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2022 ◽  
Vol 70 (3) ◽  
pp. 4485-4502
Author(s):  
Jin Wang ◽  
Chenchen Han ◽  
Xiaofeng Yu ◽  
Yongjun Ren ◽  
R. Simon Sherratt

Author(s):  
Shanshan Chen ◽  
Zhenyu Ma ◽  
Jun Gao ◽  
Wentao An
Keyword(s):  

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tanvi Chawla ◽  
Girdhari Singh ◽  
Emmanuel S. Pilli

AbstractResource Description Framework (RDF) model owing to its flexible structure is increasingly being used to represent Linked data. The rise in amount of Linked data and Knowledge graphs has resulted in an increase in the volume of RDF data. RDF is used to model metadata especially for social media domains where the data is linked. With the plethora of RDF data sources available on the Web, scalable RDF data management becomes a tedious task. In this paper, we present MuSe—an efficient distributed RDF storage scheme for storing and querying RDF data with Hadoop MapReduce. In MuSe, the Big RDF data is stored at two levels for answering the common triple patterns in SPARQL queries. MuSe considers the type of frequently occuring triple patterns and optimizes RDF storage to answer such triple patterns in minimum time. It accesses only the tables that are sufficient for answering a triple pattern instead of scanning the whole RDF dataset. The extensive experiments on two synthetic RDF datasets i.e. LUBM and WatDiv, show that MuSe outperforms the compared state-of-the art frameworks in terms of query execution time and scalability.


Micromachines ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1014
Author(s):  
Kuo-Ching Tseng ◽  
Hao-Shiang Huang ◽  
Chun-An Cheng

An energy-storage scheme with hierarchical equalization charging topology applied in a series-connected battery system is proposed in this paper. The proposed hierarchical equalization charging topology (HECT), which combines an equalizer-within module (EWM) and an equalizer between the modules (EBM), is able to rapidly achieve charging balance among a large number of cells in battery modules. The EWM is composed of a buck–boost converter, while a flyback converter constitutes the EBM. Besides, the voltage of each cell in battery modules can be accurately monitored by utilizing the proposed HECT control architecture. In addition, fewer circuit elements are required in the proposed battery equalization system and a faster balancing speed can be achieved. Satisfactory experimental results were obtained by using 12 LiFePO4 batteries, and the performance was improved by about 50% in reducing the battery voltage deviation realized in the proposed battery balancing system, which verified the function of the proposed HECT scheme.


2021 ◽  
Vol 742 (1) ◽  
pp. 012015
Author(s):  
Kuo Liu ◽  
Chengpeng Song ◽  
Fangchen Liu ◽  
Bo Chen ◽  
Jiaquan Deng
Keyword(s):  

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hongzhi Li ◽  
Dezhi Han ◽  
Mingdong Tang

With the rapid development of information technology, logistics systems are developing towards intelligence. The Internet of Things (IoT) devices throughout the logistics network could provide strong support for smart logistics. However, due to the limited computing and storage resources of IoT devices, logistics data with user sensitive information are generally stored in a centralized cloud center, which could easily cause privacy leakage. In this paper, we propose Logisticschain, a blockchain-based secure storage scheme for logistics data. In this scheme, the sensing data from IoT devices should be encrypted for fine-grained access control, and a customized blockchain structure is proposed to improve the storage efficiency of systems. Also, an efficient consensus mechanism is introduced to improve the efficiency of the consensus process in the blockchain. Specific to the logistics process, the sensing data generated from IoT devices will be encrypted and aggregated into the blockchain to ensure data security. Moreover, the stored logistics records can be securely audited by leveraging the blockchain network; both IoT data and logistics demands cannot be deleted or tampered to avoid disputes. Finally, we analyze the security and privacy properties of our Logisticschain and evaluate its performance in terms of computational costs by developing an experimental platform.


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