The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Mairtin. O'droma - One of the best experts on this subject based on the ideXlab platform.
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IEEE Conf. on Intelligent Systems - A service recommendation model for the Ubiquitous Consumer Wireless World
2016 IEEE 8th International Conference on Intelligent Systems (IS), 2016Co-Authors: Haiyang Zhang, Nikola S. Nikolov, Ivan Ganchev, Mairtin. O'dromaAbstract:This paper describes the general service recommendation process matched to the telecommunication service delivery characteristics of the Ubiquitous Consumer Wireless World (UCWW). The goal is to provide consumers with the ‘best’ service instances that match their dynamic, contextualized and personalized requirements and expectations, thereby aligning their usage of mobile services to the always best connected and best served (ABC&S) paradigm. A four-tiered Architectural Configuration of the UCWW service recommendation framework is proposed along with a suitable service recommendation model. Specific and generic smart-city application examples are outlined. Other relevant social impact of the proposed approach is highlighted at the conclusion of the paper.
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A service recommendation model for the Ubiquitous Consumer Wireless World
2016 IEEE 8th International Conference on Intelligent Systems (IS), 2016Co-Authors: Haiyang Zhang, Nikola S. Nikolov, Ivan Ganchev, Mairtin. O'dromaAbstract:This paper describes the general service recommendation process matched to the telecommunication service delivery characteristics of the Ubiquitous Consumer Wireless World (UCWW). The goal is to provide consumers with the `best' service instances that match their dynamic, contextualized and personalized requirements and expectations, thereby aligning their usage of mobile services to the always best connected and best served (ABC&S) paradigm. A four-tiered Architectural Configuration of the UCWW service recommendation framework is proposed along with a suitable service recommendation model. Specific and generic smart-city application examples are outlined. Other relevant social impact of the proposed approach is highlighted at the conclusion of the paper.
Yunji Chen - One of the best experts on this subject based on the ideXlab platform.
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ISCA - ArchRanker: a ranking approach to design space exploration
ACM SIGARCH Computer Architecture News, 2014Co-Authors: Tianshi Chen, Ke Tang, Olivier Temam, Zhiwei Xu, Zhi-hua Zhou, Yunji ChenAbstract:Architectural Design Space Exploration (DSE) is a notoriously difficult problem due to the exponentially large size of the design space and long simulation times. Previously, many studies proposed to formulate DSE as a regression problem which predicts architecture responses (e.g., time, power) of a given Architectural Configuration. Several of these techniques achieve high accuracy, though often at the cost of significant simulation time for training the regression models. We argue that the information the architect mostly needs during the DSE process is whether a given Configuration will perform better than another one in the presences of design constraints, or better than any other one seen so far, rather than precisely estimating the performance of that Configuration. Based on this observation, we propose a novel rankingbased approach to DSE where we train a model to predict which of two architecture Configurations will perform best. We show that, not only this ranking model more accurately predicts the relative merit of two architecture Configurations than an ANN-based state-of-the-art regression model, but also that it requires much fewer training simulations to achieve the same accuracy, or that it can be used for and is even better at quantifying the performance gap between two Configurations We implement the framework for training and using this model, called ArchRanker, and we evaluate it on several DSE scenarios (unicore/multicore design spaces, and both time and power performance metrics). We try to emulate as closely as possible the DSE process by creating constraint-based scenarios, or an iterative DSE process. We find that ArchRanker makes 29:68% to 54:43% fewer incorrect predictions on pairwise relative merit of Configurations (tested with 79,800 Configuration pairs) than an ANN-based regression model across all DSE scenarios considered (values averaged over all benchmarks for each scenario). We also find that, to achieve the same accuracy as ArchRanker, the ANN often requires three times more training simulations
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ArchRanker: A ranking approach to design space exploration
2014 ACM IEEE 41st International Symposium on Computer Architecture (ISCA), 2014Co-Authors: Tianshi Chen, Ke Tang, Olivier Temam, Zhiwei Xu, Zhi-hua Zhou, Yunji ChenAbstract:Architectural Design Space Exploration (DSE) is a notoriously difficult problem due to the exponentially large size of the design space and long simulation times. Previously, many studies proposed to formulate DSE as a regression problem which predicts architecture responses (e.g., time, power) of a given Architectural Configuration. Several of these techniques achieve high accuracy, though often at the cost of significant simulation time for training the regression models.We argue that the information the architect mostly needs during the DSEprocess is whether a given Configuration will perform better than another one in the presences ofdesign constraints, or better than any other one seen so far, rather than precisely estimating the performance of that Configuration. Based on this observation, we propose a novel rankingbased approach to DSE where we train a model to predict which of two architecture Configurations will perform best. We show that, not only this ranking model more accurately predicts the relative merit of two architecture Configurations than an ANN-based state-of-the-art regression model, but also that it requires much fewer training simulations to achieve the same accuracy, or that it can be used for and is even better at quantifying the performance gap between two Configurations. We implement the framework for training and using this model, called ArchRanker, and we evaluate it on several DSE scenarios (unicore/multicore design spaces, and both time and power performance metrics). We try to emulate as closely as possible the DSE process by creating constraint-based scenarios, or an iterative DSEprocess. We find that ArchRanker makes 29.68% to 54.43% fewer incorrect predictions on pairwise relative merit of Configurations (tested with 79,800 Configuration pairs) than an ANN-based regression model across all DSE scenarios considered (values averaged over all benchmarks for each scenario). We also find that, to achieve the same accuracy as ArchRanker, the ANN often requires three times more training simulations.
Haiyang Zhang - One of the best experts on this subject based on the ideXlab platform.
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IEEE Conf. on Intelligent Systems - A service recommendation model for the Ubiquitous Consumer Wireless World
2016 IEEE 8th International Conference on Intelligent Systems (IS), 2016Co-Authors: Haiyang Zhang, Nikola S. Nikolov, Ivan Ganchev, Mairtin. O'dromaAbstract:This paper describes the general service recommendation process matched to the telecommunication service delivery characteristics of the Ubiquitous Consumer Wireless World (UCWW). The goal is to provide consumers with the ‘best’ service instances that match their dynamic, contextualized and personalized requirements and expectations, thereby aligning their usage of mobile services to the always best connected and best served (ABC&S) paradigm. A four-tiered Architectural Configuration of the UCWW service recommendation framework is proposed along with a suitable service recommendation model. Specific and generic smart-city application examples are outlined. Other relevant social impact of the proposed approach is highlighted at the conclusion of the paper.
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A service recommendation model for the Ubiquitous Consumer Wireless World
2016 IEEE 8th International Conference on Intelligent Systems (IS), 2016Co-Authors: Haiyang Zhang, Nikola S. Nikolov, Ivan Ganchev, Mairtin. O'dromaAbstract:This paper describes the general service recommendation process matched to the telecommunication service delivery characteristics of the Ubiquitous Consumer Wireless World (UCWW). The goal is to provide consumers with the `best' service instances that match their dynamic, contextualized and personalized requirements and expectations, thereby aligning their usage of mobile services to the always best connected and best served (ABC&S) paradigm. A four-tiered Architectural Configuration of the UCWW service recommendation framework is proposed along with a suitable service recommendation model. Specific and generic smart-city application examples are outlined. Other relevant social impact of the proposed approach is highlighted at the conclusion of the paper.
Victor Esteller - One of the best experts on this subject based on the ideXlab platform.
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Quality-based bottom-up design of reference architecture applied to Healthcare Integrated Information Systems
2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS), 2015Co-Authors: Francisca Losavio, Oscar Ordaz, Victor EstellerAbstract:Software Product Line (SPL) is a set of software-intensive systems, sharing a common, managed set of features that satisfy the specific needs of a particular market segment or domain. Reference Architecture (RA) is the main asset shared by all SPL products; it covers commonality and variability of the SPL family of products or systems and it is used as a template to produce new products. We propose a semi-automatic bottom-up refactoring process to build RA considering the logic view (static aspects) of architectures of existing products in a domain, represented by a connected graph or valid Architectural Configuration. A candidate architecture (CA) is obtained automatically and manually completed by introducing new variants to satisfy quality properties. RA is finally built by grouping into variation points CA variants accomplishing similar tasks. We consider functional and non-functional variability modeling of components and connectors. The quality properties to be satisfied are specified by the ISO/IEC 25010 quality model, using scenarios of available Architectural solutions for each quality property that can be traced to the original component requiring it; our approach combines quality standards with goal-oriented techniques to consider non-functional variability, which is still an open research issue in SPL. The complete RA design process is applied to the domain of Integrated Healthcare Information Systems.
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RCIS - Quality-based bottom-up design of reference architecture applied to Healthcare Integrated Information Systems
2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS), 2015Co-Authors: Francisca Losavio, Oscar Ordaz, Victor EstellerAbstract:Software Product Line (SPL) is a set of software-intensive systems, sharing a common, managed set of features that satisfy the specific needs of a particular market segment or domain. Reference Architecture (RA) is the main asset shared by all SPL products; it covers commonality and variability of the SPL family of products or systems and it is used as a template to produce new products. We propose a semi-automatic bottom-up refactoring process to build RA considering the logic view (static aspects) of architectures of existing products in a domain, represented by a connected graph or valid Architectural Configuration. A candidate architecture (CA) is obtained automatically and manually completed by introducing new variants to satisfy quality properties. RA is finally built by grouping into variation points CA variants accomplishing similar tasks. We consider functional and non-functional variability modeling of components and connectors. The quality properties to be satisfied are specified by the ISO/IEC 25010 quality model, using scenarios of available Architectural solutions for each quality property that can be traced to the original component requiring it; our approach combines quality standards with goal-oriented techniques to consider non-functional variability, which is still an open research issue in SPL. The complete RA design process is applied to the domain of Integrated Healthcare Information Systems.
Tianshi Chen - One of the best experts on this subject based on the ideXlab platform.
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ISCA - ArchRanker: a ranking approach to design space exploration
ACM SIGARCH Computer Architecture News, 2014Co-Authors: Tianshi Chen, Ke Tang, Olivier Temam, Zhiwei Xu, Zhi-hua Zhou, Yunji ChenAbstract:Architectural Design Space Exploration (DSE) is a notoriously difficult problem due to the exponentially large size of the design space and long simulation times. Previously, many studies proposed to formulate DSE as a regression problem which predicts architecture responses (e.g., time, power) of a given Architectural Configuration. Several of these techniques achieve high accuracy, though often at the cost of significant simulation time for training the regression models. We argue that the information the architect mostly needs during the DSE process is whether a given Configuration will perform better than another one in the presences of design constraints, or better than any other one seen so far, rather than precisely estimating the performance of that Configuration. Based on this observation, we propose a novel rankingbased approach to DSE where we train a model to predict which of two architecture Configurations will perform best. We show that, not only this ranking model more accurately predicts the relative merit of two architecture Configurations than an ANN-based state-of-the-art regression model, but also that it requires much fewer training simulations to achieve the same accuracy, or that it can be used for and is even better at quantifying the performance gap between two Configurations We implement the framework for training and using this model, called ArchRanker, and we evaluate it on several DSE scenarios (unicore/multicore design spaces, and both time and power performance metrics). We try to emulate as closely as possible the DSE process by creating constraint-based scenarios, or an iterative DSE process. We find that ArchRanker makes 29:68% to 54:43% fewer incorrect predictions on pairwise relative merit of Configurations (tested with 79,800 Configuration pairs) than an ANN-based regression model across all DSE scenarios considered (values averaged over all benchmarks for each scenario). We also find that, to achieve the same accuracy as ArchRanker, the ANN often requires three times more training simulations
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ArchRanker: A ranking approach to design space exploration
2014 ACM IEEE 41st International Symposium on Computer Architecture (ISCA), 2014Co-Authors: Tianshi Chen, Ke Tang, Olivier Temam, Zhiwei Xu, Zhi-hua Zhou, Yunji ChenAbstract:Architectural Design Space Exploration (DSE) is a notoriously difficult problem due to the exponentially large size of the design space and long simulation times. Previously, many studies proposed to formulate DSE as a regression problem which predicts architecture responses (e.g., time, power) of a given Architectural Configuration. Several of these techniques achieve high accuracy, though often at the cost of significant simulation time for training the regression models.We argue that the information the architect mostly needs during the DSEprocess is whether a given Configuration will perform better than another one in the presences ofdesign constraints, or better than any other one seen so far, rather than precisely estimating the performance of that Configuration. Based on this observation, we propose a novel rankingbased approach to DSE where we train a model to predict which of two architecture Configurations will perform best. We show that, not only this ranking model more accurately predicts the relative merit of two architecture Configurations than an ANN-based state-of-the-art regression model, but also that it requires much fewer training simulations to achieve the same accuracy, or that it can be used for and is even better at quantifying the performance gap between two Configurations. We implement the framework for training and using this model, called ArchRanker, and we evaluate it on several DSE scenarios (unicore/multicore design spaces, and both time and power performance metrics). We try to emulate as closely as possible the DSE process by creating constraint-based scenarios, or an iterative DSEprocess. We find that ArchRanker makes 29.68% to 54.43% fewer incorrect predictions on pairwise relative merit of Configurations (tested with 79,800 Configuration pairs) than an ANN-based regression model across all DSE scenarios considered (values averaged over all benchmarks for each scenario). We also find that, to achieve the same accuracy as ArchRanker, the ANN often requires three times more training simulations.
