164 research outputs found

    Measuring the impact of COVID-19 on hospital care pathways

    Get PDF
    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

    Toward relevant answers to queries on incomplete databases

    Get PDF
    Incomplete and uncertain information is ubiquitous in database management applications. However, the techniques specifically developed to handle incomplete data are not sufficient. Even the evaluation of SQL queries on databases containing NULL values remains a challenge after 40 years. There is no consensus on what an answer to a query on an incomplete database should be, and the existing notions often have limited applicability. One of the most prevalent techniques in the literature is based on finding answers that are certainly true, independently of how missing values are interpreted. However, this notion has yielded several conflicting formal definitions for certain answers. Based on the fact that incomplete data can be enriched by some additional knowledge, we designed a notion able to unify and explain the different definitions for certain answers. Moreover, the knowledge-preserving certain answers notion is able to provide the first well-founded definition of certain answers for the relational bag data model and value-inventing queries, addressing some key limitations of previous approaches. However, it doesn’t provide any guarantee about the relevancy of the answers it captures. To understand what would be relevant answers to queries on incomplete databases, we designed and conducted a survey on the everyday usage of NULL values among database users. One of the findings from this socio-technical study is that even when users agree on the possible interpretation of NULL values, they may not agree on what a satisfactory query answer is. Therefore, to be relevant, query evaluation on incomplete databases must account for users’ tasks and preferences. We model users’ preferences and tasks with the notion of regret. The regret function captures the task-dependent loss a user endures when he considers a database as ground truth instead of another. Thanks to this notion, we designed the first framework able to provide a score accounting for the risk associated with query answers. It allows us to define the risk-minimizing answers to queries on incomplete databases. We show that for some regret functions, regret-minimizing answers coincide with certain answers. Moreover, as the notion is more agile, it can capture more nuanced answers and more interpretations of incompleteness. A different approach to improve the relevancy of an answer is to explain its provenance. We propose to partition the incompleteness into sources and measure their respective contribution to the risk of answer. As a first milestone, we study several models to predict the evolution of the risk when we clean a source of incompleteness. We implemented the framework, and it exhibits promising results on relational databases and queries with aggregate and grouping operations. Indeed, the model allows us to infer the risk reduction obtained by cleaning an attribute. Finally, by considering a game theoretical approach, the model can provide an explanation for answers based on the contribution of each attributes to the risk

    Graafitietokantojen kyselykielet ja GQL-standardi

    Get PDF
    Graafitietokantojen kyselykielet ovat kieliä, joilla voidaan suorittaa kyselyitä graafitietokantoihin. Graafitietokantojen kyselykieliä on vertailtu toisiinsa aiemminkin, mutta vertailu on toteutettu tyypillisesti vain muutamalle eri kyselykielelle. Tässä tutkielmassa vertaillaan kuutta eri kyselykieltä. Tämän lisäksi kyselykieliä vertaillaan arvion mukaan vuonna 2024 julkaistavaan ominaisuusgraafien GQL-kyselykielistandardiin. Tavoitteena on saada selville, millaisia graafitietokantojen kyselykieliä on olemassa sekä mitä eroja ja yhtäläisyyksiä on eri kyselykielten välillä. Lisäksi halutaan selvittää, millainen tuleva kyselykielistandardi GQL on ja kuinka nykyiset kyselykielet eroavat siitä ominaisuuksiltaan ja syntaksiltaan. Tutkielmassa vertailtavat kyselykielet ovat Cypher, PGQL, GSQL, Gremlin, SPARQL ja G-CORE. Tietoa kyselykielten ominaisuuksista ja syntaksista haettiin jokaisen kyselykielen virallisesta dokumentaatiosivustosta tai kyselykielestä kirjoitetuista tieteellisistä artikkeleista. Koska aineisto koostui pääosin dokumentaatioista, määritettiin tutkielman menetelmäksi dokumentaatioon perustuva katsaus. Tutkielmassa on yhteisiä piirteitä systemaattisen kirjallisuuskatsauksen kanssa. Tutkielman tuloksena saatiin selville, että graafitietokantojen kyselykielten ominaisuuksissa ja syntakseissa on eroja, mutta samat ydinominaisuudet löytyvät niistä kaikista. Kyselykielistandardi GQL:n ominaisuuksissa ja syntaksissa on vaikutteita jo olemassa olevista kyselykielistä, mutta se tulee sisältämään myös uusia ja uniikkeja ominaisuuksia

    Технология комплексной поддержки жизненного цикла семантически совместимых интеллектуальных компьютерных систем нового поколения

    Get PDF
    В издании представлено описание текущей версии открытой технологии онтологического проектирования, производства и эксплуатации семантически совместимых гибридных интеллектуальных компьютерных систем (Технологии OSTIS). Предложена стандартизация интеллектуальных компьютерных систем, а также стандартизация методов и средств их проектирования, что является важнейшим фактором, обеспечивающим семантическую совместимость интеллектуальных компьютерных систем и их компонентов, что существенное снижение трудоемкости разработки таких систем. Книга предназначена всем, кто интересуется проблемами искусственного интеллекта, а также специалистам в области интеллектуальных компьютерных систем и инженерии знаний. Может быть использована студентами, магистрантами и аспирантами специальности «Искусственный интеллект». Табл. 8. Ил. 223. Библиогр.: 665 назв

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

    Get PDF
    No abstract available

    Improving data preparation for the application of process mining

    Get PDF
    Immersed in what is already known as the fourth industrial revolution, automation and data exchange are taking on a particularly relevant role in complex environments, such as industrial manufacturing environments or logistics. This digitisation and transition to the Industry 4.0 paradigm is causing experts to start analysing business processes from other perspectives. Consequently, where management and business intelligence used to dominate, process mining appears as a link, trying to build a bridge between both disciplines to unite and improve them. This new perspective on process analysis helps to improve strategic decision making and competitive capabilities. Process mining brings together data and process perspectives in a single discipline that covers the entire spectrum of process management. Through process mining, and based on observations of their actual operations, organisations can understand the state of their operations, detect deviations, and improve their performance based on what they observe. In this way, process mining is an ally, occupying a large part of current academic and industrial research. However, although this discipline is receiving more and more attention, it presents severe application problems when it is implemented in real environments. The variety of input data in terms of form, content, semantics, and levels of abstraction makes the execution of process mining tasks in industry an iterative, tedious, and manual process, requiring multidisciplinary experts with extensive knowledge of the domain, process management, and data processing. Currently, although there are numerous academic proposals, there are no industrial solutions capable of automating these tasks. For this reason, in this thesis by compendium we address the problem of improving business processes in complex environments thanks to the study of the state-of-the-art and a set of proposals that improve relevant aspects in the life cycle of processes, from the creation of logs, log preparation, process quality assessment, and improvement of business processes. Firstly, for this thesis, a systematic study of the literature was carried out in order to gain an in-depth knowledge of the state-of-the-art in this field, as well as the different challenges faced by this discipline. This in-depth analysis has allowed us to detect a number of challenges that have not been addressed or received insufficient attention, of which three have been selected and presented as the objectives of this thesis. The first challenge is related to the assessment of the quality of input data, known as event logs, since the requeriment of the application of techniques for improving the event log must be based on the level of quality of the initial data, which is why this thesis presents a methodology and a set of metrics that support the expert in selecting which technique to apply to the data according to the quality estimation at each moment, another challenge obtained as a result of our analysis of the literature. Likewise, the use of a set of metrics to evaluate the quality of the resulting process models is also proposed, with the aim of assessing whether improvement in the quality of the input data has a direct impact on the final results. The second challenge identified is the need to improve the input data used in the analysis of business processes. As in any data-driven discipline, the quality of the results strongly depends on the quality of the input data, so the second challenge to be addressed is the improvement of the preparation of event logs. The contribution in this area is the application of natural language processing techniques to relabel activities from textual descriptions of process activities, as well as the application of clustering techniques to help simplify the results, generating more understandable models from a human point of view. Finally, the third challenge detected is related to the process optimisation, so we contribute with an approach for the optimisation of resources associated with business processes, which, through the inclusion of decision-making in the creation of flexible processes, enables significant cost reductions. Furthermore, all the proposals made in this thesis are validated and designed in collaboration with experts from different fields of industry and have been evaluated through real case studies in public and private projects in collaboration with the aeronautical industry and the logistics sector

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

    Get PDF
    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

    Approximation and Semantic Tree-Width of Conjunctive Regular Path Queries

    Get PDF
    corecore