A Smart Campus Prototype for Demonstrating the Semantic Integration of Heterogeneous Data

2011 ◽  
pp. 238-243 ◽  
Author(s):  
Aidan Boran ◽  
Ivan Bedini ◽  
Christopher J. Matheus ◽  
Peter F. Patel-Schneider ◽  
John Keeney
Keyword(s):  
Heterogeneous Data ◽  
Semantic Integration ◽  
Smart Campus

Grid Technologies in Epidemiology

2011 ◽  
pp. 426-443
Author(s):  
Ignacio Blanquer ◽  
Vicente Hernandez
Keyword(s):  
Large Scale ◽  
Heterogeneous Data ◽  
Semantic Integration ◽  
Security And Privacy ◽  
Use Case ◽  
Seamless Integration ◽  
Computationally Intensive ◽  
Art Research ◽  
Additional Constraints

Epidemiology constitutes one relevant use case for the adoption of grids for health. It combines challenges that have been traditionally addressed by grid technologies, such as managing large amounts of distributed and heterogeneous data, large scale computing and the need for integration and collaboration tools, but introduces new challenges traditionally addressed from the e-health area. The application of grid technologies to epidemiology has been concentrated in the federation of distributed repositories of data, the evaluation of computationally intensive statistical epidemiological models and the management of authorisation mechanism in virtual organisations. However, epidemiology presents important additional constraints that are not solved and harness the take-off of grid technologies. The most important problems are on the semantic integration of data, the effective management of security and privacy, the lack of exploitation models for the use of infrastructures, the instability of Quality of Service and the seamless integration of the technology on the epidemiology environment. This chapter presents an analysis of how these issues are being considered in state-of-the-art research.

scholarly journals Towards a semantic integration of data from learning platforms

2020 ◽  
Vol 9 (3) ◽  
pp. 535
Author(s):  
Khaoula Mrhar ◽  
Otmane Douimi ◽  
Mounia Abik ◽  
Naoual Chaouni Benabdellah
Keyword(s):  
Short Term Memory ◽  
Learning Strategy ◽  
Online Courses ◽  
Conditional Random Field ◽  
Formal System ◽  
Heterogeneous Data ◽  
Semantic Integration ◽  
E Learning ◽  
Learning Platforms ◽  
Data Source

<p>Nowadays, there is a huge production of Massive Open Online Courses MOOCs from universities around the world. The enrolled learners in MOOCs skyrocketed along with the number of the offered online courses. Of late, several universities scrambled to integrate MOOCs in their learning strategy. However, the majority of the universities are facing two major issues: firstly, because of the heterogeneity of the platforms used (e-learning and MOOC platforms), they are unable to establish a communication between the formal and non-formal system; secondly, they are incapable to exploit the feedbacks of the learners in a non-formal learning to personalize the learning according to the learner’s profile. Indeed, the educational platforms contain an extremely large number of data that are stored in different formats and in different places. In order to have an overview of all data related to their students from various educational heterogeneous platforms, the collection and integration of these heterogeneous data in a formal consolidated system is needed. The principal core of this system is the integration layer which is the purpose of this paper. In this paper, a semantic integration system is proposed. It allows us to extract, map and integrate data from heterogeneous learning platforms “MOOCs platforms, elearning platforms” by solving all semantic conflicts existing between these sources. Besides, we use different learning algorithms (Long short-term memory LSTM, Conditional Random Field CRF) to learn and recognize the mapping between data source and domain ontology.</p>

scholarly journals An approach for semantic integration of heterogeneous data sources

2020 ◽  
Vol 6 ◽  
pp. e254
Author(s):  
Giuseppe Fusco ◽  
Lerina Aversano
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Semantic Integration ◽  
Data Sources ◽  
Complex Data ◽  
Semantic Heterogeneity ◽  
Heterogeneous Information ◽  
Heterogeneous Data Sources ◽  
Autonomous Data Sources ◽  
Unified View

Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semi-structured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (Data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view.

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