Cross-organisation dataspace (COD) - architecture and implementation

With the rapid development of information and communication technologies, the need to share information to improve efficiency in large enterprises is also increasing rapidly. For a large enterprise the information can come from many different sources and in different formats. There is a real requirement to manage the vast amount and diverse sources of data in a convenient and integrated way so that repositories of information can be built up with little additional effort and the information can be easily accessed globally. This paper presents the design and implementation of a prototype, called COD (Cross- Organisation Dataspace), that addresses the above challenges. COD, in the context of an enterprise involving multiple organisations, allows users from different geographical locations to contribute information and to search and access information easily. The information can be contained in many different forms, e.g. text files, reports, drawings and databases

Cross-Organisation Dataspace (COD) - Architecture and Implementation

With the rapid development of information and communication technologies, the need to share information to improve efficiency in large enterprises is also increasing rapidly. For a large enterprise the information can come from many different sources and in different formats. There is a real requirement to manage the vast amount and diverse sources of data in a convenient and integrated way so that repositories of information can be built up with little additional effort and the information can be easily accessed globally. This paper presents the design and implementation of a prototype, called COD (Cross- Organisation Dataspace), that addresses the above challenges. COD, in the context of an enterprise involving multiple organisations, allows users from different geographical locations to contribute information and to search and access information easily. The information can be contained in many different forms, e.g. text files, reports, drawings and databases

Improved clinical outcomes in response to a 12-week blended digital and community-based long-COVID-19 rehabilitation programme

IntroductionTwo million people in the UK are experiencing long COVID (LC), which necessitates effective and scalable interventions to manage this condition. This study provides the first results from a scalable rehabilitation programme for participants presenting with LC.Methods601 adult participants with symptoms of LC completed the Nuffield Health COVID-19 Rehabilitation Programme between February 2021 and March 2022 and provided written informed consent for the inclusion of outcomes data in external publications. The 12-week programme included three exercise sessions per week consisting of aerobic and strength-based exercises, and stability and mobility activities. The first 6 weeks of the programme were conducted remotely, whereas the second 6 weeks incorporated face-to-face rehabilitation sessions in a community setting. A weekly telephone call with a rehabilitation specialist was also provided to support queries and advise on exercise selection, symptom management and emotional wellbeing.ResultsThe 12-week rehabilitation programme significantly improved Dyspnea-12 (D-12), Duke Activity Status Index (DASI), World Health Orginaisation-5 (WHO-5) and EQ-5D-5L utility scores (all p < 0.001), with the 95% confidence intervals (CI) for the improvement in each of these outcomes exceeding the minimum clinically important difference (MCID) for each measure (mean change [CI]: D-12: −3.4 [−3.9, −2.9]; DASI: 9.2 [8.2, 10.1]; WHO-5: 20.3 [18.6, 22.0]; EQ-5D-5L utility: 0.11 [0.10, 0.13]). Significant improvements exceeding the MCID were also observed for sit-to-stand test results (4.1 [3.5, 4.6]). On completion of the rehabilitation programme, participants also reported significantly fewer GP consultations (p < 0.001), sick days (p = 0.003) and outpatient visits (p = 0.007) during the previous 3 months compared with baseline.DiscussionThe blended and community design of this rehabilitation model makes it scalable and meets the urgent need for an effective intervention to support patients experiencing LC. This rehabilitation model is well placed to support the NHS (and other healthcare systems worldwide) in its aim of controlling the impacts of COVID-19 and delivering on its long-term plan.Clinical trial registrationhttps://www.isrctn.com/ISRCTN14707226, identifier 14707226

Query processing for data integration from multiple data sources over the Internet

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Research On Auto-Fluorescence Spectrogram For Colorectal Carcinoma With Data Mining

Data classification is an important data mining role in biomedicine. This paper proposes a method to analyze Colorectal Carcinoma Auto-Fluorescence Spectrogram data based on Counting KNN Algorithm after analyzing the characteristics of biomedicine data. Though Counting KNN Algorithm for classification is simple and effective, it doesn\u27t deal with biomedicine data well. After analyzing the algorithm performance, a novel Counting KNN algorithm by index tree is presented. Experiments show that this method outperforms the distance-based voting kNN, and C-kNN. More importantly it is a method that works for ordinal, nominal or mixed data. © 2007 IEEE