Distributed Similarity Queries in Metric Spaces

Yang, Keyu; Ding, Xin; Zhang, Yuanliang; Chen, Lu; Zheng, Baihua; Gao, Yunjun

doi:10.1007/s41019-019-0095-7

Cited by 15 publications

(2 citation statements)

References 42 publications

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“…Existing methods for distributed similarity queries in metric spaces can be partitioned into two categories [18]. The first category utilizes basic metric partitioning principles to distribute the data over the underlying network.…”

Section: Distributed Similarity Querymentioning

confidence: 99%

Scalable Multi-grained Cross-modal Similarity Query with Interpretability

Zhu

Shen

et al. 2021

Data Sci. Eng.

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Cross-modal similarity query has become a highlighted research topic for managing multimodal datasets such as images and texts. Existing researches generally focus on query accuracy by designing complex deep neural network models and hardly consider query efficiency and interpretability simultaneously, which are vital properties of cross-modal semantic query processing system on large-scale datasets. In this work, we investigate multi-grained common semantic embedding representations of images and texts and integrate interpretable query index into the deep neural network by developing a novel Multi-grained Cross-modal Query with Interpretability (MCQI) framework. The main contributions are as follows: (1) By integrating coarse-grained and fine-grained semantic learning models, a multi-grained cross-modal query processing architecture is proposed to ensure the adaptability and generality of query processing. (2) In order to capture the latent semantic relation between images and texts, the framework combines LSTM and attention mode, which enhances query accuracy for the cross-modal query and constructs the foundation for interpretable query processing. (3) Index structure and corresponding nearest neighbor query algorithm are proposed to boost the efficiency of interpretable queries. (4) A distributed query algorithm is proposed to improve the scalability of our framework. Comparing with state-of-the-art methods on widely used cross-modal datasets, the experimental results show the effectiveness of our MCQI approach.

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Section: Distributed Similarity Querymentioning

confidence: 99%

Scalable Multi-grained Cross-modal Similarity Query with Interpretability

Zhu

Shen

et al. 2021

Data Sci. Eng.

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“…All centralized methods, such as XM‐tree, 18 Hollow‐tree, 19 and B‐tree, 20 suffer from a common drawback, namely, the degradation of the efficiency of large‐scale indexing structures. In contrast, and despite their efficiency compared to centralized methods, distributed methods, such as S

^{2}

R‐tree, 21 DAPR‐tree, 22 and ADMS method 23 share the same problems like the location of the data index, the method of accessing the data index, and the method of retrieving the data after indexing.…”

Section: Introductionmentioning

confidence: 99%

Clustering and parallel indexing of big IoT data in the fog‐cloud computing level

Khettabi

Kouahla

Farou

et al. 2022

Trans Emerging Tel Tech

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In recent years, the large amount of heterogeneous data generated by the Internet of Things (IoT) sensors and devices made recording and research tasks much more difficult, and most of the state-of-the-art methods have failed to deal with the new IoT requirements. This article proposes a new efficient method that simplifies data indexing and enhances the quality and velocity of the similarity query search in the IoT environment. In this method, the fog layer was divided into two levels. In the clustering fog level, the incremental density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to separate collected data into clusters in order to minimize data overlap during in parallel indexes construction. Parallelism was also used, in the indexing fog level to speed up the similarity-based search process and speed up the similarity-based search process. The data in each cluster were indexed using our proposed structure called B3CF-tree (binary tree based on containers at the cloud-clusters fog computing level). The objects in the leaf nodes of the B3CF-trees are, finally, stored in the cloud. Using this approach for computing multiple datasets, the retrieve time of the similarity search is significantly reduced. The experimental results showed that the combination of DBSCAN clustering and parallel indexing make the B3CF-trees outperform the latest real data indexing methods. For example, in terms of quality, the B3CF-tree has the smallest number of nodes and leaf nodes. In addition, the use of parallelism during kNN search reduced, significantly, the retrieve time of the similarity query search.

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Exploiting user behavior learning for personalized trajectory recommendations

Pan

Long

et al. 2021

Front. Comput. Sci.

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Distributed Similarity Queries in Metric Spaces

Cited by 15 publications

References 42 publications

Scalable Multi-grained Cross-modal Similarity Query with Interpretability

Scalable Multi-grained Cross-modal Similarity Query with Interpretability

Clustering and parallel indexing of big IoT data in the fog‐cloud computing level

Exploiting user behavior learning for personalized trajectory recommendations

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