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Traffic analysis is a compound of strategies intended to find relationships, patterns, anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic classification is a subgroup of strategies in this field that aims at identifying the application's name or type of Internet traffic. Nowadays, traffic classification has become a challenging task due to the rise of new technologies, such as traffic encryption and encapsulation, which decrease the performance of classical traffic classification strategies. Machine Learning gains interest as a new direction in this field, showing signs of future success, such as knowledge extraction from encrypted traffic, and more accurate Quality of Service management. Machine Learning is fast becoming a key tool to build traffic classification solutions in real network traffic scenarios; in this sense, the purpose of this investigation is to explore the elements that allow this technique to work in the traffic classification field. Therefore, a systematic review is introduced based on the steps to achieve traffic classification by using Machine Learning techniques. The main aim is to understand and to identify the procedures followed by the existing works to achieve their goals. As a result, this survey paper finds a set of trends derived from the analysis performed on this domain; in this manner, the authors expect to outline future directions for Machine Learning based traffic classification.

In recent years, a revolution named Industry 4.0 has arisen. Industry 4.0 is presented as the integration of new advances in areas such as Cyber-Physical Systems, the Internet of Things and Everything (IoE), Cloud computing, the Internet of Services, Big Data Analysis, Smart Factories, Augmented Reality, among others. Industry 4.0 is not only a new industrial revolution, but also a crucial integration challenge that involves several actors from the IoE, which are people, data, services, and things. This paper proposes an approach to analyze the integration challenges in the context of Industry 4.0 using five integration levels, which are connection, communication, coordination, cooperation, and collaboration (5 C). In that sense, this paper presents a state of the art of recent studies in Industry 4.0 from an integration perspective, categorized according to the 5 C integration levels versus the four actors of IoE. Specifically, this paper considers several works intended to solve problems of autonomic integration in Industry 4.0 at the highest levels of the 5 C integration stack (coordination, cooperation, and collaboration). Also, this paper presents a case study from an integration perspective, which contemplates autonomy, self-organizing, among other aspects, in order to turn a traditional industry into a smart factory regarding the Industry 4.0 concept.

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Advances supported by emerging wearable technologies in healthcare promise patients a provision of high quality of care. Wearable computing systems represent one of the most thrust areas used to transform traditional healthcare systems into active systems able to continuously monitor and control the patients' health in order to manage their care at an early stage. However, their proliferation creates challenges related to data management and integration. The diversity and variety of wearable data related to healthcare, their huge volume and their distribution make data processing and analytics more difficult. In this paper, we propose a generic semantic big data architecture based on the "Knowledge as a Service" approach to cope with heterogeneity and scalability challenges. Our main contribution focuses on enriching the NIST Big Data model with semantics in order to smartly understand the collected data, and generate more accurate and valuable information by correlating scattered medical data stemming from multiple wearable devices or/and from other distributed data sources. We have implemented and evaluated a Wearable KaaS platform to smartly manage heterogeneous data coming from wearable devices in order to assist the physicians in supervising the patient health evolution and keep the patient up-to-date about his/her status.