When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

An Initial Study of Practicing Psychologists\u27 Views of the Utility of Ecological Momentary Assessment for Difficult Psychotherapy Cases

Ecological momentary assessment (EMA) is a burgeoning area of research, and several clinical applications of the resulting data have been identified by researchers, suggesting potential benefit to psychotherapy practice. However, practitioners often do not use traditional empirically-supported tools for diagnosis and outcome monitoring (e.g., validated interview measures and questionnaires). Thus, it is not clear how readily practitioners will take up newer technology-enhanced assessment methods, despite current enthusiasm among researchers. The current study aimed to explore the perceived usefulness of EMA-based tools for clinical assessment and outcome monitoring of difficult psychotherapy cases, as well as to identify correlates of attitudes about the usefulness of these tools. Clinical psychologists in active therapy practice with adults (n = 375) completed an internet survey including the Attitudes toward Standardized Assessment scale and the Attitudes toward Standardized Assessment Scales-Monitoring and Feedback. Respondents characterized their current diagnostic and outcome monitoring practices and rated how helpful they would find several assessment and outcome monitoring resources for a difficult case, including both traditional instruments and EMA-based methods. EMA-based tools had lower perceived usefulness than existing instruments. Attitudes toward standardized assessment and outcome monitoring predicted the perceived utility of these methods, as did several professional variables. Practicing psychologists may not adopt EMA for clinical assessment more readily than traditional assessment tools. Recommendations for facilitating the uptake of new technologies by psychotherapists are offered

Machine Learning and Cognitive Ergonomics in Air Traffic Management: Recent Developments and Considerations for Certification

Resurgent interest in artificial intelligence (AI) techniques focused research attention on their application in aviation systems including air traffic management (ATM), air traffic flow management (ATFM), and unmanned aerial systems traffic management (UTM). By considering a novel cognitive human&ndash machine interface (HMI), configured via machine learning, we examined the requirements for such techniques to be deployed operationally in an ATM system, exploring aspects of vendor verification, regulatory certification, and end-user acceptance. We conclude that research into related fields such as explainable AI (XAI) and computer-aided verification needs to keep pace with applied AI research in order to close the research gaps that could hinder operational deployment. Furthermore, we postulate that the increasing levels of automation and autonomy introduced by AI techniques will eventually subject ATM systems to certification requirements, and we propose a means by which ground-based ATM systems can be accommodated into the existing certification framework for aviation systems. Document type: Articl

Streaming the Web: Reasoning over dynamic data.

In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates. Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation. The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning. © 2014 Elsevier B.V. All rights reserved

Developing Leading and Lagging Indicators to Enhance Equipment Reliability in a Lean System

With increasing complexity in equipment, the failure rates are becoming a critical metric due to the unplanned maintenance in a production environment. Unplanned maintenance in manufacturing process is created issues with downtimes and decreasing the reliability of equipment. Failures in equipment have resulted in the loss of revenue to organizations encouraging maintenance practitioners to analyze ways to change unplanned to planned maintenance. Efficient failure prediction models are being developed to learn about the failures in advance. With this information, failures predicted can reduce the downtimes in the system and improve the throughput. The goal of this thesis is to predict failure in centrifugal pumps using various machine learning models like random forest, stochastic gradient boosting, and extreme gradient boosting. For accurate prediction, historical sensor measurements were modified into leading and lagging indicators which explained the failure patterns in the equipment were developed. The best subset of indicators was selected by filtering using random forest and utilized in the developed model. Finally, the models give a probability of failure before the failure occurs. Appropriate evaluation metrics were used to obtain the accurate model. The proposed methodology was illustrated with two case studies: first, to the centrifugal pump asset performance data provided by Meridium, Inc. and second, the data collected from aircraft turbine engine provided in the NASA prognostics data repository. The automated methodology was shown to develop and identify appropriate failure leading and lagging indicators in both cases and facilitate machine learning model development

When things matter: A survey on data-centric Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

D8.6 OPTIMAI commercialization and exploitation strategy

Deliverable D8.6 OPTIMAI commercialization and exploitation strategy 1 st version is the first version of the OPTIMAI Exploitation Plan. Exploitation aims at ensuring that OPTIMAI becomes sustainable well after the conclusion of the research project period so as to create impact. OPTIMAI intends to develop an industry environment that will optimize production, reducing production line scrap and production time, as well as improving the quality of the products through the use of a variety of technological solutions, such as Smart Instrumentation of sensors network at the shop floor, Metrology, Artificial Intelligence (AI), Digital Twins, Blockchain, and Decision Support via Augmented Reality (AR) interfaces. The innovative aspects: Decision Support Framework for Timely Notifications, Secure and adaptive multi-sensorial network and fog computing framework, Blockchain-enabled ecosystem for securing data exchange, Intelligent Marketplace for AI sharing and scrap re-use, Digital Twin for Simulation and Forecasting, Embedded Cybersecurity for IoT services, On-the-fly reconfiguration of production equipment allows businesses to reconsider quality management to eliminate faults, increase productivity, and reduce scrap. The OPTIMAI exploitation strategy has been drafted and it consists of three phases: Initial Phase, Mid Phase and Final Phase where different activities are carried out. The aim of the Initial phase (M1 to M12), reported in this deliverable, is to have an initial results' definition for OPTIMAI and the setup of the structures to be used during the project lifecycle. In this phase, also each partner's Individual Exploitation commitments and intentions are drafted, and a first analysis of the joint exploitation strategies is being presented. The next steps, leveraging on the outcomes of the preliminary market analysis, will be to update the Key Exploitable Results with a focus on their market value and business potential and to consolidate the IPR Assessment and set up a concrete Exploitation Plan. The result of the next period of activities will be reported in D8.7 OPTIMAI commercialization and exploitation strategy - 2nd version due at month 18 (June 2022