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Domain content querying using ontology-based context-awareness in information systems
Ubiquitous computing technologies have been applied in several areas. However, it still presents a number of challenges, both for the full implementation of technologies and for the integration with existing information systems. One of the main mismatches evidenced by recent works is how context-awa...
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Published in: | Data & knowledge engineering 2018-05, Vol.115, p.152-173 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Ubiquitous computing technologies have been applied in several areas. However, it still presents a number of challenges, both for the full implementation of technologies and for the integration with existing information systems. One of the main mismatches evidenced by recent works is how context-awareness, a widely used capability in ubiquitous computing and actual information systems with relational databases may be integrated to allow ubiquitous and traditional systems to query relational data sources without the necessity to modify the schema of the database. This paper presents an integration model relating context and domain information allowing relational data to be retrieved in context without the necessity to change the originally used relational queries. A set of linking rules and algorithms are formalized in a model and this model is implemented in a prototype. The evaluation of the model is performed by applying it in a case study in a Massive Open Online Course (MOOC) platform. The evaluation of the model by the application of it in a case study in a MOOC platform demonstrated the possibility to use an ontology frequently used in ubiquitous middleware as an extra filtering layer for information systems without the necessity to recreate queries or make a re-engineering in the relational database schema. The results of the queries after the application of the model showed an average decrease of 21% in returned tuples, which was evaluated as a significant reduce in tuple results. |
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ISSN: | 0169-023X 1872-6933 |
DOI: | 10.1016/j.datak.2018.03.003 |