Parallel computing of KNN Query in road network based on MapReduce

Sun, Hailong; Jing, Weipeng; Hu, Boqi

doi:10.1016/j.procs.2019.01.248

Cited by 5 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research in [ 23 , 24 ] combines MapReduce techniques and deep learning to analyze big data for traffic on a Hadoop big data platform to detect driver violations. In [ 25 , 26 ], MapReduce is used to implement a function that can calculate the shortest path in the road network more quickly and thus achieve faster and more accurate traffic route planning. In general, MapReduce techniques are mainly used to process large-scale datasets for offline analysis.…”

Section: Related Workmentioning

confidence: 99%

A vehicle license plate data access model based on the jump hash consistency algorithm

Wang,

Cheng,

Chen

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

The massive amount of vehicle plate data generated by intelligent transportation systems is widely used in the field of urban transportation information system construction and has a high scientific research and application value. The adoption of big data platforms to properly preserve, process, and exploit these valuable data resources has become a hot research area in recent years. To address the problems of implementing complex multi-conditional comprehensive query functions and flexible data applications in the key–value database storage environment of a big data platform, this paper proposes a data access model based on the jump hash consistency algorithm. Algorithms such as data slice storage and multi-threaded sliding window parallel reading are used to realize evenly distributed storage and fast reading of massive time-series data on clustered data nodes. A comparative analysis of data distribution uniformity and retrieval efficiency shows that the model can effectively avoid generating the cluster hotspot problem, support comprehensive analysis queries with various complex conditions, and maintain high query efficiency by precisely positioning the data storage range and utilizing parallel scan reading.

show abstract

Section: Related Workmentioning

confidence: 99%

A vehicle license plate data access model based on the jump hash consistency algorithm

Wang,

Cheng,

Chen

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The intelligence of machine learning is that it can automatically optimise the computer algorithm according to past experience. Some scholars have proposed that if the computer system contains machine learning function, it cannot be called an intelligent system [11]. As for machine learning, Sun et al [12] optimised the performance of deep belief networks (DBNs) in analysing large-scale data and proposed a DBN computation model based on MapReduce.…”

Section: Introductionmentioning

confidence: 99%

Adoption of deep learning Markov model combined with copula function in portfolio risk measurement

Liu

Spiridonidis

Abdulrazzqa

2021

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

In order to explore the application of machine learning algorithm to intelligent analysis of big data in an artificial intelligence (AI) environment, make cognitive computing meet the requirements of AI and better assist humans to carry out data analysis, first, the theoretical basis of machine learning algorithm is elaborated. Then, a cognitive computational model based on the machine learning algorithm is proposed, including the essence, principle, function, training method of deep belief network (DBN) algorithm, as well as the joint use of DBN algorithm and multilayer perceptron. Finally, the proposed algorithm is simulated. The results show that under the same parameter conditions, the accuracy rate of the DBN algorithm combined with multilayer perceptron is higher than that of the DBN algorithm; when the number of units is >40, the accuracy rate of the DBN algorithm combined with multilayer perceptron is significantly higher than that of the DBN algorithm; when the number of units is 30, the best effect can be obtained, and the error rate is <0.05, but the DBN algorithm cannot achieve this effect alone; when the number of network layers is specified as four, the error rate of the DBN algorithm combined with multilayer perceptron is <0.05, forming the optimal level. In the AI environment, the performance of the cognitive computational model based on the DBN algorithm and multilayer perceptron can reach the highest level, which makes the computer become a handy intelligent auxiliary tool for human beings.

show abstract

Micro-sized parallel system design proposal for the solution of robotics based engineering problem

Dereli

2021

Microsyst Technol

View full text Add to dashboard Cite

Parallel computing of KNN Query in road network based on MapReduce

Cited by 5 publications

References 9 publications

A vehicle license plate data access model based on the jump hash consistency algorithm

A vehicle license plate data access model based on the jump hash consistency algorithm

Adoption of deep learning Markov model combined with copula function in portfolio risk measurement

Micro-sized parallel system design proposal for the solution of robotics based engineering problem

Contact Info

Product

Resources

About