Research and Application of DBSCAN Algorithm Based on Hadoop Platform

2014 ◽  
pp. 73-87 ◽  
Author(s):  
Xiufen Fu ◽  
Yaguang Wang ◽  
Yanna Ge ◽  
Peiwen Chen ◽  
Shaohua Teng
Keyword(s):  
Dbscan Algorithm ◽  
Hadoop Platform

A Defense Mechanism Based on Improved Allocation Strategy of VMs Co-Resident Attack in Power Cloud Platform

2020 ◽  
Vol 13 ◽  
Author(s):  
Yuancheng Li ◽  
Pan Zhang ◽  
Daoxing Li ◽  
Jing Zeng
Keyword(s):  
Virtual Machine ◽  
Simulation Experiment ◽  
Virtual Machines ◽  
Defense Mechanism ◽  
Security Threats ◽  
Allocation Strategy ◽  
Cloud Platform ◽  
Deployment Strategy ◽  
Dbscan Algorithm ◽  
Maximum Minimum

Background: Cloud platform is widely used in electric power field. Virtual machine co-resident attack is one of the major security threats to the existing power cloud platform. Objective: This paper proposes a mechanism to defend virtual machine co-resident attack on power cloud platform. Method: Our defense mechanism uses the DBSCAN algorithm to classify and output the classification results through the random forest and uses improved virtual machine deployment strategy which combines the advantages of random round robin strategy and maximum/minimum resource strategy to deploy virtual machines. Results: we made a simulation experiment on power cloud platform of State Grid and verified the effectiveness of proposed defense deployment strategy. Conclusion: After the virtual machine deployment strategy is improved, the coverage of the virtual machine is remarkably reduced which proves that our defense mechanism achieves some effect of defending the virtual machine from virtual machine co-resident attack.

scholarly journals Massive AIS data storage and query based on Hadoop platform

2021 ◽  
Vol 1948 (1) ◽  
pp. 012016
Author(s):  
Taizhi Lv ◽  
Chenyong He ◽  
Juan Zhang ◽  
Zhiyang Song
Keyword(s):  
Data Storage ◽  
Hadoop Platform

scholarly journals Automatic fuzzy-DBSCAN algorithm for morphological and overlapping datasets

2020 ◽  
Vol 31 (6) ◽  
pp. 1245-1253
Author(s):  
Yelghi Aref ◽  
KoSe Cemal ◽  
Yelghi Asef ◽  
Shahkar Amir
Keyword(s):  
Dbscan Algorithm

scholarly journals Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam

2021 ◽  
Vol 10 (4) ◽  
pp. 198
Author(s):  
Sevim Sezi Karayazi ◽  
Gamze Dane ◽  
Bauke de Vries
Keyword(s):  
Geographically Weighted Regression ◽  
Spatial Clustering ◽  
Least Square ◽  
Geospatial Data ◽  
Evidence Based ◽  
Ordinary Least Square ◽  
Mass Tourism ◽  
Dbscan Algorithm ◽  
National Monuments ◽  
Combined Data

Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding of people’s responses to these places. In this article, the combination of multisource information about national monuments, supporting products (i.e., attractions, museums), and geospatial data are utilized to understand attractive heritage locations and the factors that make them attractive. We retrieved geotagged photographs from the Flickr API, then employed density-based spatial clustering of applications with noise (DBSCAN) algorithm to find clusters. Then combined the clusters with Amsterdam heritage data and processed the combined data with ordinary least square (OLS) and geographically weighted regression (GWR) to identify heritage attractiveness and relevance of supporting products in Amsterdam. The results show that understanding the attractiveness of heritages according to their types and supporting products in the surrounding built environment provides insights to increase unattractive heritages’ attractiveness. That may help diminish the burden of tourism in overly visited locations. The combination of less attractive heritage with strong influential supporting products could pave the way for more sustainable tourism in Amsterdam.

A Fast Fault Diagnosis Method for The Unlabeled Signal Based on Improved PSO-DBSCAN Algorithm

2021 ◽  
Author(s):  
Shijie Wei ◽  
Huina Mu ◽  
Pengbo Zhang ◽  
Xiaojian Yi ◽  
Yuhang Cui
Keyword(s):  
Fault Diagnosis ◽  
Dbscan Algorithm ◽  
Diagnosis Method ◽  
Improved Pso

Intelligent identification of multi-level nanopore signatures for accurate detection of cancer biomarkers

2017 ◽  
Vol 53 (73) ◽  
pp. 10176-10179 ◽  
Author(s):  
Jian-Hua Zhang ◽  
Xiu-Ling Liu ◽  
Zheng-Li Hu ◽  
Yi-Lun Ying ◽  
Yi-Tao Long
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Cancer Biomarkers ◽  
Serum Samples ◽  
Dbscan Algorithm ◽  
Accurate Detection ◽  
Multi Level ◽  
Current Blockage ◽  
Intelligent Recognition

We combined a modified DBSCAN algorithm with the Hidden Markov Model (HMM) for the intelligent recognition of multi-level current blockage events from the measured nanopore data of serum samples.

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