Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. However, due to the directional nature and the line-of-sight demand of THz links, as well as the ultra-dense deployment of THz networks, a number of challenges that the medium access control (MAC) layer needs to face are created. In more detail, the need of rethinking user association and resource allocation strategies by incorporating artificial intelligence (AI) capable of providing "real-time" solutions in complex and frequently changing environments becomes evident. Moreover, to satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. Motivated by this, this article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management, while maximizing systems' reliability through blockage minimization. In more detail, a fast and centralized joint user association, radio resource allocation, and blockage avoidance by means of a novel metaheuristic-machine learning framework is documented, that maximizes the THz networks performance, while minimizing the association latency by approximately three orders of magnitude. To support, within the access point (AP) coverage area, mobility management and blockage avoidance, a deep reinforcement learning (DRL) approach for beam-selection is discussed. Finally, to support user mobility between coverage areas of neighbor APs, a proactive hand-over mechanism based on AI-assisted fast channel prediction is~reported.

We present a Maritime Situational Awareness (MSA) framework for detecting and forecasting maritime events (e.g., illegal fishing) over streams of Big maritime Data. The architecture of the MSA framework relies on the following state-of-the-art components: (i) the Maritime Event Detector which uses data-driven distributed techniques deployed on a computer cluster to detect maritime events of interest in an online, real-time fashion, (ii) the Complex Event Forecasting module, which implements state-of-the-art distributed Complex Event Forecasting techniques for maritime data, (iii) the Synopses Data Engine component, that creates synopses of maritime data improving the scalability of the framework and (iv) the streaming extension of a popular data science platform, namely RapidMiner Studio, that integrates all the above, allowing users to graphically design and rapidly implement Big Data analytics pipelines which can be deployed transparently on top of distributed architectures.

With the vision to transform the current wireless network into a cyber-physical intelligent platform capable of supporting bandwidth-hungry and latency-constrained applications, both academia and industry turned their attention to the development of artificial intelligence (AI) enabled terahertz (THz) wireless networks. In this article, we list the applications of THz wireless systems in the beyond fifth generation era and discuss their enabling technologies and fundamental challenges that can be formulated as AI problems. These problems are related to physical, medium/multiple access control, radio resource management, network and transport layer. For each of them, we report the AI approaches, which have been recognized as possible solutions in the technical literature, emphasizing their principles and limitations. Finally, we provide an insightful discussion concerning research gaps and possible future directions.

We present INforE, a prototype supporting non-expert programmers in performing optimized, cross-platform, streaming analytics at scale. INforE offers: a) a new extension to the RapidMiner Studio for graphical design of Big streaming Data workflows, (b) a novel optimizer to instruct the execution of workflows across Big Data platforms and clusters, (c) a synopses data engine for interactivity at scale via the use of data summaries, (d) a distributed, online data mining and machine learning module. To our knowledge INforE is the first holistic approach in streaming settings. We demonstrate INforE in the fields of life science and financial data analysis.

We present INforE, a prototype supporting non-expert programmers in performing optimized, cross-platform, streaming analytics at scale. INforE offers: a) a new extension to the RapidMiner Studio for graphical design of Big streaming Data workflows, (b) a novel optimizer to instruct the execution of workflows across Big Data platforms and clusters, (c) a synopses data engine for interactivity at scale via the use of data summaries, (d) a distributed, online data mining and machine learning module. To our knowledge INforE is the first holistic approach in streaming settings. We demonstrate INforE in the fields of life science and financial data analysis.

With thousands of data sources available on the Web as well as within organizations, data scientists increasingly spend more time searching for data than analyzing it. In order to ease the task of finding relevant data for data mining projects, this paper presents two data discovery and data integration methods that have been developed in a joint research project by RapidMiner Research and the University of Mannheim. Given a corpus of relational tables, the methods extend a query table with additional attributes and automatically fill these new attributes with data values from the corpus. The first method, densitybased table extension, extends the query table with all attributes that can be filled with data values so that a user-specified density threshold is reached. The second method, correlation-based table extension, extends the query table with all attributes that correlate with a specific attribute of the query table. Both methods are integrated as operators into RapidMiner Studio, a popular data mining environment. This enables data scientists to search for data and apply a wide range of different mining methods to the discovered data within the same environment.

Predicting subsequent values of quality of service (QoS) properties is a key component of autonomic solutions. Predictions help in the management of cloud-based applications by preventing QoS breaches from happening. The huge amount of monitoring data generated by cloud platforms motivated the applicability of scalable data mining and machine learning techniques for predicting performance anomalies. Building prediction models individually for thousands of virtual machines (VMs) requires a robust generic methodology with minimal human intervention. In this work, we focus on these issues and present three main contributions. First, we compare several time series modelling approaches to evidence the predictive capabilities of these approaches. Second, we propose estimation-classification models that augment the predictive capabilities of machine learning classification methods (random forest, decision tree, and support vector machine) by combining them with time series analysis methods (AR, ARIMA and ETS). Third, we show how the data mining techniques in conjunction with Hadoop framework can be a useful, practical, and inexpensive method for predicting QoS attributes.

Es gibt weltweit einen erhöhten Bedarf an Stahl, aber die Stahlherstellung ist ein enorm anspruchsvoller und kostenintensiver Prozess, bei dem gute Qualität schwer zu erreichen ist. Die Verbesserung der Qualität ist noch immer die größte Herausforderung, der sich die Stahlbranche gegenüber sieht. Das EUProjekt PRESED (Predictive Sensor Data Mining for Product Quality Improvement) [Vorrausschauende Sensordatengewinnung zur Verbesserung der Produktqualität] stellt sich dieser Herausforderung durch die Fokussierung auf weitverbreitete, wiederkehrende Probleme. Die Vielfalt und Richtigkeit der Daten sowie die Veränderung der Eigenschaften des untersuchten Materials erschwert die Interpretation der Daten. In dieser Abhandlung stellen wir die Referenzarchitektur von PRESED vor, die speziell angefertigt wurde, um die zentralen Anliegen der Verwaltung und Operationalisierung von Daten zu thematisieren. Die Architektur kombiniert große und intelligente Datenkonzepte mit Datengewinnungsalgorithmen. Datenvorverarbeitung und vorausschauende Analyseaufgaben werden durch ein plastisches Datenmodell unterstützt. Der Ansatz erlaubt es den Nutzern, Prozesse zu gestalten und mehrere Algorithmen zu bewerten, die sich gezielt mit dem vorliegenden Problem befassen. Das Konzept umfasst die Sicherung und Nutzung vollständiger Produktionsdaten, anstatt sich auf aggregierte Werte zu verlassen. Erste Ergebnisse der Datenmodellierung zeigen, dass die detailgenaue Vorverarbeitung von Zeitreihendaten durch Merkmalserkennung und Prognosen im Vergleich zu traditionell verwendeter Aggregationsstatistik überlegene Erkenntnisse bietet.

Automated Service Level Agreements (SLAs) have been proposed for cloud services as contracts used to record the rights and obligations of service providers and their customers. Automation refers to the electronic formalized representation of SLAs and the management of their lifecycle by autonomous agents. Most recently, SLA automated management is becoming increasingly of importance. In previous work, we have elaborated a utility architecture that optimizes resource deployment according to business policies, as well as a mechanism for optimization in SLA negotiation. We take all that a step further with the application of actor systems as an appropriate theoretical model for fine-grained, yet simplified and practical, monitoring of massive sets of SLAs. We show that this is a realistic approach for the automated management of the complete SLA lifecycle, including negotiation and provisioning, but focus on monitoring as the driver of contemporary scalability requirements. Our proposed work separates the agreement’s fault-tolerance concerns and strategies into multiple autonomous layers that can be hierarchically combined into an intuitive, parallelized, effective and efficient management structure.