Query on the cloud: improved privacy-preserving k-nearest neighbor classification over the outsourced database

Yang, Kun; Tian, Chengliang; Xian, Hequn; Tian, Weizhong; Zhang, Yan

doi:10.1007/s11280-022-01093-4

Cited by 5 publications

(3 citation statements)

References 41 publications

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“…Queries can encompass a wide range of conditions, joins, aggregations, and other operations, introducing significant uncertainty and potential for error in cardinality estimation. Traditional methods, which often rely on database statistics and synopses such as histograms, struggle to accurately reflect the realities of data and queries [6][7][8]. They frequently make simplifying assumptions about attribute independence and uniform data distribution [9], which can lead to inaccuracies especially in modern databases characterized by large-scale, high-dimensional, and diverse datasets undergoing dynamic changes.…”

Section: Introductionmentioning

confidence: 99%

“…Disjoint (line 5), do nothing. 2. n is included in c (lines [6][7][8]. n is completely located in the subtree at the position of c, and c is used as the root node of a new round of insertion for recursive processing.…”

mentioning

confidence: 99%

“…Ljung-Box is an improvement to the Box-Pierce test, which improves the test effect of Q BP on small samples. The assumption is the same as Q BP , and the difference is that the calculation method of the test quantity is changed to Equation (8).…”

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confidence: 99%

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Cardinality estimation based on QDSPN for embedded databases under dynamic workload

Ding,

Su,

Shen

et al. 2024

Preprint

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Cardinality estimation has been a pivotal and enduring research focus within database query optimization. While significant advancements have been made in estimating cardinalities for both individual tables and complex multi-table joins, there remains a notable gap in research pertaining to embedded database scenarios. Embedded databases are typically characterized by limited resources and a preponderance of dense, short-term hotspot queries. As a result, cardinality estimation within the constraints of embedded databases poses additional complexities and challenges. In this paper, we introduce a novel Query-driven Sum-Product Network (QDSPN), which leverages the capabilities of sum-product networks (SPNs) to learn from historical data and adapt to dynamic workload variations. This approach effectively mitigates the inherent challenges of SPNs, such as false cluster collisions and independence assumption errors, particularly under conditions of strongly correlated data. Furthermore, we propose a two-stage query clustering framework tailored for dynamic workload environments. This framework serves to guide the structural configuration of the sum-product network, enhancing its adaptability and efficiency. We conduct extensive experiments to validate the performance of QDSPN under dynamic workloads. The experimental results demonstrate the evident advantages of the proposed QDSPN, and highlight its potential for widespread adoption in embedded database systems.

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