Algorithmic approach to data model versioning

Accurate real-time prediction of surface ozone at high spatial and temporal resolution is critical for air-quality management, exposure assessment, and public health protection. However, developing an operational hourly ozone prediction system at the national scale remains challenging due to the need for continuous data acquisition, integration of heterogeneous data sources, and computationally efficient modeling frameworks. This study presents a real-time, data-driven framework for high-resolution hourly ozone prediction across South Korea by constructing an automated, nationwide atmospheric database that integrates satellite, meteorological, and ground-based observations.We establish an operational data pipeline that collects and processes near-real-time meteorological observations from the Korea Meteorological Administration’s Operational Data Assimilation and Model (ODAM), atmospheric information derived from the GK2A geostationary satellite, and surface ozone measurements from the AIRKOREA monitoring network. All data streams are ingested on an hourly basis and systematically harmonized through temporal synchronization and spatial alignment to generate high-resolution predictors covering the entire Korean Peninsula. This integrated database enables consistent representation of rapidly evolving meteorological conditions, atmospheric composition, and surface-level air quality.Using the constructed real-time database, we develop a data-driven prediction model to estimate hourly surface ozone concentrations, with AIRKOREA observations used as the target variable. The modeling framework is designed with operational feasibility in mind, supporting continuous updates, automated preprocessing, and near-real-time inference without reliance on computationally expensive chemical transport models. The resulting system provides high-resolution ozone predictions that capture fine-scale spatiotemporal variability at the national level.Model evaluation demonstrates that integrating geostationary satellite data with real-time meteorological and surface observations substantially enhances the prediction of hourly ozone variability compared to single-source or static-input approaches. The proposed framework highlights the advantages of real-time, high-resolution, and nationwide data integration for operational ozone forecasting in South Korea. Beyond ozone, this scalable and extensible system provides a foundation for real-time prediction of additional atmospheric pollutants and supports the development of next-generation data-driven air-quality forecasting services.

To improve the efficiency of clinical research, we developed a system to integrate electronic medical records (EMRs) and the electronic data capture system (EDC). EDC is divided into case report form (CRF) reporter and CDMS with CRF receiver with data communication using the operational data model (ODM). The CRF reporter is incorporated into the EMR to share data witth the EMR. In the data transcription type, doctors enter data using a progress note template, which are transmitted to the reporter template. It then generates the ODM. In the direct record type, reporter templates open from the progress note and generate narrative text to make record in the progress note. The configuration files for a study are delivered from the contents server to minimize the setup. This system has been used for 15 clinical studies including 3 clinical trials. This system can save labor and financial costs in clinical research.

Objectives The objective of this study is the conceptual design, implementation and evaluation of a system for generic, standard-compliant data transfer into electronic health records (EHRs). This includes patient data from clinical research and medical care that has been semantically annotated and enhanced with metadata. The implementation is based on the single-source approach. Technical and clinical feasibilities, as well as cost-benefit efficiency, were investigated in everyday clinical practice.Methods Münster University Hospital is a tertiary care hospital with 1,457 beds and 10,823 staff who treated 548,110 patients in 2018. Single-source metadata architecture transformation (SMA:T) was implemented as an extension to the EHR system. This architecture uses Model Driven Software Development (MDSD) to generate documentation forms according to the Clinical Data Interchange Standards Consortium (CDISC) operational data model (ODM). Clinical data are stored in ODM format in the EHR system database. Documentation forms are based on Google's Material Design Standard. SMA:T was used at a total of five clinics and one administrative department in the period from March 1, 2018 until March 31, 2019 in everyday clinical practice.Results The technical and clinical feasibility of SMA:T was demonstrated in the course of the study. Seventeen documentation forms including 373 data items were created with SMA:T. Those were created for 2,484 patients by 283 users in everyday clinical practice. A total of 121 documentation forms were examined retrospectively. The Constructive cost model (COCOMO II) was used to calculate cost and time savings. The form development mean time was reduced by 83.4% from 3,357 to 557 hours. Average costs per form went down from EUR 953 to 158.Conclusion Automated generic transfer of standard-compliant data and metadata into EHRs is technically and clinically feasible, cost efficient, and a useful method to establish comprehensive and semantically annotated clinical documentation. Savings of time and personnel resources are possible.

eSource for clinical trials: Implementation and evaluation of a standards-based approach in a real world trial

In business contexts, data from several sources are usually collected, integrated, stored and analyzed to support decision-making at the operational, tactical or strategic organizational levels. For these several levels, with different data needs, this paper proposes a Common Data Framework for identifying and modeling the different data repositories that support those data needs. The proposed approach considers all the business needs expressed in business processes models and that were useful in the identification of the operational data model, for on-line transaction processing, the analytical data model, for a data warehousing system, and on-line analytical processing cubes for a traditional Business Intelligence environment. Besides providing an integrated framework in which the data models complement each other, this approach also allows checking the conformity of the data stored in the different repositories, being possible to identify business requirements that were not properly modeled.

A bi-level object-oriented data model together with a user query language called OFQL is designed which can support applications like GIS (geographic information systems). The data model is divided into two layers, the higher-level data model and the lower-level data model. The higher-level data model or the geographic object data model primarily consists of the geographic objects and a set of semantic spatial functions through which the topological relationships of the geographic objects are defined or derived. The lower-level data model or the geometric object data model has geometric objects which are the actual spatial representations of the geographic objects in the higher-level data model. It also has a set of geometric functions that retrieve, manipulate, and compute for geometric objects. The general architecture of a GIS system using this data modeling approach consists of two modules: the query processor and the function implementor. With the OFQL user-interface, the user is able to pose queries of a geographic nature without knowing the details of the spatial representation and computation of the geographic objects.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

ABSTRACTThe data exchange standard identified as AP‐233, ‘systems engineering application protocol’ is to be established within the framework of ISO 10303, “STEP”, the Standard for the Technical Exchange of Product Data. The standard activity was initiated, and much initial technical work was pursued, by the European research project SEDRES. The work within AP‐233 has continued, building on the work of SEDRES, with a global industrial representation. This paper gives a status report on the AP‐233 activity. A follow‐on project SEDRES‐2, with expanded consortium, is well underway. The essence of the SEDRES‐2 mission statement is: to extend, validate and standardise the Systems Engineering (SE) data model and nurture its practical implementation and multi‐sector exploitation as a key enabler for industry. This paper explains the objectives, approach, time scales, and extended data scope of the SEDRES‐2 project, and gives a preview of early results from this next stage in achieving a comprehensive, workable systems engineering data exchange standard. Finally, it explains the significance of AP‐233 to your work, and what you can do to contribute to achieving the goal of a supported and adopted SE data standard.

Within the PREDIMED Clinical Data Warehouse (CDW) of Grenoble Alpes University Hospital (CHUGA), we have developed a hypergraph based operational data model, aiming at empowering physicians to explore, visualize and qualitatively analyze interactively the complex and massive information of the patients treated in CHUGA. This model constitutes a central target structure, expressed in a dual form, both graphical and formal, which gathers the concepts and their semantic relations into a hypergraph whose implementation can easily be manipulated by medical experts. The implementation is based on a property graph database linked to an interactive graphical interface allowing to navigate through the data and to interact in real time with a search engine, visualization and analysis tools. This model and its agile implementation allow for easy structural changes inherent to the evolution of techniques and practices in the health field. This flexibility provides adaptability to the evolution of interoperability standards.

One of the central problems in creating information management solutions for precision dairy farming is integration and interpretation of heterogeneous data coming from different equipment and data sources. Establishing a unifying data model is recognized as a cornerstone to such solutions. Here, the challenge lies both in agreeing on a common information context, and in selecting appropriate model representations, model query and update techniques, which guarantee model extensibility. While already existing ISOagriNET Data Dictionaries capture broad variety of the livestock and dairy farming concepts, the existing model representation technics cannot support efficient model extension. In this paper, we present our experience with using the representation and query standards and tools of the Semantic Web, endorsed by the World Wide Web Consortium (W3C), in particular the Resource Description Framework (RDF), the Web Ontology Language (OWL), and the SPARQL query standard, to encode and manipulate the dairy farming domain knowledge in a form of the Dairy Farming Ontology (DFO). Within the research project agriOpenLink the DFO has been created in two phases. The first phase focused on using the Semantic Web tools to facilitate easy encoding and manipulation of the dairy farming domain knowledge and the platform operational data models. The second phase focused on translating ISOagriNET Data Dictionaries into their semantic representations. Resulting DFO is maintained in a semantic repository, and it presents a knowledge graph and as integration backbone for the agriOpenLink decision support platform.

BackgroundWe determined whether it is feasible to identify important changes in care management resulting from cardiovascular magnetic resonance (CMR) in patients who activate the primary percutaneous coronary intervention (PPCI) pathway from hospital episode data, in order to construct a composite primary outcome (hypothesised to reduce the risk of major adverse cardiac-related events, MACE) to compare patients exposed to CMR or not.MethodsWe used Hospital Episode Statistics (HES) and Patient Episode Database for Wales (PEDW) to identify clinical events that reflected important changes in management in the year following the index admission in five subgroups of patients who activated the PPCI pathway recruited as part of a feasibility cohort study (n = 1655 with HES/PEDW data). For all subgroups, we identified frequency of events and time to the first event for each change in management.ResultsWe identified all clinical events (new diagnoses, additional diagnostic tests and procedures) except for medication prescriptions. Diagnostic tests were underestimated because most are carried out in outpatient clinics and outpatient datasets had missing procedure codes for 74% of patients (some tests done in hospital may also not be recorded). We successfully tabulated frequencies of events and distributions of times to first event for most changes in management by CMR status and in CMR / non CMR centres.ConclusionsIt is feasible to identify changes in care management between patients who have / do not have CMR within relevant patient subgroups. Further work to derive a weighting algorithm is required before attempting to combine the events in a composite endpoint.

On integrating database modeling constructs for software engineering databases

If change is constant, then change management is constant, but maintaining successful change management programs and strategies can be challenging, especially if the impact of the change is big or the time between the changes is small, or the frequency of changes is high. Organizational changes can be seen as mandatory route for the adaptation of new markets trends, client expectations or response to social and geopolitical situations and events that demand structural and radical changes for the organization to move on. However organizations are composed of humans and change management tends to be more related with human resource management than operations management. It is the degree of effective human involvement that defines and drives a successful change, even if this change has limited human input such as technological changes, supplier changes, etc.Over the years several change management theories have been evolved and adapted in the market. Some use more aggressive and X type or management practices, such as the Kotter’s model for change, while others use more democratic and Y type of management practices such as the ADKAR model for change. In either case, or in the ones that are more in the middle such as the Lewin’s forces for change, the participation of the employees needs to be secured with their willingness to engage constructively and not disruptively. Change is more likely to be adopted, and last longer, if intentionally or unintentionally is conceived as the right thing to do, with individual benefits. Therefore, the management objectives in a change management strategy can be directed under a new leadership type that integrates the democratic, participative, situational and transformational leadership theories.This paper presents a critical and comparative analysis between the Kotter’s Model for change, the ADKAR Model and the Lewin’s Forces for change. The result of this analysis highlights the leadership type that is primarily used and its distance from other the leadership types. It also indicates the switch of the management and leadership practices during the change management process, and the challenges related to that. The result of these analysis attempt to identify the degree of democracy used in the change management process which is essential for the effective and long lasting implementation of a change strategy. Democratic change programs can be implemented with less resistance regardless the impact of the change or the frequency they are executed. Therefore the paper identifies the change management model that is closer related to the Company Democracy Model used primarily for innovation based organizations strategies, and extends the application of this change management model to such type of organizations which are also characterized from their non-hierarchical structures and organizational cultures. These type of neo-liberal organizations are the most difficult to change as their degree or democracy and freedom to operate does not favour change management strategies driven mostly by logic, order and authority. For this a new leadership type is introduced that promotes co-opetitition instead of competition, an non-competitive collective effort to go through a change.The research conducted for this paper is based on an extensive literature review on change management theories, primary research with surveys and interviews but also analysis of case related studies to indicate the need for the critically analyse the two models studied and set the base for a new change management and leadership theory. Furthermore, the paper presents the pre and post-condition for adopting the new theory, highlights research limitations, and identifies areas of further research to be conducted for the optimization of the new theory and its contribution to the science of management and leadership.

BackgroundWe researched the feasibility of using routinely collected data to establish a registry to document the use of cardiac magnetic resonance imaging (CMR) in patients with suspected heart attack who...

Semantic and object-oriented data models, such as ER, OMT, IFO, and others, have been extensively used for modeling geographic applications. Despite their semantic expressiveness, such models present limitations to adequately model those applications, since they do not provide appropriate primitives for representing spatial data. This paper presents OMT-G, an object oriented data model for geographic applications. OMT-G provides primitives for modeling the geometry and the topology of spatial data, supporting different topological structures, multiple views of objects, and spatial relationships. OMT-G also includes tools to specify transformation processes and presentation alternatives, that allow, among many other possibilities, modeling for multiple representations and multiple presentations. In this way, it overcomes the main limitations of the existing models, thus providing more adequate tools for modeling geographic applications. A comparison with other data models is also presented in order to stress the main advantages of OMT-G.

The reliability and safety of distribution network operation have great significance for daily power supply security. In order to make full use of multi-source distribution network data, diagnose and evaluate the evaluation index of distribution network operation reliability, this paper proposes a long-short-term memory neural network and random matrix operation diagnosis method for distribution network based on the combination of physics and data model. The long-shortterm memory neural network and random matrix theory are analyzed first and the long-short-term memory neural network is used to train the node load data to obtain the prediction model. According to the definition of the multilayer physical index system, the system physical index is calculated. The description is established combined with the random matrix. Data indicators for overall operational posture. Principal component analysis is used to extract the main physical indicators and integrate them with the data indicators to obtain the diagnostic indicators for the distribution network operation assessment. The actual operation data proves that the proposed evaluation and diagnosis method can effectively reflect the running state of the urban distribution network, and has good multi-source data application capability and practicability.