Distributed Geospatial Data Management for Entomological and Epidemiological Studies

2013 ◽  
pp. 1773-1793
Author(s):  
Hugo Martins ◽  
Jorge G. Rocha
Keyword(s):  
Data Management ◽  
Data Model ◽  
Epidemiological Studies ◽  
Geospatial Data ◽  
Database Integration ◽  
Model Data ◽  
Unique Data ◽  
Semantic Translation ◽  
Open Geospatial Consortium ◽  
Ogc Standards

Since the authors were able to design all the supporting software, all syntactical interoperability was guaranteed by the use of Open Geospatial Consortium (OGC) standards. The semantic interoperability was assured by design, by developing a unique data model. Data invariants are guaranteed either by the interface, with validation routines written in Javascript, or by the data constrains included in the database. Integration and interoperability with other BT programs might require some additional effort, but all the necessary semantic translation could be encapsulated into the WFS component.

Distributed Geospatial Data Management for Entomological and Epidemiological Studies

2012 ◽  
pp. 220-240
Author(s):  
Hugo Martins ◽  
Jorge G. Rocha
Keyword(s):  
Data Management ◽  
Data Model ◽  
Epidemiological Studies ◽  
Geospatial Data ◽  
Database Integration ◽  
Model Data ◽  
Unique Data ◽  
Semantic Translation ◽  
Open Geospatial Consortium ◽  
Ogc Standards

Since the authors were able to design all the supporting software, all syntactical interoperability was guaranteed by the use of Open Geospatial Consortium (OGC) standards. The semantic interoperability was assured by design, by developing a unique data model. Data invariants are guaranteed either by the interface, with validation routines written in Javascript, or by the data constrains included in the database. Integration and interoperability with other BT programs might require some additional effort, but all the necessary semantic translation could be encapsulated into the WFS component.

scholarly journals Harmonising the OGC Standards for the Built Environment: A CityGML Extension for LandInfra

2019 ◽  
Vol 8 (6) ◽  
pp. 246 ◽  
Author(s):  
Kavisha Kumar ◽  
Anna Labetski ◽  
Ken Arroyo Ohori ◽  
Hugo Ledoux ◽  
Jantien Stoter
Keyword(s):  
Built Environment ◽  
Data Model ◽  
Markup Language ◽  
Application Domain ◽  
Software Support ◽  
Geography Markup Language ◽  
Domain Extension ◽  
Open Geospatial Consortium ◽  
Civil Engineering Infrastructure ◽  
Ogc Standards

The relatively new Open Geospatial Consortium (OGC) standard LandInfra documents in its data model land and civil engineering infrastructure features. It has a Geography Markup Language (GML) implementation, OGC InfraGML, which has essentially no software support and is rarely used in practice. In order to share the benefits of LandInfra (and InfraGML) with a wider public, we have created the Infra Application Domain Extension (ADE), a CityGML ADE that allows us to store LandInfra features in CityGML. In this paper, we semantically map LandInfra to CityGML, describe our ADE, and discuss a few used cases where our ADE can be useful for applications for the built environment. We also provide software to automatically convert datasets from InfraGML to CityGML (and our ADE), and vice versa, as well as to validate them, which will help practitioners generate real-world InfraGML datasets.

MapTap and Cornelia Slow Digital Art History and Formal Art Historical Social Network Research

2016 ◽  
Vol 79 (3) ◽  
pp. 315-330 ◽  
Author(s):  
Koenraad Brosens ◽  
Klara Alen ◽  
Astrid Slegten ◽  
Fred Truyen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Early Modern ◽  
Data Model ◽  
Art History ◽  
Digital Art ◽  
Unique Data ◽  
Network Research ◽  
Custom Made

Abstract The essay introduces MapTap, a research project that zooms in on the ever-changing social networks underpinning Flemish tapestry (1620 – 1720). MapTap develops the young and still slightly amorphous field of Formal Art Historical Social Network Research (FAHSNR) and is fueled by Cornelia, a custom-made database. Cornelia’s unique data model allows researchers to organize attribution and relational data from a wide array of sources in such a way that the complex multiplex and multimode networks emerging from the data can be transformed into partial unimode networks that enable proper FAHSNR. A case study revealing the key roles played by women in the tapestry landscape shows how this kind of slow digital art history can further our understanding of early modern creative communities and industries.

scholarly journals Mining multiple sources of historical data: The example of a standardized dataset of medieval monasteries and convents in France

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Adam Mertel ◽  
David Zbíral
Keyword(s):  
Data Mining ◽  
Spatial Patterns ◽  
Data Model ◽  
Computational Analysis ◽  
Historical Data ◽  
Model Data ◽  
Multiple Sources ◽  
The Public ◽  
Comprehensive Collection ◽  
Online Databases

<p><strong>Abstract.</strong> In this paper, we present a dataset of medieval monasteries and convents on the territory of today’s France and discuss the workflow of its integration. Spatial historical data are usually dispersed and stored in various forms &amp;ndash; encyclopedias and catalogues, websites, online databases, and printed maps. In order to cope with this heterogeneity and proceed to computational analysis, we have devised a method that includes the creation of a data model, data mining from sources, data transformation, geocoding, editing, and conflicts solving.</p><p> The resulting dataset is probably the most comprehensive collection of records on medieval monasteries within the borders of today’s France. It can be used for understanding the spatial patterns of medieval Christian monasticism and the implantation of the official Church infrastructure, as well as the relation between this official infrastructure and phenomena covered in other datasets. We open this dataset, as well as scripts for mining, to the public (https://github.com/adammertel/dissinet.monasteries) and provide a map tool to visualize, filter, and download the records (http://hde.geogr.muni.cz/monasteries).</p>

A Storage Model of Equipment Data Based on HBase

2015 ◽  
Vol 713-715 ◽  
pp. 2418-2422
Author(s):  
Lei Rao ◽  
Fan De Yang ◽  
Xin Ming Li ◽  
Dong Liu
Keyword(s):  
Data Management ◽  
Data Model ◽  
File System ◽  
File Systems ◽  
Database Systems ◽  
Labor Management ◽  
Storage Model ◽  
Reading And Writing ◽  
Writing Processes ◽  
Three Stages

Data management has experienced three stages: labor management, file systems, and database systems. In this paper, manage equipment data using a combination of HDFS file system and HBase database: the principles of HBase data management is studied; equipment data’s reading and writing processes is established; data model of equipment database is designed based on HBase.

Sensor Grid Enhancement with Data Management System for Ubiquitous Healthcare Computing

2013 ◽  
pp. 184-210
Author(s):  
Nikolaos Preve
Keyword(s):  
Data Management ◽  
Network Architecture ◽  
Management System ◽  
Data Access ◽  
Sensor Nodes ◽  
Data Management System ◽  
Biomedical Sensors ◽  
Communication Ability ◽  
Open Geospatial Consortium ◽  
Network Technologies

A Wireless Sensor Network (WSN) can be deployed to monitor the health of patients suffering from critical diseases. A wireless network consisting of biomedical sensors can also be implanted into the patient's body and can monitor the patients' conditions. These sensor devices, apart from having an enormous capability of collecting data from their physical surroundings, are also resource constraint in nature with a limited processing and communication ability. Therefore, it is necessary to integrate them with the Grid technology in order to process and store the collected data by the sensor nodes. This chapter proposes the SEnsor Grid Enhancement Data Management system, called SEGEDMA, ensuring the integration of different network technologies and the continuous data access to system users. The main contribution of this work is to achieve the interoperability of both technologies through a novel network architecture ensuring also the interoperability of Open Geospatial Consortium (OGC) and HL7 standards. According to the results SEGEDMA can be applied successfully in a decentralized healthcare environment.

scholarly journals CaosDB—Research Data Management for Complex, Changing, and Automated Research Workflows

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 83 ◽  
Author(s):  
Timm Fitschen ◽  
Alexander Schlemmer ◽  
Daniel Hornung ◽  
Henrik tom Wörden ◽  
Ulrich Parlitz ◽  
...  
Keyword(s):  
Data Management ◽  
Data Model ◽  
Query Language ◽  
Research Data ◽  
Data Management System ◽  
Biomedical Sciences ◽  
Seamless Integration ◽  
Research Data Management ◽  
The Status ◽  
Data Acquisition And Processing

We present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data in a FAIR way. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practices and thus support changes in workflow and data structure. Nevertheless, it should encourage and support the development and observation of standards and furthermore facilitate the automation of data acquisition and processing with specialized software. The storage data model of an RDMS must reflect these complexities with appropriate semantics and ontologies while offering simple methods for finding, retrieving, and understanding relevant data. We show how CaosDB responds to these challenges and give an overview of its data model, the CaosDB Server and its easy-to-learn CaosDB Query Language. We briefly discuss the status of the implementation, how we currently use CaosDB, and how we plan to use and extend it.

scholarly journals Data management for developing digital twin ontology model

2020 ◽  
pp. 095440542097811
Author(s):  
Sumit Singh ◽  
Essam Shehab ◽  
Nigel Higgins ◽  
Kevin Fowler ◽  
Dylan Reynolds ◽  
...  
Keyword(s):  
Data Management ◽  
Data Model ◽  
Domain Knowledge ◽  
Conceptual Knowledge ◽  
Current Approach ◽  
Model Structure ◽  
Digital Twin ◽  
Ontology Model ◽  
Minimum Data ◽  
Novel Concept

Digital Twin (DT) is the imitation of the real world product, process or system. Digital Twin is the ideal solution for data-driven optimisations in different phases of the product lifecycle. With the rapid growth in DT research, data management for digital twin is a challenging field for both industries and academia. The challenges for DT data management are analysed in this article are data variety, big data & data mining and DT dynamics. The current research proposes a novel concept of DT ontology model and methodology to address these data management challenges. The DT ontology model captures and models the conceptual knowledge of the DT domain. Using the proposed methodology, such domain knowledge is transformed into a minimum data model structure to map, query and manage databases for DT applications. The proposed research is further validated using a case study based on Condition-Based Monitoring (CBM) DT application. The query formulation around minimum data model structure further shows the effectiveness of the current approach by returning accurate results, along with maintaining semantics and conceptual relationships along DT lifecycle. The method not only provides flexibility to retain knowledge along DT lifecycle but also helps users and developers to design, maintain and query databases effectively for DT applications and systems of different scale and complexities.

Sign in / Sign up

Export Citation Format

Share Document