Enabling the Continuous Evolution of Ontologies for Ontology-Based Data Management

2019 ◽  
pp. 19-43
Author(s):  
André Pomp ◽  
Johannes Lipp ◽  
Tobias Meisen
Keyword(s):  
Data Management ◽  
Continuous Evolution

scholarly journals Virtual research environments as-a-service by gCube

2016 ◽  
Author(s):  
Massimiliano Assante ◽  
Leonardo Candela ◽  
Donatella Castelli ◽  
Gianpaolo Coro ◽  
Lucio Lelii ◽  
...  
Keyword(s):  
Information Systems ◽  
Data Management ◽  
Large Scale ◽  
Software System ◽  
Data Services ◽  
Continuous Evolution ◽  
Research Environments ◽  
Data Infrastructures ◽  
Hybrid Data ◽  
Oriented Research

Science is in continuous evolution and so are the methodologies and approaches scientists tend to apply by calling for appropriate supporting environments. This is in part due to the limitations of the existing practices and in part due to the new possibilities offered by technology advances. gCube is a software system promoting elastic and seamless access to research assets (data, services, computing) across the boundaries of institutions, disciplines and providers to favour collaborative-oriented research tasks. Its primary goal is to enable Hybrid Data Infrastructures facilitating the dynamic definition and operation of Virtual Research Environments. To this end, it offers a comprehensive set of data management commodities on various types of data and a rich array of ``mediators'' to interface well-established Infrastructures and Information Systems from various domains. Its effectiveness has been proved by operating the D4Science.org infrastructure and serving concrete, multidisciplinary, challenging, and large scale scenarios. This paper gives an overview of the gCube system.

scholarly journals Virtual research environments as-a-service by gCube

2016 ◽  
Author(s):  
Massimiliano Assante ◽  
Leonardo Candela ◽  
Donatella Castelli ◽  
Gianpaolo Coro ◽  
Lucio Lelii ◽  
...  
Keyword(s):  
Information Systems ◽  
Data Management ◽  
Large Scale ◽  
Software System ◽  
Data Services ◽  
Continuous Evolution ◽  
Research Environments ◽  
Data Infrastructures ◽  
Hybrid Data ◽  
Oriented Research

Science is in continuous evolution and so are the methodologies and approaches scientists tend to apply by calling for appropriate supporting environments. This is in part due to the limitations of the existing practices and in part due to the new possibilities offered by technology advances. gCube is a software system promoting elastic and seamless access to research assets (data, services, computing) across the boundaries of institutions, disciplines and providers to favour collaborative-oriented research tasks. Its primary goal is to enable Hybrid Data Infrastructures facilitating the dynamic definition and operation of Virtual Research Environments. To this end, it offers a comprehensive set of data management commodities on various types of data and a rich array of ``mediators'' to interface well-established Infrastructures and Information Systems from various domains. Its effectiveness has been proved by operating the D4Science.org infrastructure and serving concrete, multidisciplinary, challenging, and large scale scenarios. This paper gives an overview of the gCube system.

Health Informatics: Handle with Caution

1997 ◽  
Vol 36 (02) ◽  
pp. 79-81
Author(s):  
V. Leroy ◽  
S. Maurice-Tison ◽  
B. Le Blanc ◽  
R. Salamon
Keyword(s):  
Health Care ◽  
Data Management ◽  
Medical Informatics ◽  
Health Care Professionals ◽  
Data Security ◽  
Health Informatics ◽  
The Other ◽  
Inappropriate Use

Abstract:The increased use of computers is a response to the considerable growth in information in all fields of activities. Related to this, in the field of medicine a new component appeared about 40 years ago: Medical Informatics. Its goals are to assist health care professionals in the choice of data to manage and in the choice of applications of such data. These possibilities for data management must be well understood and, related to this, two major dangers must be emphasized. One concerns data security, and the other concerns the processing of these data. This paper discusses these items and warns of the inappropriate use of medical informatics.

Data Management in an Epidemiological Study : Experiences from the Iowa 500 Field Follow-up and Family Study

1980 ◽  
Vol 19 (01) ◽  
pp. 37-41
Author(s):  
R. F. Woolson ◽  
M. T. Tsuang ◽  
L. R. Urban
Keyword(s):  
Data Management ◽  
Epidemiological Study ◽  
Family Study ◽  
Data Sets ◽  
Data Handling ◽  
Management Decisions ◽  
Numerous Data ◽  
Made In

We are now conducting a forty-year follow-up and family study of 200 schizophrenics, 325 manic-depressives and 160 surgical controls. This study began in 1973 and has continued to the present date. Numerous data handling and data management decisions were made in the course of collecting the data for the project. In this report some of the practical difficulties in the data handling and computer management of such large and bulky data sets are enumerated.

Framework for Biosignal Interpretation in Intensive Care and Anesthesia

1997 ◽  
Vol 36 (04/05) ◽  
pp. 340-344 ◽  
Author(s):  
I. Korhonen ◽  
M. van Gils ◽  
A. Kari ◽  
N. Saranummi
Keyword(s):  
Critical Care ◽  
Intensive Care ◽  
Data Management ◽  
Clinical Evaluation ◽  
Patient Monitoring ◽  
Research Community ◽  
Clinical Routine ◽  
The Poor ◽  
Outcomes Of Care

Abstract:Improved monitoring improves outcomes of care. As critical care is “critical”, everything that can be done to detect and prevent complications as early as possible benefits the patients. In spite of major efforts by the research community to develop and apply sophisticated biosignal interpretation methods (BSI), the uptake of the results by industry has been poor. Consequently, the BSI methods used in clinical routine are fairly simple. This paper postulates that the main reason for the poor uptake is the insufficient bridging between the actors (i.e., clinicians, industry and research). This makes it difficult for the BSI developers to understand what can be implemented into commercial systems and what will be accepted by clinicians as routine tools. A framework is suggested that enables improved interaction and cooperation between the actors. This framework is based on the emerging commercial patient monitoring and data management platforms which can be shared and utilized by all concerned, from research to development and finally to clinical evaluation.

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