527 research outputs found
Measuring the impact of COVID-19 on hospital care pathways
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
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
Semantic Data Management in Data Lakes
In recent years, data lakes emerged as away to manage large amounts of
heterogeneous data for modern data analytics. One way to prevent data lakes
from turning into inoperable data swamps is semantic data management. Some
approaches propose the linkage of metadata to knowledge graphs based on the
Linked Data principles to provide more meaning and semantics to the data in the
lake. Such a semantic layer may be utilized not only for data management but
also to tackle the problem of data integration from heterogeneous sources, in
order to make data access more expressive and interoperable. In this survey, we
review recent approaches with a specific focus on the application within data
lake systems and scalability to Big Data. We classify the approaches into (i)
basic semantic data management, (ii) semantic modeling approaches for enriching
metadata in data lakes, and (iii) methods for ontologybased data access. In
each category, we cover the main techniques and their background, and compare
latest research. Finally, we point out challenges for future work in this
research area, which needs a closer integration of Big Data and Semantic Web
technologies
A Survey on Mapping Semi-Structured Data and Graph Data to Relational Data
The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today's market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases' capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from eachmodel's mapping strategies, aswell as a newresearch topic - mapping multi-model data into relational tables.Peer reviewe
On Making in the Digital Humanities
On Making in the Digital Humanities fills a gap in our understanding of digital humanities projects and craft by exploring the processes of making as much as the products that arise from it.
The volume draws focus to the interwoven layers of human and technological textures that constitute digital humanities scholarship. To do this, it assembles a group of well-known, experienced and emerging scholars in the digital humanities to reflect on various forms of making (we privilege here the creative and applied side of the digital humanities). The volume honours the work of John Bradley, as it is totemic of a practice of making that is deeply informed by critical perspectives. A special chapter also honours the profound contributions that this volume’s co-editor, Stéfan Sinclair, made to the creative, applied and intellectual praxis of making and the digital humanities. Stéfan Sinclair passed away on 6 August 2020.
The chapters gathered here are individually important, but together provide a very human view on what it is to do the digital humanities, in the past, present and future. This book will accordingly be of interest to researchers, teachers and students of the digital humanities; creative humanities, including maker spaces and culture; information studies; the history of computing and technology; and the history of science and the humanities
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
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