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Building on virtualization and programmability, 5G networks aim for concurrent support of application domains with different functional and QoS requirements, both across and within vertical domains. Towards these requirements, network slicing mechanisms allow the management and orchestration of the underlying pool of resources, typically within a single administrative domain. However, in several occasions, verticals are expected to have a large geographical footprint, often crossing the administrative borders of multiple network domains, placing a subsequent functional requirement for cross-domain orchestration. In this paper we describe our approach on cross-domain slicing operations for the case of Industrial Applications with strict and flexible QoS requirements, with a particular focus on Wind Power plant networks. We describe the design of our SDN-based orchestration TRL-7 prototype and further provide a detailed look on the testbed prepared for measurements in an operational Wind Power plant in Brande, Denmark.

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The complexity of Business Intelligence activities has driven the proposal of several approaches for the effective modeling of Extract-Transform-Load (ETL) processes, based on the conceptual abstraction of their operations. Apart from fostering automation and maintainability, such modeling also provides the building blocks to identify and represent frequently recurring patterns. Despite some existing work on classifying ETL components and functionality archetypes, the issue of systematically mining such patterns and their connection to quality attributes such as performance has not yet been addressed. In this work, we propose a methodology for the identification of ETL structural patterns. We logically model the ETL workflows using labeled graphs and employ graph algorithms to identify candidate patterns and to recognize them on different workflows. We showcase our approach through a use case that is applied on implemented ETL processes from the TPC-DI specification and we present mined ETL patterns. Decomposing ETL processes to identified patterns, our approach provides a stepping stone for the automatic translation of ETL logical models to their conceptual representation and to generate fine-grained cost models at the granularity level of patterns.Peer ReviewedPostprint (author's final draft

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Obtaining the right set of data for evaluating the fulfillment of different quality factors in the extract-transform-load (ETL) process design is rather challenging. First, the real data might be out of reach due to different privacy constraints, while manually providing a synthetic set of data is known as a labor-intensive task that needs to take various combinations of process parameters into account. More importantly, having a single dataset usually does not represent the evolution of data throughout the complete process lifespan, hence missing the plethora of possible test cases. To facilitate such demanding task, in this paper we propose an automatic data generator (i.e., Bijoux). Starting from a given ETL process model, Bijoux extracts the semantics of data transformations, analyzes the constraints they imply over input data, and automatically generates testing datasets. Bijoux is highly modular and configurable to enable end-users to generate datasets for a variety of interesting test scenarios (e.g., evaluating specific parts of an input ETL process design, with different input dataset sizes, different distributions of data, and different operation selectivities). We have developed a running prototype that implements the functionality of our data generation framework and here we report our experimental findings showing the effectiveness and scalability of our approach.

Internet of Things-aware process execution imposes new requirements on process modeling that are outside the scope of current modeling languages. Internet of Things (IoT) devices may vanish, appear or stay unknown during process execution, which renders process resource allocation at design time infeasible. Devices’ capabilities are often only available in a particular real-world context at runtime. This is not considered by current approaches that use services for encapsulating device functionality. We propose a novel approach to enable both service discovery and invocation for IoT-aware processes based on users’ goals that are defined as part of a process. We apply the Tropos goal modeling methodology to represent the dependencies between these goals and IoT device capabilities. Furthermore, we present a Semantic Access Layer (SAL) to transform these goals into service invocations using generated SPARQL queries. The SAL executes the queries on a knowledge base representing runtime domain knowledge about IoT services, their capabilities, and context. As a result, it invokes the identified IoT services and transfers the responses back to the process engine. The evaluation of our approach within several Smart Home scenarios shows an increase of flexibility and separation of concerns for scalable, IoT-aware process execution.