Fuzzy Ontology-based Spatial Data Warehouse for Context-aware Search and Recommendation

Baazaoui-Zghal, Hajer

doi:10.5220/0006010501610166

Cited by 2 publications

(3 citation statements)

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“…SDW is utilized by geospatial decision support systems (DSSs) to query and analyze position information-related data [13], [31]. According to Baazaoui-Zghal [32], SDW has become increasingly necessary in many fields, such as healthcare and environmental disaster management. SDW is generally considered an efficient option when large volumes of data are involved.…”

Section: ) Spatial Data Warehousesmentioning

confidence: 99%

“…Li et al [33] suggested the use of a NoSQL database as a warehouse for geospatial big data whilst using a traditional geospatial database as the application server. Baazaoui-Zghal [32] proposed an ontology-based fuzzy SDW for contextual search and recommendation based on the integration of uncertain data at multiple levels of the knowledge layer whilst decisional architecture, contextual search and recommendation remain the same. The extraction of relevant and interesting information from SDWs can be complicated.…”

Section: ) Spatial Data Warehousesmentioning

confidence: 99%

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Review on Integrating Geospatial Big Datasets and Open Research Issues

et al. 2021

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Big data and geographic information systems (GIS) are two technologies that have increasingly influenced many areas in the last 10 years and will continue to improve and help solve serious global problems, such as consequences of climate change or global pandemics. A wide spectrum of GIS applications interacts with the continuous growth of geospatial big data sources to drive precise and informed decisions. Geospatial big data integration is designed to accomplish the compatibility of distinct geospatial datasets regardless of their spatial coverage. The large number of geospatial big data sources demand effective data integration for storing and handling such datasets, which will be used for geospatial data analysis and visualization. For instance, risk management datasets related to healthcare and the environment are heterogeneous and disparate. Obtaining a unified view of such geospatial big datasets is complicated and challenging, especially if we consider problems related to healthcare pandemics and environmental disasters. Hence, before we can attempt to predict and mitigate processes occurring in these domains, we must realize that geospatial big data integration is crucial in consolidating datasets. We explore and discuss issues involved in integrating geospatial big datasets in this study. We then classify big data integration processes into three categories, namely, data warehousing, data transformation and integration methods. Furthermore, several research challenges focused on geospatial big data, big earth data, data warehousing, data transformation and linked data are presented. Lastly, open research issues and emerging trends that require in-depth investigations in the near future are highlighted in this study.INDEX TERMS Big data integration, geographic information system (GIS), geospatial big data.

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Section: ) Spatial Data Warehousesmentioning

confidence: 99%

Section: ) Spatial Data Warehousesmentioning

confidence: 99%

Review on Integrating Geospatial Big Datasets and Open Research Issues

et al. 2021

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“…Maneechai and Kamolphiwong [128] proposed reducing the networking overhead for distributed reasoning across ontologies by piggybacking semantic information on the handshake messages necessary in peer to peer networks. Baazaoui-Zghal [129] proposed a meta ontology with fuzzy logic to explicitly specify knowledge about concepts, relationships, instances and axioms to improve user searches. Fuzzy logic was integrated into the ontological definition in order to handle imprecise information.…”

Section: F Context Knowledge Generation and Managementmentioning

confidence: 99%

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

et al. 2019

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Autonomous vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, advanced-vehicle systems (AVSs) need to be better contextually aware. The vast literature referring to ''context-aware systems'' is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realize contextual awareness in AVSs. This paper uses a human-AVSs (HAVSs) lens to polarize the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextual-awareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human-machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.

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Fuzzy Ontology-based Spatial Data Warehouse for Context-aware Search and Recommendation

Cited by 2 publications

References 0 publications

Review on Integrating Geospatial Big Datasets and Open Research Issues

Review on Integrating Geospatial Big Datasets and Open Research Issues

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

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