Dbms/Gis Applications in Integrated Marine Data Management

Author(s):  
N. N. Mikhailov ◽  
A. A. Vorontsov
Author(s):  
Catherine Maillard ◽  
Roy Lowry ◽  
G. Maudire ◽  
Dick Schaap ◽  
SeaDataNet Consortium
Keyword(s):  

Author(s):  
Helen M. Glaves

The paradigm shift in marine research moving from the traditional discipline based methodology to a multidisciplinary, ecosystem level approach is being driven by changes in both the policies for the management and exploitation of the ocean, and the scientific method itself. The availability of large volumes of good quality data is fundamental to this increasingly holistic approach to ocean research but there are significant barriers to its re-use. The Ocean Data Interoperability Platform (ODIP) project has been funded in parallel by the European Commission, National Science Foundation in the USA and the Australian Government to promote the development of a common framework for marine data management that leverages the existing marine e-infrastructures which have been created in response to the need for greater sharing of marine data at a regional level.


Author(s):  
Helen M. Glaves

The paradigm shift in marine research moving from the traditional discipline based methodology to a multidisciplinary, ecosystem level approach is being driven by changes in both the policies for the management and exploitation of the ocean, and the scientific method itself. The availability of large volumes of good quality data is fundamental to this increasingly holistic approach to ocean research but there are significant barriers to its re-use. The Ocean Data Interoperability Platform (ODIP) project has been funded in parallel by the European Commission, National Science Foundation in the USA and the Australian Government to promote the development of a common framework for marine data management that leverages the existing marine e-infrastructures which have been created in response to the need for greater sharing of marine data at a regional level.


2020 ◽  
Author(s):  
Leda Pecci ◽  
Michele Fichaut ◽  
Dick Schaap

<p>The pan-European SeaDataNet marine and ocean data infrastructure started in early 2000, by means of a European funded project to create a framework for the management of large and diverse sets of data deriving from in situ measurements. It has been improved thanks to different European projects, it represents the joint efforts of several marine institutes around the European and the Mediterranean seas. The current project that is improving the infrastructure is the SeaDataCloud Horizon 2020 project; it involves a network of 56 partners across 29 countries.</p><p>According to our main objectivest he project designed and implemented actions which can spur a response on an international level, creating the basis to reinforce the pan-European SeaDataCloud community.</p><p> </p><p>Information Technology (IT) has an important impact on how people work together. In the SeaDataCloud project the following web communication tools are used:</p><ul><li>SeaDataNet website and Extranet;</li> <li>Partners’ websites;</li> <li>Mailing lists;</li> <li>Electronic newsletters;</li> <li>On line educational materials;</li> <li>Videos and video tutorials;</li> <li>Twitter;</li> <li>Articles in e-journals;</li> </ul><p> </p><p>Members of the SeaDataCloud and SeaDataNet I and II, have had the opportunity of face to face meetings, the norm is to travel even for meetings of short duration. This investment in time and money allows direct contact between the partners of the projects. This creates an opportunity for people across Europe to meet each other, to work together and to speak openly.</p><p> </p><p>The IMDIS (International Conference on Marine Data and Information Systems) conferences have been organized in the framework of the European funded projects that have allowed the SeaDataNet infrastructure to be developed and upgraded. The meetings started in 2005 with the first conference organised in Brest (France), to share knowledge and best practices on marine data management. IMDIS is a unique platform and has the following goals:</p><ul><li>Raise awareness of the SeaDataNet infrastructure, new development and standards;</li> <li>Share experiences in ocean data management;</li> <li>Enable synergies between data providers and data managers.</li> </ul><p> </p><p>It has been a breeding ground for inspirational ideas, for example the project ODIP (Ocean Data Interoperability Platform) that led to its successor ODIP II project was conceived during one of the conferences. The challenges and objectives of the projects were to find common interoperability solutions to problems in ocean data sharing, in collaboration with institutions from Europe, USA and Australia. In this case the IMDIS series of conferences have represented an opportunity not only for knowledge exchange in ocean data management but they have led to significant results in terms of new synergies that made it possible to find new partners and projects.</p><p>The direct interactions during the meetings as well as the on line tools have had a positive impact on reinforcing the development of a large SeaDataNet community across Europe and beyond.</p><p>The SeaDataCloud project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nº 730960.</p>


Author(s):  
M. W. Jahn ◽  
P. E. Bradley ◽  
M. Al Doori ◽  
M. Breunig

Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. Approaches for a new geo-spatio-temporal and object model of DB4GeO meeting the requirements of big geo-spatial data are discussed in detail. Finally, a conclusion and outlook on our future research are given on the way to support the processing of geo-analytics and -simulations in a parallel and distributed system environment.


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