The Experts below are selected from a list of 63285 Experts worldwide ranked by ideXlab platform
Onur Altintas - One of the best experts on this subject based on the ideXlab platform.
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a distributed system for reducing uploaded Data Redundancy in vehicular networks
IEEE International Conference on Pervasive Computing and Communications, 2019Co-Authors: Zezhi Wang, Yajie Zhao, Lewis Tseng, Takamasa Higuchi, Onur AltintasAbstract:Uploading vehicle sensor Data to support autonomous driving is necessary to understand the current situation to make the best decision. In this paper, we develop a system that relies on a peer-to-peer mechanism to obtain information in a vehicular network. Consider a scenario in which each vehicle equipped with a camera and communication capability is responsible to upload new snapshots to a Datacenter, and the Datacenter combines the snapshots to create a map. In a naive sensor Data upload scheme, each vehicle uploads its snapshot periodically and the Datacenter will find out new information and integrate it to a map. This method might work if only a small number of vehicles are uploading at the same time. However, this naive method is not scalable when dozens of vehicles need to upload, as the communication bandwidth will be a bottleneck. To address the challenge, we propose a novel distributed system to reduce Data Redundancy. As a result, the bandwidth consumption between vehicles and the Datacenter is reduced as well. The key idea is to use location information (e.g., GPS coordinates) to simplify the design and coordination among peers (vehicles), and rely on computer vision algorithms to remove redundant Data and identify important information to be uploaded. In this paper, we outline the design of our system and verify the efficacy of our system through a simulation study.
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PerCom Workshops - A Distributed System for Reducing Uploaded Data Redundancy in Vehicular Networks
2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2019Co-Authors: Zezhi Wang, Yajie Zhao, Lewis Tseng, Takamasa Higuchi, Onur AltintasAbstract:Uploading vehicle sensor Data to support autonomous driving is necessary to understand the current situation to make the best decision. In this paper, we develop a system that relies on a peer-to-peer mechanism to obtain information in a vehicular network. Consider a scenario in which each vehicle equipped with a camera and communication capability is responsible to upload new snapshots to a Datacenter, and the Datacenter combines the snapshots to create a map. In a naive sensor Data upload scheme, each vehicle uploads its snapshot periodically and the Datacenter will find out new information and integrate it to a map. This method might work if only a small number of vehicles are uploading at the same time. However, this naive method is not scalable when dozens of vehicles need to upload, as the communication bandwidth will be a bottleneck. To address the challenge, we propose a novel distributed system to reduce Data Redundancy. As a result, the bandwidth consumption between vehicles and the Datacenter is reduced as well. The key idea is to use location information (e.g., GPS coordinates) to simplify the design and coordination among peers (vehicles), and rely on computer vision algorithms to remove redundant Data and identify important information to be uploaded. In this paper, we outline the design of our system and verify the efficacy of our system through a simulation study.
Ricardo Rodrigues Ciferri - One of the best experts on this subject based on the ideXlab platform.
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The impact of spatial Data Redundancy on SOLAP query performance
Journal of the Brazilian Computer Society, 2009Co-Authors: Thiago Luís Lopes Siqueira, Cristina Dutra De Aguiar Ciferri, Valéria Cesário Times, Anjolina Grisi De Oliveira, Ricardo Rodrigues CiferriAbstract:Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical Data models for GDW. However, little effort has been focused on studying how spatial Data Redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that Redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in Data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most 0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial Data Redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.FAPESPCNPqCoordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)INEPFINE
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The impact of spatial Data Redundancy on SOLAP query performance
Journal of the Brazilian Computer Society, 2009Co-Authors: Thiago Luís Lopes Siqueira, Valéria Cesário Times, Cristina Dutra De Aguiar Ciferri, Anjolina Grisi De Oliveira, Ricardo Rodrigues CiferriAbstract:Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical Data models for GDW. However, little effort has been focused on studying how spatial Data Redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that Redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in Data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most 0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial Data Redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.
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GeoInfo - Investigating the Effects of Spatial Data Redundancy in Query Performance over Geographical Data Warehouses.
2008Co-Authors: Thiago Luís Lopes Siqueira, Valéria Cesário Times, Ricardo Rodrigues Ciferri, Cristina Dutra De Aguiar CiferriAbstract:1 This work has been supported by the following Brazilian research agencies: CAPES, CNPq, FAPESP, FINEP and INEP. The first two authors also thank the support of the Web-PIDE Project in the context of the Observatory of the Education of the Brazilian Government. Abstract. Geographical Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis. For these, several conceptual and logical Data models have been proposed in the literature. However, little attention has been devoted to the study of how spatial Data Redundancy affects query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that Redundancy is related to high performance losses. Further, we analyze the indexing issue, aiming at improving query performance on a redundant GDW. Comparisons among the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST showed that SB-index significantly improves the elapsed time on query processing from 25% up to 95%.
Zezhi Wang - One of the best experts on this subject based on the ideXlab platform.
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a distributed system for reducing uploaded Data Redundancy in vehicular networks
IEEE International Conference on Pervasive Computing and Communications, 2019Co-Authors: Zezhi Wang, Yajie Zhao, Lewis Tseng, Takamasa Higuchi, Onur AltintasAbstract:Uploading vehicle sensor Data to support autonomous driving is necessary to understand the current situation to make the best decision. In this paper, we develop a system that relies on a peer-to-peer mechanism to obtain information in a vehicular network. Consider a scenario in which each vehicle equipped with a camera and communication capability is responsible to upload new snapshots to a Datacenter, and the Datacenter combines the snapshots to create a map. In a naive sensor Data upload scheme, each vehicle uploads its snapshot periodically and the Datacenter will find out new information and integrate it to a map. This method might work if only a small number of vehicles are uploading at the same time. However, this naive method is not scalable when dozens of vehicles need to upload, as the communication bandwidth will be a bottleneck. To address the challenge, we propose a novel distributed system to reduce Data Redundancy. As a result, the bandwidth consumption between vehicles and the Datacenter is reduced as well. The key idea is to use location information (e.g., GPS coordinates) to simplify the design and coordination among peers (vehicles), and rely on computer vision algorithms to remove redundant Data and identify important information to be uploaded. In this paper, we outline the design of our system and verify the efficacy of our system through a simulation study.
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PerCom Workshops - A Distributed System for Reducing Uploaded Data Redundancy in Vehicular Networks
2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2019Co-Authors: Zezhi Wang, Yajie Zhao, Lewis Tseng, Takamasa Higuchi, Onur AltintasAbstract:Uploading vehicle sensor Data to support autonomous driving is necessary to understand the current situation to make the best decision. In this paper, we develop a system that relies on a peer-to-peer mechanism to obtain information in a vehicular network. Consider a scenario in which each vehicle equipped with a camera and communication capability is responsible to upload new snapshots to a Datacenter, and the Datacenter combines the snapshots to create a map. In a naive sensor Data upload scheme, each vehicle uploads its snapshot periodically and the Datacenter will find out new information and integrate it to a map. This method might work if only a small number of vehicles are uploading at the same time. However, this naive method is not scalable when dozens of vehicles need to upload, as the communication bandwidth will be a bottleneck. To address the challenge, we propose a novel distributed system to reduce Data Redundancy. As a result, the bandwidth consumption between vehicles and the Datacenter is reduced as well. The key idea is to use location information (e.g., GPS coordinates) to simplify the design and coordination among peers (vehicles), and rely on computer vision algorithms to remove redundant Data and identify important information to be uploaded. In this paper, we outline the design of our system and verify the efficacy of our system through a simulation study.
Thiago Luís Lopes Siqueira - One of the best experts on this subject based on the ideXlab platform.
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The impact of spatial Data Redundancy on SOLAP query performance
Journal of the Brazilian Computer Society, 2009Co-Authors: Thiago Luís Lopes Siqueira, Cristina Dutra De Aguiar Ciferri, Valéria Cesário Times, Anjolina Grisi De Oliveira, Ricardo Rodrigues CiferriAbstract:Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical Data models for GDW. However, little effort has been focused on studying how spatial Data Redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that Redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in Data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most 0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial Data Redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.FAPESPCNPqCoordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)INEPFINE
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The impact of spatial Data Redundancy on SOLAP query performance
Journal of the Brazilian Computer Society, 2009Co-Authors: Thiago Luís Lopes Siqueira, Valéria Cesário Times, Cristina Dutra De Aguiar Ciferri, Anjolina Grisi De Oliveira, Ricardo Rodrigues CiferriAbstract:Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical Data models for GDW. However, little effort has been focused on studying how spatial Data Redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that Redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in Data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most 0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial Data Redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.
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GeoInfo - Investigating the Effects of Spatial Data Redundancy in Query Performance over Geographical Data Warehouses.
2008Co-Authors: Thiago Luís Lopes Siqueira, Valéria Cesário Times, Ricardo Rodrigues Ciferri, Cristina Dutra De Aguiar CiferriAbstract:1 This work has been supported by the following Brazilian research agencies: CAPES, CNPq, FAPESP, FINEP and INEP. The first two authors also thank the support of the Web-PIDE Project in the context of the Observatory of the Education of the Brazilian Government. Abstract. Geographical Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis. For these, several conceptual and logical Data models have been proposed in the literature. However, little attention has been devoted to the study of how spatial Data Redundancy affects query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that Redundancy is related to high performance losses. Further, we analyze the indexing issue, aiming at improving query performance on a redundant GDW. Comparisons among the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST showed that SB-index significantly improves the elapsed time on query processing from 25% up to 95%.
B. Seeger - One of the best experts on this subject based on the ideXlab platform.
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Data Redundancy and duplicate detection in spatial join processing
Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073), 2000Co-Authors: J.-p. Dittrich, B. SeegerAbstract:The partition-based spatial-merge join (PBSM) of J.M. Patel and D.J. DeWitt (1996) and the size separation spatial join (S/sup 3/J) of N. Koudas and K.C. Sevcik (1997) are considered to be among the most efficient methods for processing spatial (intersection) joins on two or more spatial relations. Neither method assumes the presence of pre-existing spatial indices on the relations. In this paper, we propose several improvements to these join algorithms. In particular, we deal with the impact of Data Redundancy and duplicate detection on the performance of these methods. For PBSM, we present a simple and inexpensive online method to detect duplicates in the response set. There is no longer any need to eliminate duplicates in a final sorting phase, as was originally suggested. We also investigate the impact of different internal algorithms on the total run-time of PBSM. For S/sup 3/J, we break with the original design goal and introduce controlled Redundancy of Data objects. Results of a large set of experiments with real Data sets reveal that our suggested modifications to PBSM and S/sup 3/J result in substantial performance improvements, where PBSM is generally superior to S/sup 3/J.
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ICDE - Data Redundancy and duplicate detection in spatial join processing
Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073), 1Co-Authors: Jens Dittrich, B. SeegerAbstract:The partition-based spatial-merge join (PBSM) of J.M. Patel and D.J. DeWitt (1996) and the size separation spatial join (S/sup 3/J) of N. Koudas and K.C. Sevcik (1997) are considered to be among the most efficient methods for processing spatial (intersection) joins on two or more spatial relations. Neither method assumes the presence of pre-existing spatial indices on the relations. In this paper, we propose several improvements to these join algorithms. In particular, we deal with the impact of Data Redundancy and duplicate detection on the performance of these methods. For PBSM, we present a simple and inexpensive online method to detect duplicates in the response set. There is no longer any need to eliminate duplicates in a final sorting phase, as was originally suggested. We also investigate the impact of different internal algorithms on the total run-time of PBSM. For S/sup 3/J, we break with the original design goal and introduce controlled Redundancy of Data objects. Results of a large set of experiments with real Data sets reveal that our suggested modifications to PBSM and S/sup 3/J result in substantial performance improvements, where PBSM is generally superior to S/sup 3/J.
