Learned Metric Index — Proposition of learned indexing for unstructured data

Antol, Matej; Oľha, Jaroslav; Slanináková, Terézia; Dohnal, Vlastislav

doi:10.1016/j.is.2021.101774

Cited by 19 publications

(11 citation statements)

References 12 publications

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“…Following the architectural design of RMI, we proposed the Learned metric index (LMI) [1], which can use a series of arbitrary machine learning models to solve the classification problem by learning a pre-defined partitioning scheme. This was later extended to a fully unsupervised (data-driven) version introduced in [18], which is utilized in this work.…”

Section: Related Workmentioning

confidence: 99%

“…Notably, in 2018, Kraska et al [11] suggested that all conventional index structures could be viewed as models of data distributions, implying that machine and deep learning models could be used in their place. Even though the idea was originally proposed and tested on structured data, this reframing of the problem has already inspired similar work in the realm of unstructured datasets [1][7] [20].…”

Section: Introductionmentioning

confidence: 99%

“…We present a simple pipeline where protein structures are first transformed into short vectors and used to train multiple partitioning and classification models -these are linked together to form a learned index structure. The index then answers queries by returning several candidate leaf nodes, and filtering the objects stored therein using basic vector (similarity) functions 1 .…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the search space to a small number of candidate protein structures, we used the Learned Metric Index (LMI), a tree index structure where each internal node is a learned model trained on a sub-section of data assigned to it by its parent node [1]. Specifically, we used the data-driven version of LMI, where the partitioning is determined in an unsupervised manner.…”

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confidence: 99%

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Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques

Oľha¹,

Slanináková²,

Martin³

et al. 2022

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Despite the constant evolution of similarity searching research, it continues to face the same challenges stemming from the complexity of the data, such as the curse of dimensionality and computationally expensive distance functions. Various machine learning techniques have proven capable of replacing elaborate mathematical models with combinations of simple linear functions, often gaining speed and simplicity at the cost of formal guarantees of accuracy and correctness of querying. The authors explore the potential of this research trend by presenting a lightweight solution for the complex problem of 3D protein structure search. The solution consists of three steps -(i) transformation of 3D protein structural information into very compact vectors, (ii) use of a probabilistic model to group these vectors and respond to queries by returning a given number of similar objects, and (iii) a final filtering step which applies basic vector distance functions to refine the result.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques

Oľha¹,

Slanináková²,

Martin³

et al. 2022

Preprint

Self Cite

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“…Image mining is an important method to extract feature vectors from images that can reflect the hidden content of images [1], so as to analyze the association of image feature vectors, realize image classification and analysis, and improve the applicability of images. At present, data mining methods are often used in structural data mining, and image data belongs to unstructured data [2]. Structured data is simply a database.…”

Section: Introductionmentioning

confidence: 99%

Anatomical mining method of cervical nerve root syndrome under visual sensing technology

2022

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: The gray resolution of anatomical image of cervical nerve root syndrome is low, that can not be mined accurately. OBJECTIVES: Aiming at the defect of low gray resolution of anatomical images, an image mining method using visual perception technology was studied. METHODS: According to the visual perception technology, the internal parameter matrix and external parameter matrix of binocular visual camera were determined by coordinate transformation, and the anatomical images of cervical nerve root syndrome were collected. The collected images are smoothed and enhanced by nonlinear smoothing algorithm and multi-scale nonlinear contrast enhancement method. The directional binary simple descriptor method is selected to extract the features of the enhanced image; Using K-means clustering algorithm, the anatomical image mining of cervical nerve root syndrome is completed by obtaining the initial clustering center and image mining. RESULTS: Experimental results show that the information entropy of the images mined by the proposed method is higher than 5, the average gradient is greater than 7, the edge information retention is greater than 0.7, the peak signal-to-noise ratio is higher than 30 dB, and the similarity of the same category of images is greater than 0.9. CONCLUSIONS: This method can effectively mine the anatomical images of cervical nerve root syndrome and provide an important basis for the diagnosis and treatment of cervical nerve root syndrome.

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