications of knowledge graph fusion have been introduced for smart systems. The emergence of various disruptive technologies such as big data, Internet of Things, and artificial intelligence have instigated our society to generate enormous volumes of data. The effective, efficient, and transparent capture and fusion of knowledge from a massive amount data is becoming an increasingly popular and crucial topic. In this study, we aim to provide a broad, complete, and systematic overview of the definitions and challenges of the knowledge graph fusion, which represents a holistic approach for integrating, enhancing, and unifying knowledge graphs. Further, advanced techniques for handling knowledge graph fusion along with the pragmatic smart systems leveraging it are discussed as a part of multiple perspectives. We believe that this survey study can be used as a potential reference for system practitioners and researchers in surpassing current obstacles as well as shaping their future direction. [ABSTRACT FROM AUTHOR]