A KNN Query Processing Algorithm over High-Dimensional Data Objects in P2P Systems

Qiao, Bo; Ding, Linlin; Wei, Yong; Wang, Xiaoyang

doi:10.1007/978-3-642-28314-7_19

Cited by 3 publications

(6 citation statements)

References 7 publications

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“…Generally, δ is an integer more than 1. Using X(t) in (5) replace X(t) in (6), we would calculate the adjusted decay ratio relationship by (7).…”

Section: B Complex Quantification Methodsmentioning

confidence: 99%

“…WSEPM-KNN [7][8][9] uses the static length of the predication windows to predicate the web services evaluation, while WSEPM-TU uses the dynamic length of the predication windows. This experiment shows the performance of WSEPM-KNN and WSEPM-TU.…”

Section: E Performance Of Various Predication Modelsmentioning

confidence: 99%

“…[4], and K Nearest Neighbor (KNN) in Ref. [7,8,9] fail to analyze the time utility of the historical ratings when evaluating web services. In reality, the recent ratings express more valuable than the ratings in the past.…”

Section: Introductionmentioning

confidence: 99%

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Web Services Evaluation Predication Model based on Time Utility

Yin¹,

Cui²,

Dong³

et al. 2012

JSW

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Current predication models fail to consider the time utility of the web services evaluation predication and treat the different historical ratings in the same way. To solve this problem, we put forward web service evaluation predication model based on time utility. In the model, naïve quantification method and complex quantification method are proposed to achieve the distinct and proper time utility for the services evaluation predication procedure. Then, the quantification results are used to optimizes the length of the predication windows. Also, feedback control strategy is involved to enhance the robust of the model when facing malicious. Experimental results shows our model would calculate the proper time utility and obtain the lower predication error compared with current predication models. Feedback control strategy is an effective method to reduce the impact of malicious ratings and guarantee the lower predication error compared with the model without the feedback control strategy

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“…Generally, δ is an integer more than 1. Using X(t) in (5) replace X(t) in (6), we would calculate the adjusted decay ratio relationship by (7).…”

Section: B Complex Quantification Methodsmentioning

confidence: 99%

Section: E Performance Of Various Predication Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Web Services Evaluation Predication Model based on Time Utility

Yin¹,

Cui²,

Dong³

et al. 2012

JSW

View full text Add to dashboard Cite

show abstract

“…For high-dimensional indexing, the kNN query process is enhanced by using adaptive cluster distance bounds in the study of [ 119 ]. For effective data management and query processing in peer-to-peer systems, the researchers came up with a distributed multidimensional data index (QDBI) [ 125 ] that is based on quad-trees. Here, every peer uses an MX-CIF quad-tree to generate index items for their high-dimensional data.…”

Section: Classification Of Knn Queriesmentioning

confidence: 99%

“…The processing of difficult queries over high-dimensional data items, such as the kNN query, has been the subject of several studies [ 168 , 169 , 170 ]. For effective data management and query optimisation in a P2P system, in this study, researchers present a distributed multi-dimensional data index (QDBI) [ 125 ] which is based on quad-trees. Here, every peer uses an MX-CIF quad-tree to generate an index item for their high-dimensional data.…”

Section: Knn Searchmentioning

confidence: 99%

Survey on Exact kNN Queries over High-Dimensional Data Space

Ukey

Yang

et al. 2023

Sensors

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k nearest neighbours (kNN) queries are fundamental in many applications, ranging from data mining, recommendation system and Internet of Things, to Industry 4.0 framework applications. In mining, specifically, it can be used for the classification of human activities, iterative closest point registration and pattern recognition and has also been helpful for intrusion detection systems and fault detection. Due to the importance of kNN queries, many algorithms have been proposed in the literature, for both static and dynamic data. In this paper, we focus on exact kNN queries and present a comprehensive survey of exact kNN queries. In particular, we study two fundamental types of exact kNN queries: the kNN Search queries and the kNN Join queries. Our survey focuses on exact approaches over high-dimensional data space, which covers 20 kNN Search methods and 9 kNN Join methods. To the best of our knowledge, this is the first work of a comprehensive survey of exact kNN queries over high-dimensional datasets. We specifically categorise the algorithms based on indexing strategies, data and space partitioning strategies, clustering techniques and the computing paradigm. We provide useful insights for the evolution of approaches based on the various categorisation factors, as well as the possibility of further expansion. Lastly, we discuss some open challenges and future research directions.

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Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications

Halder,

Uddin,

Uddin

et al. 2024

J Big Data

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The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performance analysis of modifications made to enhance the exact kNN techniques, particularly focusing on kNN Search and kNN Join for high-dimensional data. We delve deep into 31 kNN search methods and 12 kNN join methods, providing a methodological overview and analytical insight into each, emphasizing their strengths, limitations, and applicability. An important feature of our study is the provision of the source code for each of the kNN methods discussed, fostering ease of experimentation and comparative analysis for readers. Motivated by the rising significance of kNN in high-dimensional spaces and a recognized gap in comprehensive surveys on exact kNN techniques, our work seeks to bridge this gap. Additionally, we outline existing challenges and present potential directions for future research in the domain of kNN techniques, offering a holistic guide that amalgamates, compares, and dissects existing methodologies in a coherent manner. Graphical Abstract

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A KNN Query Processing Algorithm over High-Dimensional Data Objects in P2P Systems

Cited by 3 publications

References 7 publications

Web Services Evaluation Predication Model based on Time Utility

Web Services Evaluation Predication Model based on Time Utility

Survey on Exact kNN Queries over High-Dimensional Data Space

Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications

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