A Data Structure Model Supporting Railway Distributed System Integration

2011 ◽  
Author(s):  
Hanning Wang ◽  
Weixiang Xu ◽  
Chaolong Jia
Keyword(s):  
Data Integration ◽  
Distributed System ◽  
Spatial Data ◽  
Data Model ◽  
System Integration ◽  
Information Exchange ◽  
Data Models ◽  
Common Data Model ◽  
Data Set ◽  
Application Systems

Railway distributed system integration needs to realize information exchange, resources sharing and coordination process across fields, departments and application systems. And railway data integration is essential to implement this integration. In order to resolve the problem of heterogeneity of data models among data sources of different railway operation systems, this paper presents a novel integration data model of spatial structure, a XML-oriented 3-dimension common data model. The proposed model accommodates both the flexibility of level relationship and syntax expression in data integration. In this model, a spatial data pattern is used to describe and express the characteristic relationship of data items among all types of data. Based on the data model with rooted directed graph and the organization of level as well as the flexibility of the expression, the model can represent the mapping between different data models, including relationship model and object-oriented model. A consistent concept and algebraic description of the data set is given to function as the metadata in data integration, so that the algebraic manipulation of data integration is standardized to support the data integration of distributed system.

scholarly journals The Unification of the Code Lists Provided within the Data Model Originating from the Inspire Technical Guidelines and the Ones Provided for Gesut Databases in the Context of Potential Exploitation in the Mining Industry

2016 ◽  
Vol 23 (3) ◽  
pp. 178-182
Author(s):  
Andrzej Zygmuniak ◽  
Violetta Sokoła-Szewioła
Keyword(s):  
Spatial Data ◽  
Data Model ◽  
Spatial Databases ◽  
Mining Industry ◽  
Data Models ◽  
Research Projects ◽  
Mining Areas ◽  
European Parties ◽  
Potential Exploitation ◽  
The Way

Abstract This study is aimed at exposing differences between two data models in case of code lists values provided there. The first of them is an obligatory one for managing Geodesic Register of Utility Networks databases in Poland [9] and the second is the model originating from the Technical Guidelines issued to the INSPIRE Directive. Since the second one mentioned is the basis for managing spatial databases among European parties, correlating these two data models has an effect in easing the way of harmonizing and, in consequence, exchanging spatial data. Therefore, the study presents the possibilities of increasing compatibility between the values of the code lists concerning attributes for objects provided in both models. In practice, it could lead to an increase of the competitiveness of entities managing or processing such databases and to greater involvement in scientific or research projects when it comes to the mining industry. Moreover, since utility networks located on mining areas are under particular protection, the ability of making them more fitted to their own needs will make it possible for mining plants to exchange spatial data in a more efficient way.

scholarly journals From OpenEHR to FHIR and OMOP Data Model for Microbiology Findings

2021 ◽  
Author(s):  
Eugenia Rinaldi ◽  
Sylvia Thun
Keyword(s):  
Infection Control ◽  
Data Model ◽  
Data Exchange ◽  
Control Group ◽  
Common Data Model ◽  
University Hospitals ◽  
Data Set ◽  
Related Data ◽  
National Initiative ◽  
Data Elements

HiGHmed is a German Consortium where eight University Hospitals have agreed to the cross-institutional data exchange through novel medical informatics solutions. The HiGHmed Use Case Infection Control group has modelled a set of infection-related data in the openEHR format. In order to establish interoperability with the other German Consortia belonging to the same national initiative, we mapped the openEHR information to the Fast Healthcare Interoperability Resources (FHIR) format recommended within the initiative. FHIR enables fast exchange of data thanks to the discrete and independent data elements into which information is organized. Furthermore, to explore the possibility of maximizing analysis capabilities for our data set, we subsequently mapped the FHIR elements to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). The OMOP data model is designed to support the conduct of research to identify and evaluate associations between interventions and outcomes caused by these interventions. Mapping across standard allows to exploit their peculiarities while establishing and/or maintaining interoperability. This article provides an overview of our experience in mapping infection control related data across three different standards openEHR, FHIR and OMOP CDM.

scholarly journals Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership

2015 ◽  
Vol 06 (03) ◽  
pp. 536-547 ◽  
Author(s):  
F.S. Resnic ◽  
S.L. Robbins ◽  
J. Denton ◽  
L. Nookala ◽  
D. Meeker ◽  
...  
Keyword(s):  
Health Systems ◽  
Data Model ◽  
Comparative Effectiveness ◽  
Large Scale ◽  
Medication Administration ◽  
Use Cases ◽  
Common Data Model ◽  
Medical Outcomes ◽  
Data Set ◽  
Case Examples

SummaryBackground: Adoption of a common data model across health systems is a key infrastructure requirement to allow large scale distributed comparative effectiveness analyses. There are a growing number of common data models (CDM), such as Mini-Sentinel, and the Observational Medical Outcomes Partnership (OMOP) CDMs.Objective: In this case study, we describe the challenges and opportunities of a study specific use of the OMOP CDM by two health systems and describe three comparative effectiveness use cases developed from the CDM.Methods: The project transformed two health system databases (using crosswalks provided) into the OMOP CDM. Cohorts were developed from the transformed CDMs for three comparative effectiveness use case examples. Administrative/billing, demographic, order history, medication, and laboratory were included in the CDM transformation and cohort development rules.Results: Record counts per person month are presented for the eligible cohorts, highlighting differences between the civilian and federal datasets, e.g. the federal data set had more outpatient visits per person month (6.44 vs. 2.05 per person month). The count of medications per person month reflected the fact that one system‘s medications were extracted from orders while the other system had pharmacy fills and medication administration records. The federal system also had a higher prevalence of the conditions in all three use cases. Both systems required manual coding of some types of data to convert to the CDM.Conclusion: The data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data. The need to manually code subsets of data limited the conversion. However, once the native data was converted to the CDM, both systems were then able to use the same queries to identify cohorts. Thus, the CDM minimized the effort to develop cohorts and analyze the results across the sites.FitzHenry F, Resnic FS, Robbins SL, Denton J, Nookala L, Meeker D, Ohno-Machado L, Matheny ME. A Case Report on Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership. Appl Clin Inform 2015; 6: 536–547http://dx.doi.org/10.4338/ACI-2014-12-CR-0121

scholarly journals The IeDEA Data Exchange Standard: a common data model for global HIV cohort collaboration

2020 ◽  
Author(s):  
Stephany N Duda ◽  
Beverly S Musick ◽  
Mary-Ann Davies ◽  
Annette H Sohn ◽  
Bruno Ledergerber ◽  
...  
Keyword(s):  
Data Model ◽  
Data Exchange ◽  
Governance Structure ◽  
Data Models ◽  
Hiv Care ◽  
Common Data Model ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
Hiv Research ◽  
Data Exchange Standard

Objective To describe content domains and applications of the IeDEA Data Exchange Standard, its development history, governance structure, and relationships to other established data models, as well as to share open source, reusable, scalable, and adaptable implementation tools with the informatics community. Methods In 2012, the International Epidemiology Databases to Evaluate AIDS (IeDEA) collaboration began development of a data exchange standard, the IeDEA DES, to support collaborative global HIV epidemiology research. With the HIV Cohorts Data Exchange Protocol as a template, a global group of data managers, statisticians, clinicians, informaticians, and epidemiologists reviewed existing data schemas and clinic data procedures to develop the HIV data exchange model. The model received a substantial update in 2017, with annual updates thereafter. Findings The resulting IeDEA DES is a patient-centric common data model designed for HIV research that has been informed by established data models from US-based electronic health records, broad experience in data collection in resource-limited settings, and informatics best practices. The IeDEA DES is inherently flexible and continues to grow based on the ongoing stewardship of the IeDEA Data Harmonization Working Group with input from external collaborators. Use of the IeDEA DES has improved multiregional collaboration within and beyond IeDEA, expediting over 95 multiregional research projects using data from more than 400 HIV care and treatment sites across seven global regions. A detailed data model specification and REDCap data entry templates that implement the IeDEA DES are publicly available on GitHub. Conclusions The IeDEA common data model and related resources are powerful tools to foster collaboration and accelerate science across research networks. While currently directed towards observational HIV research and data from resource-limited settings, this model is flexible and extendable to other areas of health research.

scholarly journals AN APPROACH FOR CALCULATING LAND VALUATION BY USING INSPIRE DATA MODELS

2017 ◽  
Vol XLII-4/W6 ◽  
pp. 19-21 ◽  
Author(s):  
A. C. Aydinoglu ◽  
R. Bovkir
Keyword(s):  
Data Structures ◽  
Data Model ◽  
Data Models ◽  
Data Set ◽  
Valuation Process ◽  
Land Valuation ◽  
Gis Software ◽  
Data Requirements ◽  
Target Data

Land valuation is a highly important concept for societies and governments have always emphasis on the process especially for taxation, expropriation, market capitalization and economic activity purposes. To success an interoperable and standardised land valuation, INSPIRE data models can be very practical and effective. If data used in land valuation process produced in compliance with INSPIRE specifications, a reliable and effective land valuation process can be performed. In this study, possibility of the performing land valuation process with using the INSPIRE data models was analysed and with the help of Geographic Information Systems (GIS) a case study in Pendik was implemented. For this purpose, firstly data analysis and gathering was performed. After, different data structures were transformed according to the INSPIRE data model requirements. For each data set necessary ETL (Extract-Transform-Load) tools were produced and all data transformed according to the target data requirements. With the availability and practicability of spatial analysis tools of GIS software, land valuation calculations were performed for study area.

scholarly journals OSIRIS: A Minimum Data Set for Data Sharing and Interoperability in Oncology

2021 ◽  
pp. 256-265
Author(s):  
Julien Guérin ◽  
Yec'han Laizet ◽  
Vincent Le Texier ◽  
Laetitia Chanas ◽  
Bastien Rance ◽  
...  
Keyword(s):  
Cancer Research ◽  
Data Sharing ◽  
Data Model ◽  
Task Force ◽  
Genomic Data ◽  
Common Data Model ◽  
University Hospitals ◽  
Data Set ◽  
Community Initiatives ◽  
Minimal Data

PURPOSE Many institutions throughout the world have launched precision medicine initiatives in oncology, and a large amount of clinical and genomic data is being produced. Although there have been attempts at data sharing with the community, initiatives are still limited. In this context, a French task force composed of Integrated Cancer Research Sites (SIRICs), comprehensive cancer centers from the Unicancer network (one of Europe's largest cancer research organization), and university hospitals launched an initiative to improve and accelerate retrospective and prospective clinical and genomic data sharing in oncology. MATERIALS AND METHODS For 5 years, the OSIRIS group has worked on structuring data and identifying technical solutions for collecting and sharing them. The group used a multidisciplinary approach that included weekly scientific and technical meetings over several months to foster a national consensus on a minimal data set. RESULTS The resulting OSIRIS set and event-based data model, which is able to capture the disease course, was built with 67 clinical and 65 omics items. The group made it compatible with the HL7 Fast Healthcare Interoperability Resources (FHIR) format to maximize interoperability. The OSIRIS set was reviewed, approved by a National Plan Strategic Committee, and freely released to the community. A proof-of-concept study was carried out to put the OSIRIS set and Common Data Model into practice using a cohort of 300 patients. CONCLUSION Using a national and bottom-up approach, the OSIRIS group has defined a model including a minimal set of clinical and genomic data that can be used to accelerate data sharing produced in oncology. The model relies on clear and formally defined terminologies and, as such, may also benefit the larger international community.

scholarly journals A CF data model and implementation

2017 ◽  
Author(s):  
David Hassell ◽  
Jonathan Gregory ◽  
Jon Blower ◽  
Bryan N. Lawrence ◽  
Karl E. Taylor
Keyword(s):  
Data Model ◽  
Physical Meaning ◽  
Data Models ◽  
Common Data Model ◽  
Software Library ◽  
Minimal Set ◽  
Temporal Properties ◽  
Data Form ◽  
Coverage Model ◽  
Open Geospatial Consortium

Abstract. The CF (Climate and Forecast) metadata conventions are designed to promote the creation, processing and sharing of climate and forecasting data using Network Common Data Form (netCDF) files and libraries. The CF conventions provide a description of the physical meaning of data and of their spatial and temporal properties, but they depend on the netCDF file encoding which can currently only be fully understood and interpreted by someone familiar with the rules and relationships specified in the conventions documentation. To aid in development of CF-compliant software and to capture with a minimal set of elements all of the information contained in the CF conventions, we propose a formal data model for CF which is independent of netCDF and describes all possible CF-compliant data. Because such data will often be analysed and visualised using software based on other data models, we compare the CF data model with the ISO 19123 coverage model, the Open Geospatial Consortium CF netCDF standard and the Unidata Common Data Model. To demonstrate that the CF data model can in fact be implemented, we present cf-python, a Python software library that conforms to the model and can manipulate any CF-compliant dataset.

scholarly journals Fast Healthcare Interoperability Resources (FHIR) as a Meta Model to Integrate Common Data Models: Development of a Tool and Quantitative Validation Study

10.2196/15199 ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. e15199
Author(s):  
Emily Rose Pfaff ◽  
James Champion ◽  
Robert Louis Bradford ◽  
Marshall Clark ◽  
Hao Xu ◽  
...  
Keyword(s):  
Clinical Data ◽  
Data Model ◽  
Research Collaboration ◽  
Data Models ◽  
Research Network ◽  
Common Data Model ◽  
Patient Centered ◽  
Health Level 7 ◽  
Value Sets ◽  
Single Standard

Background In a multisite clinical research collaboration, institutions may or may not use the same common data model (CDM) to store clinical data. To overcome this challenge, we proposed to use Health Level 7’s Fast Healthcare Interoperability Resources (FHIR) as a meta-CDM—a single standard to represent clinical data. Objective In this study, we aimed to create an open-source application termed the Clinical Asset Mapping Program for FHIR (CAMP FHIR) to efficiently transform clinical data to FHIR for supporting source-agnostic CDM-to-FHIR mapping. Methods Mapping with CAMP FHIR involves (1) mapping each source variable to its corresponding FHIR element and (2) mapping each item in the source data’s value sets to the corresponding FHIR value set item for variables with strict value sets. To date, CAMP FHIR has been used to transform 108 variables from the Informatics for Integrating Biology & the Bedside (i2b2) and Patient-Centered Outcomes Research Network data models to fields across 7 FHIR resources. It is designed to allow input from any source data model and will support additional FHIR resources in the future. Results We have used CAMP FHIR to transform data on approximately 23,000 patients with asthma from our institution’s i2b2 database. Data quality and integrity were validated against the origin point of the data, our enterprise clinical data warehouse. Conclusions We believe that CAMP FHIR can serve as an alternative to implementing new CDMs on a project-by-project basis. Moreover, the use of FHIR as a CDM could support rare data sharing opportunities, such as collaborations between academic medical centers and community hospitals. We anticipate adoption and use of CAMP FHIR to foster sharing of clinical data across institutions for downstream applications in translational research.

Metadata-oriented Data Model Supporting Railway Distributed System Integration

2012 ◽  
Vol 7 (4) ◽  
Author(s):  
Hanning Wang ◽  
Weixiang Xu ◽  
Chaolong Jia
Keyword(s):  
Distributed System ◽  
Data Model ◽  
System Integration
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