Embedding Index Maintenance in Store Routines to Accelerate Secondary Index Building in HBase

2018 ◽  
Author(s):  
Chun Cao ◽  
Weiyi Wang ◽  
Ying Zhang ◽  
Jian Lu
Keyword(s):  
Index Maintenance ◽  
Secondary Index ◽  
Index Building

Regional Bitmap Index: A Secondary Index for Data Management in Could Computing Environment

2012 ◽  
Vol 35 (11) ◽  
pp. 2306 ◽  
Author(s):  
Bi-Ping MENG ◽  
Teng-Jiao WANG ◽  
Hong-Yan LI ◽  
Dong-Qing YANG
Keyword(s):  
Data Management ◽  
Computing Environment ◽  
Bitmap Index ◽  
Secondary Index

scholarly journals Role of eNOS in water exchange index maintenance-MRI studies

2017 ◽  
Vol 886 ◽  
pp. 012002 ◽  
Author(s):  
D Atochin ◽  
M Litvak ◽  
S Huang ◽  
Y R Kim ◽  
P Huang
Keyword(s):  
Water Exchange ◽  
Mri Studies ◽  
Index Maintenance

Secondary Index

2017 ◽  
pp. 1-2
Author(s):  
Yannis Manolopoulos ◽  
Yannis Theodoridis ◽  
Vassilis J. Tsotras
Keyword(s):  
Secondary Index

scholarly journals Battlefield of global ranking: How do power rivalries shape soft power index building?

2019 ◽  
Vol 4 (2) ◽  
pp. 179-202 ◽  
Author(s):  
Chang Zhang ◽  
Ruiqin Wu
Keyword(s):  
Soft Power ◽  
Power Index ◽  
Cultural Diplomacy ◽  
International Competition ◽  
Power Measurement ◽  
Political Values ◽  
National Image ◽  
Development Models ◽  
Index Building ◽  
Selection Of

International competition over soft power has largely transformed from image promotion and cultural diplomacy to benchmark setting. Benchmarks breed discourses and discourses embody power. The article argues that the soft power index building has turned into a battlefield where different values, norms and development models struggle for legitimacy through quasi-scientific validations. By critically examining the methods employed by two soft power indexes, Portland Soft Power 30 Index and China National Image Global Survey, this article unpacks the mechanisms by which institutions from western and emerging (Brazil, Russia, India, China and South Africa (BRICS)) states embed political values, interests and agendas in the selection of data, indicators and treatments of data. The article finds that while the soft power indexes originating from Western organizations largely normalized liberal values and the current international hierarchy, the Chinese national image survey provides a more self-reflective approach to soft power measurement.

scholarly journals An Efficient 3D Model Retrieval Method Based on Convolutional Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Bo Ding ◽  
Lei Tang ◽  
Yong-jun He
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
3D Model ◽  
3D Models ◽  
3D Model Retrieval ◽  
Similarity Matching ◽  
Retrieval Method ◽  
Model Classification ◽  
Model Retrieval ◽  
Index Building

Recently, 3D model retrieval based on views has become a research hotspot. In this method, 3D models are represented as a collection of 2D projective views, which allows deep learning techniques to be used for 3D model classification and retrieval. However, current methods need improvements in both accuracy and efficiency. To solve these problems, we propose a new 3D model retrieval method, which includes index building and model retrieval. In the index building stage, 3D models in library are projected to generate a large number of views, and then representative views are selected and input into a well-learned convolutional neural network (CNN) to extract features. Next, the features are organized according to their labels to build indexes. In this stage, the views used for representing 3D models are reduced substantially on the premise of keeping enough information of 3D models. This method reduces the number of similarity matching by 87.8%. In retrieval, the 2D views of the input model are classified into a category with the CNN and voting algorithm, and then only the features of one category rather than all categories are chosen to perform similarity matching. In this way, the searching space for retrieval is reduced. In addition, the number of used views for retrieval is gradually increased. Once there is enough evidence to determine a 3D model, the retrieval process will be terminated ahead of time. The variable view matching method further reduces the number of similarity matching by 21.4%. Experiments on the rigid 3D model datasets ModelNet10 and ModelNet40 and the nonrigid 3D model dataset McGill10 show that the proposed method has achieved retrieval accuracy rates of 94%, 92%, and 100%, respectively.

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