The story of DB4GeO – A service-based geo-database architecture to support multi-dimensional data analysis and visualization

Breunig, Martin; Kuper, Paul Vincent; Butwilowski, Edgar; Thomsen, Andreas; Jahn, Markus; Dittrich, André; Al-Doori, Mulhim; Golovko, Darya; Menninghaus, Mathias

doi:10.1016/j.isprsjprs.2015.12.006

Cited by 21 publications

(31 citation statements)

References 17 publications

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“…Resuming the big data concepts and techniques from above and postulating a sound mathematical approach for a topologically consistent geo-spatio-temporal model, in a second step we introduce the theoretical and practical procedure of redesigning our geo-spatio-temporal database architecture called DB4GeO (Breunig et al, 2016) to implement parallel and distributed datamanagement concepts for a centralized workflow to reduce data transfer for ST-TOLAP in conjunction with simulation analytics. The main goal is to set up a hybrid data managment system which is able to provide a controlled data transfer from persistent storages to in-memory on RAM and provides a PlugIn-based service infrastructure which supports parallel algorithms to be plugged into the parallel database architectures where few different types of servers deal with the same data stock.…”

Section: Second Step: Implementation Of a Geo-spatio-temporal Databasmentioning

confidence: 99%

“…The connection to VTK and ParaView enables a lot of new features to DB4GeO not only in computational geometry and visualization for geo-spatio-temporal use. At the moment the DB4GeO architecture is based on db4o (database for objects) to handle the persistency of the stored geo-spatio-temporal objects and uses a RESTful (Representational State Transfer) Paradigm for web-communication (Breunig et al, 2016). But reading and writing to the physical hardware of VTKs geometry types and data sets is going to be managed, structured and distributed by DB4GeO in accordance with its own geo-spatio-temporal model and object model using a NoSQL-DB as backend to provide an efficient physical data integration in parallel DB environments.…”

Section: Second Step: Implementation Of a Geo-spatio-temporal Databasmentioning

confidence: 99%

“…A suitable topological model is the key to have control over efficient data distribution for load-balancing and sharding in case of processes depending on topological constraints. DB4GeO is an object oriented geospatio-temporal DBMS prototype developed to handle moving and morphing volumes, surfaces, lines and pointclouds written in JAVA programming language (Breunig et al, 2016).…”

Section: Second Step: Implementation Of a Geo-spatio-temporal Databasmentioning

confidence: 99%

“…The following papers deal with implementation and tests. We here focus on a mathematical model for topological consistency checking and on the preparation of DB4GeO (Breunig et al, 2016), our service-based geo-spatio-temporal database architecture, to handle big geo-spatial data with embedded complex analytics and simulations on parallel database system architectures. A brief discussion about big data in conjunction with GIS is given in (Goodchild, 2016).…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Topologically Consistent Models for Efficient Big Geo-Spatio-Temporal Data Distribution

Jahn

Bradley

Doori

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT: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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Section: Second Step: Implementation Of a Geo-spatio-temporal Databasmentioning

confidence: 99%

Section: Second Step: Implementation Of a Geo-spatio-temporal Databasmentioning

confidence: 99%

Section: Second Step: Implementation Of a Geo-spatio-temporal Databasmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Topologically Consistent Models for Efficient Big Geo-Spatio-Temporal Data Distribution

Jahn

Bradley

Doori

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…DB4GeO has already been tested to demonstrate several topological (Thomsen et al 2008), geospatial and spatiotemporal geo-applications (Breunig et al, 2016). Figure 6 reviews the current system architecture of DB4GeO.…”

Section: Review Of Db4geomentioning

confidence: 99%

Application of 3d Spatio-Temporal Data Modeling, Management, and Analysis in Db4geo

Kuper

Breunig

Al-Doori

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Many of today´s world wide challenges such as climate change, water supply and transport systems in cities or movements of crowds need spatio-temporal data to be examined in detail. Thus the number of examinations in 3D space dealing with geospatial objects moving in space and time or even changing their shapes in time will rapidly increase in the future. Prominent spatio-temporal applications are subsurface reservoir modeling, water supply after seawater desalination and the development of transport systems in mega cities. All of these applications generate large spatio-temporal data sets. However, the modeling, management and analysis of 3D geo-objects with changing shape and attributes in time still is a challenge for geospatial database architectures. In this article we describe the application of concepts for the modeling, management and analysis of 2.5D and 3D spatial plus 1D temporal objects implemented in DB4GeO, our service-oriented geospatial database architecture. An example application with spatio-temporal data of a landfill, near the city of Osnabrück in Germany demonstrates the usage of the concepts. Finally, an outlook on our future research focusing on new applications with big data analysis in three spatial plus one temporal dimension in the United Arab Emirates, especially the Dubai area, is given.

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Spatial data infrastructure (SDI) for inventory rockfalls with fragmentation information

2022

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The fragmentation phenomenon has a significant effect on rockfall risk assessment. This information is difficult to obtain, but it is key to improving rockfall modelling. For this reason, the RockModels team has gathered data on the fragmentation of several natural events since 2014 that nowadays wants to share them with professionals, academics and stakeholders. The best way for the dissemination of this information is the use of standard or data specifications in order to be interoperable. A fragmentation rockfall database has been created using all the gathered information, according to the INSPIRE Natural Hazard Area Data Specification currently in force. However, new tables have had to be added, since this specification does not consider fragmentation data. There are currently 6000 records of geometries of source areas, envelopes, deposits and mostly individual blocks. A web mapping application, with an automatic function for coordinate reference system transformation, has been created to facilitate access to the spatial database information. All that was developed on open-source software such as OpenLayers JavaScript library, database (PostGre-PostGIS) and the map generating Web Map Service (GeoServer). As more data are collected, the database can be easily updated and the new information will be published. Moreover, to improve data interpretation, a future task is to incorporate 3D models on the web application. The existence of this public database will facilitate research and advance in knowledge of this kind of natural hazards. Graphical abstract Rockfall volume distribution inventory data. Georeferenced rockfall database including fragmentation data according to Technical Data Specifications of INSPIRE for natural hazards zone. Database mapping of rockfall inventory data in a WMS open access. Geometric modelling and geovisualization to fragmentation process in rockfall investigations.

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The story of DB4GeO – A service-based geo-database architecture to support multi-dimensional data analysis and visualization

Cited by 21 publications

References 17 publications

Topologically Consistent Models for Efficient Big Geo-Spatio-Temporal Data Distribution

Topologically Consistent Models for Efficient Big Geo-Spatio-Temporal Data Distribution

Application of 3d Spatio-Temporal Data Modeling, Management, and Analysis in Db4geo

Spatial data infrastructure (SDI) for inventory rockfalls with fragmentation information

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