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Background: Despite advances in treatments, 30% to 50% of stage III-IV head and neck squamous cell carcinoma (HNSCC) patients relapse within 2 years after treatment. The Big Data to Decide (BD2Decide) project aimed to build a database for prognostic prediction modeling. Methods: Stage III-IV HNSCC patients with locoregionally advanced HNSCC treated with curative intent (1537) were included. Whole transcriptomics and radiomics analyses were performed using pretreatment tumor samples and computed tomography/magnetic resonance imaging scans, respectively. Results: The entire cohort was composed of 71% male (1097)and 29% female (440): oral cavity (429, 28%), oropharynx (624, 41%), larynx (314, 20%), and hypopharynx (170, 11%); median follow-up 50.5 months. Transcriptomics and imaging data were available for 1284 (83%) and 1239 (80%) cases, respectively; 1047 (68%) patients shared both. Conclusions: This annotated database represents the HNSCC largest available repository and will enable to develop/validate a decision support system integrating multiscale data to explore through classical and machine learning models their prognostic role.

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This paper reports the work in progress towards the specification of a conceptual architecture of a smart system for supporting the management of disruptions in the manufacturing domain. In particular, it proposes an approach to the description of the system architecture based on a number of interrelated viewpoints following the pertinent ISO 42010 standard. The approach is being developed in the context of the EU-funded H2020 DISRUPT project aiming to deliver a comprehensive datadriven solution for automated vertical and horizontal integration facilitating the transition into smart manufacturing.

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The orchestration of smart manufacturing service operations and processes arises as a challenging step in the realization of the Industry 4.0 vision. This paper presents the work in progress towards the specifications of a controlling environment for data-driven orchestration of software services in future smart manufacturing scenarios. The paper discusses the role and significance of multi-aspect data in the management of manufacturing operations and proposes a reference architecture for controlling the orchestration of the respective data services, following the work that has been conducted in the context of the EU-funded project DISRUPT.

Data-driven decision making is at the core of Industry 4.0. This paper describes the specification of a conceptual architecture of a smart system for supporting decision making in the context of disruptive events in manufacturing operations. Following a viewpoint-oriented approach, the proposed architecture identifies the functional components that facilitate decision making and establishes the interfaces between them, demonstrates the information flow within the manufacturing ecosystem for vertical / horizontal integration and establishes the mapping of the functional components to different software containers, execution environments and physical devices.