A PID-Based kNN Query Processing Algorithm for Spatial Data

Qiao, Baiyou; Li, Ma; Chen, Linlin; Hu, Bing

doi:10.3390/s22197651

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…Therefore, a method of adjusting the length of the DAVAR window based on the adaptive PID principle is proposed. The length of the truncation window is adjusted adaptively to achieve better tracking ability and ideal variance confidence [25,26].…”

Section: Dynamic Allan Variance Based On Adaptive Pid Principle 41 Pi...mentioning

confidence: 99%

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Adaptive Dynamic Analysis of MEMS Gyroscope Random Noise Based on PID-DAVAR

Zhang

et al. 2023

Micromachines

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As a MEMS gyroscope is susceptible to environmental interference, its performance is degraded due to random noise. Accurate and rapid analysis of random noise of MEMS gyroscope is of great significance to improve the gyroscope’s performance. A PID-DAVAR adaptive algorithm is designed by combining the PID principle with DAVAR. It can adaptively adjust the length of the truncation window according to the dynamic characteristics of the gyroscope’s output signal. When the output signal fluctuates drastically, the length of the truncation window becomes smaller, and the mutation characteristics of the intercepted signal are analyzed detailed and thoroughly. When the output signal fluctuates steadily, the length of the truncation window becomes larger, and the intercepted signals are analyzed swiftly and roughly. The variable length of the truncation window ensures the confidence of the variance and shortens the data processing time without losing the signal characteristics. Experimental and simulation results show that the PID-DAVAR adaptive algorithm can shorten the data processing time by 50%. The tracking error of the noise coefficients of angular random walk, bias instability, and rate random walk is about 10% on average, and the minimum error is about 4%. It can accurately and promptly present the dynamic characteristics of the MEMS gyroscope’s random noise. The PID-DAVAR adaptive algorithm not only satisfies the requirement of variance confidence but also has a good signal-tracking ability.

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Section: Dynamic Allan Variance Based On Adaptive Pid Principle 41 Pi...mentioning

confidence: 99%

“…The deviation index ε k is calculated by Equation (26). The deviation index ε k can be regarded as e(k) in Equation (25). The difference between ε k and ε k−1 is multiplied by K p as the proportional term of the output of the PID-DAVAR adaptive algorithm.…”

Section: Pid-davar Adaptive Algorithmmentioning

confidence: 99%

Adaptive Dynamic Analysis of MEMS Gyroscope Random Noise Based on PID-DAVAR

Zhang

et al. 2023

Micromachines

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“…In [29], the authors presented a PID-based kNN query processing method (PIDKNN) for geographic large data using Spark, bringing proportional integral derivative (PID) control technology into kNN query processing. This approach employs a grid partition technique to split the whole data space into uniform grid cells, after which a grid-based index is built.…”

Section: Related Workmentioning

confidence: 99%

Big Data Clustering Using Chemical Reaction Optimization Technique: A Computational Symmetry Paradigm for Location-Aware Decision Support in Geospatial Query Processing

et al. 2022

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The emergence of geospatial big data has opened up new avenues for identifying urban environments. Although both geographic information systems (GIS) and expert systems (ES) have been useful in resolving geographical decision issues, they are not without their own shortcomings. The combination of GIS and ES has gained popularity due to the necessity of boosting the effectiveness of these tools in resolving very difficult spatial decision-making problems. The clustering method generates the functional effects necessary to apply spatial analysis techniques. In a symmetric clustering system, two or more nodes run applications and monitor each other simultaneously. This system is more efficient than an asymmetric system since it utilizes all available hardware and does not maintain a node in a hot standby state. However, it is still a major issue to figure out how to expand and speed up clustering algorithms without sacrificing efficiency. The work presented in this paper introduces an optimized hierarchical distributed k-medoid symmetric clustering algorithm for big data spatial query processing. To increase the k-medoid method’s efficiency and create more precise clusters, a hybrid approach combining the k-medoid and Chemical Reaction Optimization (CRO) techniques is presented. CRO is used in this approach to broaden the scope of the optimal medoid and improve clustering by obtaining more accurate data. The suggested paradigm solves the current technique’s issue of predicting the accurate clusters’ number. The suggested approach includes two phases: in the first phase, the local clusters are built using Apache Spark’s parallelism paradigm based on their portion of the whole dataset. In the second phase, the local clusters are merged to create condensed and reliable final clusters. The suggested approach condenses the data provided during aggregation and creates the ideal clusters’ number automatically based on the dataset’s structures. The suggested approach is robust and delivers high-quality results for spatial query analysis, as shown by experimental results. The proposed model reduces average query latency by 23%.

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A PID-Based kNN Query Processing Algorithm for Spatial Data

Cited by 2 publications

References 26 publications

Adaptive Dynamic Analysis of MEMS Gyroscope Random Noise Based on PID-DAVAR

Adaptive Dynamic Analysis of MEMS Gyroscope Random Noise Based on PID-DAVAR

Big Data Clustering Using Chemical Reaction Optimization Technique: A Computational Symmetry Paradigm for Location-Aware Decision Support in Geospatial Query Processing

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