scholarly journals Efficient data reduction for large-scale genetic mapping

2015 ◽  
Author(s):  
Veronika Strnadová-Neeley ◽  
Aydın Buluç ◽  
Jarrod Chapman ◽  
John R. Gilbert ◽  
Joseph Gonzalez ◽  
...  
Keyword(s):  
Genetic Mapping ◽  
Data Reduction ◽  
Large Scale ◽  
Efficient Data

scholarly journals Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM)

2021 ◽  
Vol 13 (7) ◽  
pp. 1367
Author(s):  
Yuanzhi Cai ◽  
Hong Huang ◽  
Kaiyang Wang ◽  
Cheng Zhang ◽  
Lei Fan ◽  
...  
Keyword(s):  
Large Scale ◽  
Semantic Segmentation ◽  
Optimal Combination ◽  
Reconstruction Technique ◽  
Process Data ◽  
Redundant Data ◽  
Multiple Data ◽  
Information Models ◽  
Efficient Data

Over the last decade, a 3D reconstruction technique has been developed to present the latest as-is information for various objects and build the city information models. Meanwhile, deep learning based approaches are employed to add semantic information to the models. Studies have proved that the accuracy of the model could be improved by combining multiple data channels (e.g., XYZ, Intensity, D, and RGB). Nevertheless, the redundant data channels in large-scale datasets may cause high computation cost and time during data processing. Few researchers have addressed the question of which combination of channels is optimal in terms of overall accuracy (OA) and mean intersection over union (mIoU). Therefore, a framework is proposed to explore an efficient data fusion approach for semantic segmentation by selecting an optimal combination of data channels. In the framework, a total of 13 channel combinations are investigated to pre-process data and the encoder-to-decoder structure is utilized for network permutations. A case study is carried out to investigate the efficiency of the proposed approach by adopting a city-level benchmark dataset and applying nine networks. It is found that the combination of IRGB channels provide the best OA performance, while IRGBD channels provide the best mIoU performance.

Adaptive data reduction for large-scale transaction data

2008 ◽  
Vol 188 (3) ◽  
pp. 910-924 ◽  
Author(s):  
Xiao-Bai Li ◽  
Varghese S. Jacob
Keyword(s):  
Data Reduction ◽  
Large Scale ◽  
Transaction Data

Energy Efficient Data Collection in Large-Scale Internet of Things via Computation Offloading

2019 ◽  
Vol 6 (3) ◽  
pp. 4176-4187 ◽  
Author(s):  
Guorui Li ◽  
Jingsha He ◽  
Sancheng Peng ◽  
Weijia Jia ◽  
Cong Wang ◽  
...  
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Large Scale ◽  
Computation Offloading ◽  
Efficient Data ◽  
Efficient Data Collection

scholarly journals An efficient data reduction method and its application to cluster analysis

2017 ◽  
Vol 238 ◽  
pp. 234-244 ◽  
Author(s):  
Jianpei Wang ◽  
Shihong Yue ◽  
Xiao Yu ◽  
Yaru Wang
Keyword(s):  
Cluster Analysis ◽  
Data Reduction ◽  
Reduction Method ◽  
Efficient Data ◽  
Data Reduction Method

scholarly journals Error-Controlled Data Reduction Approach for Large-Scale Structured Datasets

2021 ◽  
Vol 33 (12) ◽  
pp. 1795-1802
Author(s):  
Zhiwei Ai ◽  
Juelin Leng ◽  
Fang Xia ◽  
Huawei Wang ◽  
Yi Cao
Keyword(s):  
Data Reduction ◽  
Large Scale ◽  
Reduction Approach

Secure and Privacy Preserving Keyword Search over the Large Scale Cloud Data

2016 ◽  
pp. 200-215
Author(s):  
Wei Zhang ◽  
Jie Wu ◽  
Yaping Lin
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Keyword Search ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Data Utilization ◽  
Cloud Data ◽  
Efficient Resource ◽  
Efficient Data

Cloud computing has attracted a lot of interests from both the academics and the industries, since it provides efficient resource management, economical cost, and fast deployment. However, concerns on security and privacy become the main obstacle for the large scale application of cloud computing. Encryption would be an alternative way to relief the concern. However, data encryption makes efficient data utilization a challenging problem. To address this problem, secure and privacy preserving keyword search over large scale cloud data is proposed and widely developed. In this paper, we make a thorough survey on the secure and privacy preserving keyword search over large scale cloud data. We investigate existing research arts category by category, where the category is classified according to the search functionality. In each category, we first elaborate on the key idea of existing research works, then we conclude some open and interesting problems.

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