72 research outputs found

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    Research Paper: Process Mining and Synthetic Health Data: Reflections and Lessons Learnt

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    Analysing the treatment pathways in real-world health data can provide valuable insight for clinicians and decision-makers. However, the procedures for acquiring real-world data for research can be restrictive, time-consuming and risks disclosing identifiable information. Synthetic data might enable representative analysis without direct access to sensitive data. In the first part of our paper, we propose an approach for grading synthetic data for process analysis based on its fidelity to relationships found in real-world data. In the second part, we apply our grading approach by assessing cancer patient pathways in a synthetic healthcare dataset (The Simulacrum provided by the English National Cancer Registration and Analysis Service) using process mining. Visualisations of the patient pathways within the synthetic data appear plausible, showing relationships between events confirmed in the underlying non-synthetic data. Data quality issues are also present within the synthetic data which reflect real-world problems and artefacts from the synthetic dataset’s creation. Process mining of synthetic data in healthcare is an emerging field with novel challenges. We conclude that researchers should be aware of the risks when extrapolating results produced from research on synthetic data to real-world scenarios and assess findings with analysts who are able to view the underlying data

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    Semantic data integration for supply chain management: with a specific focus on applications in the semiconductor industry

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    Supply Chain Management (SCM) is essential to monitor, control, and enhance the performance of SCs. Increasing globalization and diversity of Supply Chains (SC)s lead to complex SC structures, limited visibility among SC partners, and challenging collaboration caused by dispersed data silos. Digitalization is responsible for driving and transforming SCs of fundamental sectors such as the semiconductor industry. This is further accelerated due to the inevitable role that semiconductor products play in electronics, IoT, and security systems. Semiconductor SCM is unique as the SC operations exhibit special features, e.g., long production lead times and short product life. Hence, systematic SCM is required to establish information exchange, overcome inefficiency resulting from incompatibility, and adapt to industry-specific challenges. The Semantic Web is designed for linking data and establishing information exchange. Semantic models provide high-level descriptions of the domain that enable interoperability. Semantic data integration consolidates the heterogeneous data into meaningful and valuable information. The main goal of this thesis is to investigate Semantic Web Technologies (SWT) for SCM with a specific focus on applications in the semiconductor industry. As part of SCM, End-to-End SC modeling ensures visibility of SC partners and flows. Existing models are limited in the way they represent operational SC relationships beyond one-to-one structures. The scarcity of empirical data from multiple SC partners hinders the analysis of the impact of supply network partners on each other and the benchmarking of the overall SC performance. In our work, we investigate (i) how semantic models can be used to standardize and benchmark SCs. Moreover, in a volatile and unpredictable environment, SC experts require methodical and efficient approaches to integrate various data sources for informed decision-making regarding SC behavior. Thus, this work addresses (ii) how semantic data integration can help make SCs more efficient and resilient. Moreover, to secure a good position in a competitive market, semiconductor SCs strive to implement operational strategies to control demand variation, i.e., bullwhip, while maintaining sustainable relationships with customers. We examine (iii) how we can apply semantic technologies to specifically support semiconductor SCs. In this thesis, we provide semantic models that integrate, in a standardized way, SC processes, structure, and flows, ensuring both an elaborate understanding of the holistic SCs and including granular operational details. We demonstrate that these models enable the instantiation of a synthetic SC for benchmarking. We contribute with semantic data integration applications to enable interoperability and make SCs more efficient and resilient. Moreover, we leverage ontologies and KGs to implement customer-oriented bullwhip-taming strategies. We create semantic-based approaches intertwined with Artificial Intelligence (AI) algorithms to address semiconductor industry specifics and ensure operational excellence. The results prove that relying on semantic technologies contributes to achieving rigorous and systematic SCM. We deem that better standardization, simulation, benchmarking, and analysis, as elaborated in the contributions, will help master more complex SC scenarios. SCs stakeholders can increasingly understand the domain and thus are better equipped with effective control strategies to restrain disruption accelerators, such as the bullwhip effect. In essence, the proposed Sematic Web Technology-based strategies unlock the potential to increase the efficiency, resilience, and operational excellence of supply networks and the semiconductor SC in particular

    Process Mining Handbook

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    This is an open access book. This book comprises all the single courses given as part of the First Summer School on Process Mining, PMSS 2022, which was held in Aachen, Germany, during July 4-8, 2022. This volume contains 17 chapters organized into the following topical sections: Introduction; process discovery; conformance checking; data preprocessing; process enhancement and monitoring; assorted process mining topics; industrial perspective and applications; and closing

    Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

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    Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for future development and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications

    Yavaa: supporting data workflows from discovery to visualization

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    Recent years have witness an increasing number of data silos being opened up both within organizations and to the general public: Scientists publish their raw data as supplements to articles or even standalone artifacts to enable others to verify and extend their work. Governments pass laws to open up formerly protected data treasures to improve accountability and transparency as well as to enable new business ideas based on this public good. Even companies share structured information about their products and services to advertise their use and thus increase revenue. Exploiting this wealth of information holds many challenges for users, though. Oftentimes data is provided as tables whose sheer endless rows of daunting numbers are barely accessible. InfoVis can mitigate this gap. However, offered visualization options are generally very limited and next to no support is given in applying any of them. The same holds true for data wrangling. Only very few options to adjust the data to the current needs and barely any protection are in place to prevent even the most obvious mistakes. When it comes to data from multiple providers, the situation gets even bleaker. Only recently tools emerged to search for datasets across institutional borders reasonably. Easy-to-use ways to combine these datasets are still missing, though. Finally, results generally lack proper documentation of their provenance. So even the most compelling visualizations can be called into question when their coming about remains unclear. The foundations for a vivid exchange and exploitation of open data are set, but the barrier of entry remains relatively high, especially for non-expert users. This thesis aims to lower that barrier by providing tools and assistance, reducing the amount of prior experience and skills required. It covers the whole workflow ranging from identifying proper datasets, over possible transformations, up until the export of the result in the form of suitable visualizations

    28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

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    The 28th International Symposium on Temporal Representation and Reasoning (TIME 2021) was planned to take place in Klagenfurt, Austria, but had to move to an online conference due to the insecurities and restrictions caused by the pandemic. Since its frst edition in 1994, TIME Symposium is quite unique in the panorama of the scientifc conferences as its main goal is to bring together researchers from distinct research areas involving the management and representation of temporal data as well as the reasoning about temporal aspects of information. Moreover, TIME Symposium aims to bridge theoretical and applied research, as well as to serve as an interdisciplinary forum for exchange among researchers from the areas of artifcial intelligence, database management, logic and verifcation, and beyond

    Memorias del Congreso Argentino en Ciencias de la Computación - CACIC 2021

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    Trabajos presentados en el XXVII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de Salta los días 4 al 8 de octubre de 2021, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Universidad Nacional de Salta (UNSA).Red de Universidades con Carreras en Informátic
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