scholarly journals Open Data Integration in 3D CityGML-based Models Generation

2021 ◽  
Author(s):  
Mcdonnell Maieron ◽  
José Moreira de Oliveira
Keyword(s):  
Data Integration ◽  
Open Data

scholarly journals Framework for a Symmetric Integration Approach

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 224
Author(s):  
Mihaela Muntean ◽  
Claudiu Brândaş ◽  
Tanita Cîrstea
Keyword(s):  
Data Integration ◽  
Open Data ◽  
Application Integration ◽  
Cloud Environment ◽  
Integration Framework ◽  
Symmetry Approach ◽  
Integration Approach ◽  
Implementation Aspects

An Application-to-Application integration framework in the cloud environment is proposed. The methodological demarche is developed using a data symmetry approach. Implementation aspects of integration considered the Open Data Protocol (OData) service as an integrator. An important issue in the cloud environment is to integrate and ensure the quality of transferred and processed data. An efficient way of ensuring the completeness and integrity of data transferred between different applications and systems is the symmetry of data integration. With these considerations, the integration of SAP Hybris Cloud for Customer with S/4 HANA Cloud was implemented.

Data Integration for Open Data on the Web

2017 ◽  
pp. 1-28 ◽  
Author(s):  
Sebastian Neumaier ◽  
Axel Polleres ◽  
Simon Steyskal ◽  
Jürgen Umbrich
Keyword(s):  
Data Integration ◽  
Open Data ◽  
The Web

Using Information Visualization to Support Open Data Integration

2015 ◽  
pp. 1-15
Author(s):  
Paulo Carvalho ◽  
Patrik Hitzelberger ◽  
Benoît Otjacques ◽  
Fatma Bouali ◽  
Gilles Venturini
Keyword(s):  
Data Integration ◽  
Information Visualization ◽  
Open Data

Open Data Platform for Data Integration, Visualisation and Map Design

2014 ◽  
pp. 3-11
Author(s):  
Tomas Mildorf ◽  
Jan Jezek ◽  
Otakar Cerba ◽  
Christian Malewski ◽  
Simon Templer ◽  
...  
Keyword(s):  
Data Integration ◽  
Open Data ◽  
Data Platform ◽  
Map Design

scholarly journals A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics

2020 ◽  
Vol 9 (8) ◽  
pp. 474
Author(s):  
Linfang Ding ◽  
Guohui Xiao ◽  
Diego Calvanese ◽  
Liqiu Meng
Keyword(s):  
Data Integration ◽  
Visual Analysis ◽  
Open Data ◽  
Data Access ◽  
Heterogeneous Data ◽  
Geospatial Data ◽  
Making Sense ◽  
Research Areas ◽  
Geovisual Analytics ◽  
Integration Techniques

In a variety of applications relying on geospatial data, getting insights into heterogeneous geodata sources is crucial for decision making, but often challenging. The reason is that it typically requires combining information coming from different sources via data integration techniques, and then making sense out of the combined data via sophisticated analysis methods. To address this challenge we rely on two well-established research areas: data integration and geovisual analytics, and propose to adopt an ontology-based approach to decouple the challenges of data access and analytics. Our framework consists of two modules centered around an ontology: (1) an ontology-based data integration (OBDI) module, in which mappings specify the relationship between the underlying data and a domain ontology; (2) a geovisual analytics (GeoVA) module, designed for the exploration of the integrated data, by explicitly making use of standard ontologies. In this framework, ontologies play a central role by providing a coherent view over the heterogeneous data, and by acting as a mediator for visual analysis tasks. We test our framework in a scenario for the investigation of the spatiotemporal patterns of meteorological and traffic data from several open data sources. Initial studies show that our approach is feasible for the exploration and understanding of heterogeneous geospatial data.

scholarly journals Integrating Historical Person Registers as Linked Open Data in the WarSampo Knowledge Graph

2020 ◽  
pp. 118-126
Author(s):  
Mikko Koho ◽  
Petri Leskinen ◽  
Eero Hyvönen
Keyword(s):  
Data Integration ◽  
Record Linkage ◽  
Open Data ◽  
Knowledge Graph ◽  
Distributed Data ◽  
Application Development ◽  
Named Entities ◽  
Domain Experts ◽  
World War 2 ◽  
Semantic Data

Abstract Semantic data integration from heterogeneous, distributed data silos enables Digital Humanities research and application development employing a larger, mutually enriched and interlinked knowledge graph. However, data integration is challenging, involving aligning the data models and reconciling the concepts and named entities, such as persons and places. This paper presents a record linkage process to reconcile person references in different military historical person registers with structured metadata. The information about persons is aggregated into a single knowledge graph. The process was applied to reconcile three person registers of the popular semantic portal “WarSampo – Finnish World War 2 on the Semantic Web”. The registers contain detailed information about some 100 000 people and are individually maintained by domain experts. Thus, the integration process needs to be automatic and adaptable to changes in the registers. An evaluation of the record linkage results is promising and provides some insight into military person register reconciliation in general.

scholarly journals BIG GEO DATA MANAGEMENT: AN EXPLORATION WITH SOCIAL MEDIA AND TELECOMMUNICATIONS OPEN DATA

2016 ◽  
Vol XLI-B2 ◽  
pp. 595-601
Author(s):  
C. Arias Munoz ◽  
M. A. Brovelli ◽  
S. Corti ◽  
G. Zamboni
Keyword(s):  
Data Integration ◽  
High Speed ◽  
High Performance ◽  
Open Data ◽  
Geospatial Data ◽  
Data Sets ◽  
Complex Data ◽  
Complex Data Sets ◽  
Computationally Intensive ◽  
The Government

The term Big Data has been recently used to define big, highly varied, complex data sets, which are created and updated at a high speed and require faster processing, namely, a reduced time to filter and analyse relevant data. These data is also increasingly becoming Open Data (data that can be freely distributed) made public by the government, agencies, private enterprises and among others. There are at least two issues that can obstruct the availability and use of Open Big Datasets: Firstly, the gathering and geoprocessing of these datasets are very computationally intensive; hence, it is necessary to integrate high-performance solutions, preferably internet based, to achieve the goals. Secondly, the problems of heterogeneity and inconsistency in geospatial data are well known and affect the data integration process, but is particularly problematic for Big Geo Data. Therefore, Big Geo Data integration will be one of the most challenging issues to solve. With these applications, we demonstrate that is possible to provide processed Big Geo Data to common users, using open geospatial standards and technologies. NoSQL databases like MongoDB and frameworks like RASDAMAN could offer different functionalities that facilitate working with larger volumes and more heterogeneous geospatial data sources.

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