The Experts below are selected from a list of 495294 Experts worldwide ranked by ideXlab platform
Francois Dessables - One of the best experts on this subject based on the ideXlab platform.
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A Big Data Architecture for Automotive Applications: PSA Group Deployment Experience
Proceedings - 2017 17th IEEE ACM International Symposium on Cluster Cloud and Grid Computing CCGRID 2017, 2017Co-Authors: Amir Haroun, Ahmed Mostefaoui, Francois DessablesAbstract:Vehicles have become moving sensor platforms collecting huge volumes of Data from their various embedded sensors. This Data has a great value for automotive manufacturers and vehicles owners. Indeed, connected vehicles Data can be used in a large broad of automotive services ranging from safety services to well-being services (e.g. fatigue detection). However, vehicle fleets send big volumes of Data that traditional computing and storage approaches are not able to manage efficiently. In this paper, we present the experience of the PSA Group on leveraging big Data in automotive context. We describe in depth the big Data Architecture deployed within the PSA Group and the underlaying technologies/products used in each component.
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CCGrid - A Big Data Architecture for Automotive Applications: PSA Group Deployment Experience
2017 17th IEEE ACM International Symposium on Cluster Cloud and Grid Computing (CCGRID), 2017Co-Authors: Amir Haroun, Ahmed Mostefaoui, Francois DessablesAbstract:Vehicles have become moving sensor platforms collecting huge volumes of Data from their various embedded sensors. This Data has a great value for automotive manufacturers and vehicles owners. Indeed, connected vehicles Data can be used in a large broad of automotive services ranging from safety services to well-being services (e.g. fatigue detection). However, vehicle fleets send big volumes of Data that traditional computing and storage approaches are not able to manage efficiently. In this paper, we present the experience of the PSA Group1 on leveraging big Data in automotive context. We describe in depth the big Data Architecture deployed within the PSA Group and the underlaying technologies/products used in each component.
Amir Haroun - One of the best experts on this subject based on the ideXlab platform.
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A Big Data Architecture for Automotive Applications: PSA Group Deployment Experience
Proceedings - 2017 17th IEEE ACM International Symposium on Cluster Cloud and Grid Computing CCGRID 2017, 2017Co-Authors: Amir Haroun, Ahmed Mostefaoui, Francois DessablesAbstract:Vehicles have become moving sensor platforms collecting huge volumes of Data from their various embedded sensors. This Data has a great value for automotive manufacturers and vehicles owners. Indeed, connected vehicles Data can be used in a large broad of automotive services ranging from safety services to well-being services (e.g. fatigue detection). However, vehicle fleets send big volumes of Data that traditional computing and storage approaches are not able to manage efficiently. In this paper, we present the experience of the PSA Group on leveraging big Data in automotive context. We describe in depth the big Data Architecture deployed within the PSA Group and the underlaying technologies/products used in each component.
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CCGrid - A Big Data Architecture for Automotive Applications: PSA Group Deployment Experience
2017 17th IEEE ACM International Symposium on Cluster Cloud and Grid Computing (CCGRID), 2017Co-Authors: Amir Haroun, Ahmed Mostefaoui, Francois DessablesAbstract:Vehicles have become moving sensor platforms collecting huge volumes of Data from their various embedded sensors. This Data has a great value for automotive manufacturers and vehicles owners. Indeed, connected vehicles Data can be used in a large broad of automotive services ranging from safety services to well-being services (e.g. fatigue detection). However, vehicle fleets send big volumes of Data that traditional computing and storage approaches are not able to manage efficiently. In this paper, we present the experience of the PSA Group1 on leveraging big Data in automotive context. We describe in depth the big Data Architecture deployed within the PSA Group and the underlaying technologies/products used in each component.
Ahmed Mostefaoui - One of the best experts on this subject based on the ideXlab platform.
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A Big Data Architecture for Automotive Applications: PSA Group Deployment Experience
Proceedings - 2017 17th IEEE ACM International Symposium on Cluster Cloud and Grid Computing CCGRID 2017, 2017Co-Authors: Amir Haroun, Ahmed Mostefaoui, Francois DessablesAbstract:Vehicles have become moving sensor platforms collecting huge volumes of Data from their various embedded sensors. This Data has a great value for automotive manufacturers and vehicles owners. Indeed, connected vehicles Data can be used in a large broad of automotive services ranging from safety services to well-being services (e.g. fatigue detection). However, vehicle fleets send big volumes of Data that traditional computing and storage approaches are not able to manage efficiently. In this paper, we present the experience of the PSA Group on leveraging big Data in automotive context. We describe in depth the big Data Architecture deployed within the PSA Group and the underlaying technologies/products used in each component.
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CCGrid - A Big Data Architecture for Automotive Applications: PSA Group Deployment Experience
2017 17th IEEE ACM International Symposium on Cluster Cloud and Grid Computing (CCGRID), 2017Co-Authors: Amir Haroun, Ahmed Mostefaoui, Francois DessablesAbstract:Vehicles have become moving sensor platforms collecting huge volumes of Data from their various embedded sensors. This Data has a great value for automotive manufacturers and vehicles owners. Indeed, connected vehicles Data can be used in a large broad of automotive services ranging from safety services to well-being services (e.g. fatigue detection). However, vehicle fleets send big volumes of Data that traditional computing and storage approaches are not able to manage efficiently. In this paper, we present the experience of the PSA Group1 on leveraging big Data in automotive context. We describe in depth the big Data Architecture deployed within the PSA Group and the underlaying technologies/products used in each component.
Huawei Huang - One of the best experts on this subject based on the ideXlab platform.
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qoe driven big Data Architecture for smart city
IEEE Communications Magazine, 2018Co-Authors: Xiaoming He, Kun Wang, Huawei HuangAbstract:In the era of big Data, the applications/services of the smart city are expected to offer end users better QoE than in a conventional smart city. Nevertheless, various types of sensors will produce an increasing volume of big Data along with the implementation of a smart city, where we face redundant and diverse Data. Therefore, providing satisfactory QoE will become the major challenge in the big-Data-based smart city. In this article, to enhance the QoE, we propose a novel big Data Architecture consisting of three planes: the Data storage plane, the Data processing plane, and the Data application plane. The Data storage plane stores a wide variety of Data collected by sensors and originating from different Data sources. Then the Data processing plane filters, analyzes, and processes the ocean of Data to make decisions autonomously for extracting high-quality information. Finally, the application plane initiates the execution of the events corresponding to the decisions delivered from the Data processing plane. Under this Architecture, we particularly use machine learning techniques, trying to acquire accurate Data and deliver precise information to end users. Simulation results indicate that our proposals could achieve high QoE performance for the smart city.
David F. Andersen - One of the best experts on this subject based on the ideXlab platform.
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Challenges and requirements for developing Data Architecture supporting integration of sustainable supply chains
Information Technology and Management, 2015Co-Authors: Djoko Sigit Sayogo, Luis Luna-reyes, Holly Jarman, Giri Tayi, Deborah Lines Andersen, Theresa A. Pardo, Jing Zhang, David F. AndersenAbstract:Information asymmetry between consumers and supply chain actors represents a major barrier to the expansion of sustainable consumption. Developing an interoperable Data Architecture that enables the integration of Data regarding sustainability practices from disparate sources in sustainable supply chains is important for improving market transparency. This paper identifies main issues and requirements as perceived by the key stakeholders in the coffee supply chain for such development. The analysis reveals that building an interoperable Data Architecture necessitates awareness of several major challenges, including the difficulties of collecting accurate and creditable Data, limited technological capabilities, complex Data ownership and disclosure policy, issues of confidentiality, privacy and economic value of information, and cost of disclosing information. To deal with these challenges, we recommend that the development need to ensure Data quality, integrity and security, design information policy balancing commercial interests and openness, and design appropriate governance mechanism to complement the technological design in order to ensure the fair and proper use of the system.
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DG.O - A stakeholder analysis of interoperable Data Architecture: the case of I-Choose
Proceedings of the 13th Annual International Conference on Digital Government Research - dg.o '12, 2012Co-Authors: Djoko Sigit Sayogo, Luis Luna-reyes, Holly Jarman, Giri Tayi, David F. Andersen, Theresa A. Pardo, Jing Zhang, Andrew Whitmore, Jana Hrdinova, Xing TanAbstract:This paper presents the challenges associated with developing a Data Architecture supporting information interoperability in the supply-chain for sustainable food products. We analyze information elicited from experts in the supply-chain for organic and fair trade coffee to identify relevant stakeholders and the issues and challenges connected with developing an interoperable Data Architecture. This study assesses the salience of individual stakeholder groups and the challenges based on the stakeholders' attributes in terms of power, legitimacy and urgency. The following five issues/challenges were found to be the most salient, requiring primary focus in developing interoperable Data Architecture: trust in Data, cost to maintain the system, political resistance, oversight and governance, and the cost to consumers in terms of time and effort. In the conclusion we discuss potential future research and practical implications for designing an interoperable Data Architecture.
