Indexing Method Research Articles

Reindeer grazing reduces climate‐driven vegetation changes and shifts trophic interactions in the Fennoscandian tundra

Herbivores drive shifts in plant species composition by interacting with vegetation through defoliation, trampling and nutrient addition: urine and faeces. As herbivore effects on vegetation accumulate over time, they might spillover to other trophic levels, but how and when this happens is poorly understood. Since it is methodologically demanding to measure biodiversity across spatial gradients, an alternative approach is to assess it through biodiversity indices of vascular plants. We employed the Index of biodiversity relevance developed for Swedish flora which provides an estimated number of organisms associated with a plant species, allowing the quantification of trophic community size. Values from this index were coupled with vegetation data from a network of 96 fenced and paired grazed plots across Fennoscandia. We analysed the role herbivory has on plant richness and diversity, and on the number of organisms that interact with the vegetation according to the index values. We also explored how herbivores influence the competitive effects of tall shrubs on other plants since the dominance of a vegetation type links directly to biodiversity. Plant diversity had no clear response to grazing. Overall vegetation and the vegetation subgroups herbs and non‐fruit shrubs had higher biodiversity index values in fenced plots, indicating a higher number of plant–host interactions. Herb cover was negatively related to shrubs in both treatments but with a faster decline in the absence of herbivores. This study highlights the importance of maintaining herbivore populations in the Arctic to conserve the vegetation structure and biodiversity of the tundra. This method of coupling biodiversity indexes with vegetation data provides complementary information to the plant diversity, especially when methodological or time constraints prevent complete field inventories.

Similarity search on social networks with incremental graph indexing based on probabilistic inference

PurposeThis paper aims to propose an incremental graph indexing method based on probabilistic inferences in Bayesian network (BN) for approximate nearest neighbor search (ANNS) that adds unindexed queries into the graph index incrementally.Design/methodology/approachThis paper first uses the attention mechanism based graph convolutional network to embed a social network into the low-dimensional vector space, which could improve the efficiency of graph index construction. To add the unindexed queries into the graph index incrementally, this study proposes to learn the rule-based BN from social interactions. Thus, the dependency relations of unindexed queries and their neighbors are represented, and the probabilistic inferences in BN are then performed.FindingsExperimental results demonstrate that the proposed method improves the search precision by at least 5% and search efficiency by 10% compared to the state-of-the-art methods.Originality/valueThis paper proposes a novel method to construct the incremental graph index based on probabilistic inferences in BN, such that both indexed and unindexed queries in ANNS could be addressed efficiently.

Hierarchical Indexing and Compression Method with AI-Enhanced Restoration for Scientific Data Service

In the process of data services, compressing and indexing data can reduce storage costs, improve query efficiency, and thus enhance the quality of data services. However, different service requirements have diverse demands for data precision. Traditional lossy compression techniques fail to meet the precision requirements of different data due to their fixed compression parameters and schemes. Additionally, error-bounded lossy compression techniques, due to their tightly coupled design, cannot achieve high compression ratios under high precision requirements. To address these issues, this paper proposes a lossy compression technique based on error control. Instead of imposing precision constraints during compression, this method first uses the JPEG compression algorithm for multi-level compression and then manages data through a tree-based index structure to achieve error control. This approach satisfies error control requirements while effectively avoiding tight coupling. Additionally, this paper enhances data restoration effects using a deep learning network and provides a range query processing algorithm for the tree-based index to improve query efficiency. We evaluated our solution using ocean data. Experimental results show that, while maintaining data precision requirements (PSNR of at least 39 dB), our compression ratio can reach 64, which is twice that of the SZ compression algorithm.

Influence of automated indexing in Medical Subject Headings (MeSH) selection for pharmacy practice journals

BackgroundThe Medical Subject Headings (MeSH) thesaurus is the controlled vocabulary used to index articles in MEDLINE. MeSH were mainly manually selected until June 2022 when an automated algorithm, the Medical Text Indexer (MTI) automated was fully implemented. A selection of automated indexed articles is then reviewed (curated) by human indexers to ensure the quality of the process. ObjectiveTo describe the association of MEDLINE indexing methods (i.e., manual, automated, and automated + curated) on the MeSH assignment in pharmacy practice journals compared with medical journals. MethodsOriginal research articles published between 2016 and 2023 in two groups of journals (i.e., the Big-five general medicine and three pharmacy practice journals) were selected from PubMed using journal-specific search strategies. Metadata of the articles, including MeSH terms and indexing method, was extracted. A list of pharmacy-specific MeSH terms had been compiled from previously published studies, and their presence in pharmacy practice journal records was investigated. Using bivariate and multivariate analyses, as well as effect size measures, the number of MeSH per article was compared between journal groups, geographic origin of the journal, and indexing method. ResultsA total of 8479 original research articles was retrieved: 6254 from the medical journals and 2225 from pharmacy practice journals. The number of articles indexed by the various methods was disproportionate; 77.8 % of medical and 50.5 % of pharmacy manually indexed. Among those indexed using the automated system, 51.1 % medical and 10.9 % pharmacy practice articles were then curated to ensure the indexing quality. Number of MeSH per article varied among the three indexing methods for medical and pharmacy journals, with 15.5 vs. 13.0 in manually indexed, 9.4 vs. 7.4 in automated indexed, and 12.1 vs. 7.8 in automated and then curated, respectively. Multivariate analysis showed significant effect of indexing method and journal group in the number of MeSH attributed, but not the geographical origin of the journal. ConclusionsArticles indexed using automated MTI have less MeSH than manually indexed articles. Articles published in pharmacy practice journals were indexed with fewer number of MeSH compared with general medical journal articles regardless of the indexing method used.

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume of trajectory data to facilitate efficient trajectory retrieval and access. It is difficult for existing indexing methods that primarily rely on data-driven indexing structures (such as R-Tree) or space-driven indexing structures (such as Quadtree) to support efficient analysis and computation of data based on spatio-temporal range queries as a service basis, especially when applied to massive trajectory data. In this study, we propose a massive GPS data storage and indexing method based on uneven spatial segmentation and trajectory optimization segmentation. Primarily, the method divides GPS trajectories in a large spatio-temporal data space into multiple MBR sequences by greedy algorithm. Then, a hybrid indexing model for segmented trajectories is constructed to form a global spatio-temporal segmentation scheme, called HHBITS index, to achieve hierarchical organization of trajectory data. Eventually, a spatio-temporal range query processing method is proposed based on this index. This paper implements and evaluates the index in MongoDB and compares it with two other spatio-temporal composite indexes for performing spatio-temporal range queries efficiently. The experimental results show that the method in this paper has high performance in responding to spatio-temporal queries on large-scale trajectory data.

Incremental Sliding Window Connectivity over Streaming Graphs

We study index-based processing for connectivity queries within sliding windows on streaming graphs. These queries, which determine whether two vertices belong to the same connected component, are fundamental operations in real-time graph data processing and demand high throughput and low latency. While indexing methods that leverage data structures for fully dynamic connectivity can facilitate efficient query processing, they encounter significant challenges with deleting expired edges from the window during window updates. We introduce a novel indexing approach that eliminates the need for physically performing edge deletions. This is achieved through a unique bidirectional incremental computation framework, referred to as the BIC model. The BIC model implements two distinct incremental computations to compute connected components within the window, operating along and against the timeline, respectively. These computations are then merged to efficiently compute queries in the window. We propose techniques for optimized index storage, incremental index updates, and efficient query processing to improve BIC effectiveness. Empirically, BIC achieves a 14× increase in throughput and a reduction in P95 latency by up to 3900× when compared to state-of-the-art indexes.

Enhancing data retrieval efficiency in large-scale JavaScript object notation datasets by using indexing techniques

<div class="translate-tooltip-mtz green sm-root translate hidden_translate"><div class="header-wrapper"><div class="header-controls"><span>The use of JavaScript Object Notation (JSON) format as a Not only Structured Query Language (NoSQL) storage solution has grown in popularity, but has presented technical challenges, particularly in indexing large-scale JSON files. This has resulted in slow data retrieval, especially for larger datasets. In this study, we propose the use of JSON datasets to preserve data in resource survey processes. We conducted experiments on a 32-gigabyte dataset containing 1,000,000 transactions in JSON format and implemented two indexing methods, dense and sparse, to improve retrieval efficiency. Additionally, we determined the optimal range of segment sizes for the indexing methods. Our findings revealed that adopting dense indexing reduced data retrieval time from 15,635 milliseconds to 55 milliseconds in one-to-one data retrieval, and from 38,300 milliseconds to 1 millisecond in the absence of keywords. In contrast, using sparse indexing reduced data retrieval time from 33,726 milliseconds to 36 milliseconds in one-to-many data retrieval and from 47,203 milliseconds to 0.17 milliseconds when keywords were not found. Furthermore, we discovered that the optimal segment size range was between 20,000 and 200,000 transactions for both dense and sparse indexing.</span></div></div></div>

The Effect of Senegal River Irrigation Water Quality on Soil Salinization: A Study of the Main Canal of the M’Pourie Plain in Mauritania

In this study, the Senegal River, being the main source of water, plays a crucial role in the area’s agricultural development. Irrigation on the M’Pourie plain using water from the Senegal River is carried out without any prior sanitation control. An evaluation of the quality of irrigation water and its impact on soil salinization in different agricultural plots soil salinity is crucial for the effective utilization of traditional irrigation water over extended periods. Comprehensive physico-chemical analyses were conducted across nine locations on the M’Pourie plain in Rosso during the dynamic seasons of 2021-2023. Nevertheless, a relatively small number of studies have employed soil salinity indexing methods to examine the consequences of river irrigation on soil salinity. The analysis and interpretation of the results obtained were based both on classic methods (average and correlations) and more advanced techniques such as principal component analysis (PCA) and the Piper diagram which allow characterization and a spatial typology of water. Analysis of the Piper diagram highlights the distinction between two groups of water, weakly and moderately mineralized, ranging from 52.22 μS.cm-1 in the dry season to 72.22 μS.cm-1 in the rainy season, presenting a sodium-potassium bicarbonate facies The variability of irrigation water supplies, proves to be important in the functioning of an agro-systems. Two modes of operation have become individualized: the dry phase mode, characterized by very strong mineralization of the water linked to a significant load of dissolved elements, and the wet phase mode, whose water quality is poorly mineralized but shows the impact that its irrigation water can represent in the loading of organic and mineral pollution and the need for strict control of these waters upstream before their agricultural use. The results of this study show the absence of risks of soil salinization in relation to the chemical nature of irrigation water and the impact of agriculture on the M’Pourie plain.

K-dStHash tree for indexing big spatio-temporal datasets

Today’s era is witness of tremendous ever growing spatial, temporal and spatiotemporal data. The huge spatio-temporal data immensely pushes the need for design and development of novel methods tailored for indexing spatio-temporal data. In this research paper, we propose the design of a novel spatio-temporal data indexing method, named as k-dStHash. We have proposed the algorithm k-dStHashInsertion for inserting spatio-temporal objects and an algorithm k-dStHashSrchPlaceTime has been used to search for the objects at given location and time. It is able to handle datasets with duplicate keys which has been ignored in many research works. Though the algorithm k-dStHashInsertion takes 1.3-1.5 times longer time to insert data in k-dStHash data structure as it needs to find a specific location to organize data efficiently, but when it comes to search for required records it is even more than 90 times faster when analyzed in comparison to brute force method. It is generalized enough to organize any kind of k-dimensional data and time-based data also including object finding, fleet management, clustering, leader identification, nearest neighbor, human/animal tracking, path finding and many more.

NIPT-PG: empowering non-invasive prenatal testing to learn from population genomics through an incremental pan-genomic approach.

Non-invasive prenatal testing (NIPT) is a quite popular approach for detecting fetal genomic aneuploidies. However, due to the limitations on sequencing read length and coverage, NIPT suffers a bottleneck on further improving performance and conducting earlier detection. The errors mainly come from reference biases and population polymorphism. To break this bottleneck, we proposed NIPT-PG, which enables the NIPT algorithm to learn from population data. A pan-genome model is introduced to incorporate variant and polymorphic loci information from tested population. Subsequently, we proposed a sequence-to-graph alignment method, which considers the read mis-match rates during the mapping process, and an indexing method using hash indexing and adjacency lists to accelerate the read alignment process. Finally, by integrating multi-source aligned read and polymorphic sites across the pan-genome, NIPT-PG obtains a more accurate z-score, thereby improving the accuracy of chromosomal aneuploidy detection. We tested NIPT-PG on two simulated datasets and 745 real-world cell-free DNA sequencing data sets from pregnant women. Results demonstrate that NIPT-PG outperforms the standard z-score test. Furthermore, combining experimental and theoretical analyses, we demonstrate the probably approximately correct learnability of NIPT-PG. In summary, NIPT-PG provides a new perspective for fetal chromosomal aneuploidies detection. NIPT-PG may have broad applications in clinical testing, and its detection results can serve as a reference for false positive samples approaching the critical threshold.

MEM-based pangenome indexing for k-mer queries.

Pangenomes are growing in number and size, thanks to the prevalence of high-quality long-read assemblies. However, current methods for studying sequence composition and conservation within pangenomes have limitations. Methods based on graph pangenomes require a computationally expensive multiple-alignment step, which can leave out some variation. Indexes based on k-mers and de Bruijn graphs are limited to answering questions at a specific substring length k. We present Maximal Exact Match Ordered (MEMO), a pangenome indexing method based on maximal exact matches (MEMs) between sequences. A single MEMO index can handle arbitrary-length queries over pangenomic windows. MEMO enables both queries that test k-mer presence/absence (membership queries) and that count the number of genomes containing k-mers in a window (conservation queries). MEMO's index for a pangenome of 89 human autosomal haplotypes fits in 2.04 GB, 8.8× smaller than a comparable KMC3 index and 11.4× smaller than a PanKmer index. MEMO indexes can be made smaller by sacrificing some counting resolution, with our decile-resolution HPRC index reaching 0.67 GB. MEMO can conduct a conservation query for 31-mers over the human leukocyte antigen locus in 13.89 seconds, 2.5x faster than other approaches. MEMO's small index size, lack of k-mer length dependence, and efficient queries make it a flexible tool for studying and visualizing substring conservation in pangenomes.

Efficient and robust 3D reconstruction: Absolute phase extraction algorithm based on three gray images

The structured light three-dimensional (3D) measurement technology based on optical metrics has gained increasing applications, and there is a growing demand for capturing transient trajectory changes in dynamic scenes. Conventional temporal phase unwrapping (TPU) approaches often require multi-frequency fringe patterns for application. However, the inevitable conflict arises between the number of projected fringe patterns and the measurement speed. This paper proposes a highly accurate and robust 3D measurement method using only 3-step phase-shifting fringe patterns. To precisely reconstruct the 3D shape using the fewest fringe patterns, it is essential to extract as much information as possible from the available patterns. Therefore, we calculate the wrapped phase distribution using 3-step phase-shifting fringe patterns to ensure accuracy. Simultaneously, the trapezoidal phase-shifting method is employed to calculate the intensity ratio distribution. Through the proposed region-based indexing method, the intensity ratio map is transformed into an unwrapped phase map. Additionally, a novel self-correction method is introduced to correct the non-linear errors in the unwrapped phase map, which is then utilized for the calculation of the fringe order required for phase unwrapping. This high-speed measurement method utilizes only 3-step phase-shifting fringe patterns while retaining the advantages of TPU, meeting the requirements of high-speed and full-field measurements. Experimental results on both static and dynamic scenes validate the effectiveness and versatility of the proposed method, demonstrating its flexibility and simplicity in application. In our 3D measurement system, the method achieves 3D reconstruction of the dynamic object's trajectory at a measurement speed of 107 frames per second.

Single Image Integrated Deblurring Algorithm in Non-Uniform Environment

Motion blurriness in an image caused by camera shake during exposure is unavoidable. It could lead to information loss and degradation in the image quality. Therefore, many researchers are dedicated to developing image deblurring techniques to recover clear images from blurred images. During the deblurring process, structural edges in images play a vital part in estimating the blur kernels. For images with rich textures, fine-scale edges become more apparent. This will cause vagueness in the image’s structural edges and affect the accuracy of the kernel estimation process. In this study, we propose a single-image motion deblurring by kernel estimation method combined with L0-Regularized Intensity and Gradient Prior, and enhanced Scale Aware Smoothing methods. Two types of non-uniform datasets are used, which are real and synthetic. While synthetic datasets are utilized to assess the consistency in performance across real and synthetic images, real datasets are used to portray the level of detail and variation of actual blurred images. The dataset is divided into five categories (people, nature, manmade, text, and night light). Two image quality metrics were selected: full-reference assessment, including learned perceptual image patch similarity (LPIPS), peak signal-to-noise ratio (PSNR), and structural similarity indexing method (SSIM) for synthetic datasets, and no-reference assessments, including blind/reference-less image spatial quality (BRISQUE), natural image quality evaluator (NIQE) and perception-based image quality evaluator (PIQE) for real datasets. According to the findings, the fusion method performs best in the text category, followed by manmade and in nature, then night light and poor in people. The proposed method not only removes fine-scale edges and preserves the boundary sharpness, but also improved the estimated blur kernel.

A standard ranking algorithm for robust iris template protection

In iris biometric recognition systems, protecting the storage and transmission of iris templates is crucial, and template protection techniques are pivotal for ensuring their security. A prevalent approach involves using indexing methods as an effective algorithm for iris template protection, leveraging the index or rank of the extracted iris code to generate a secure iris template. Meantime, many privacy threats to biometric data have emerged, necessitating heightened protection measures. Specifically, protecting the privacy of iris data is imperative within the context of iris template protection during recognition processes. As stipulated by the international standard ISO/IEC 30136, effective iris template protection must concurrently meet the criteria of irreversibility, revocability, and unlinkability. Nevertheless, existing indexing methods on iris template protection faced the formidable challenge of simultaneously fulfilling these three privacy requirements while maintaining the efficacy of iris recognition. This paper introduces a standard ranking (standardR) algorithm, named standardR, designed to enhance the security of iris templates by transforming each iris template into an irreversible representation. The experimental results on the benchmarked Casia-Iris-interval dataset, along with two additional iris datasets MMU1 and UBRIS 1, demonstrate the efficacy of the proposed algorithm. The proposed standardR algorithm achieves an equal error rate (EER) of 0.1695% and an area under the curve of 0.93011% with the Casia-Iris-Interval dataset. Furthermore, the algorithm maintains efficient recognition with a reduced iris code length of 1280 bits, a time complexity of O(n log n), and satisfies the biometric template protection (BTP) requirements in irreversibility, unlinkability, and renewability.

Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry.

Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.

Study of Air Ordering Procedure for Information Broadcasting

Wireless data broadcasting is a new way to share public information with many mobile users. To measure how well these systems work, we look at two main things: how long it takes to access the information (access latency) and how long the device has to stay active (tuning time). Indexing technology helps reduce tuning time by letting devices find information quickly and then switch to a low-power mode while waiting. Different indexing methods work better or worse, so comparing them can be hard. In this paper, I look at and compare some popular indexing methods for wireless data broadcasting, such as distributed index, exponential index, hash table, and Huffman tree index. I use probability theory to analyze how each method performs. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6393

Abstract LB234: Identifying cancer vulnerabilities associated with changes in chromatin accessibility by simultaneously profiling hundreds of cancer cell lines with ATAC-seq

Abstract The goal of the Cancer Dependency Map project (DepMap) is to identify the landscape of cancer vulnerabilities by systematically profiling hundreds of cell lines representing the heterogeneity of human cancer. Cancer cells are known to undergo significant alterations in the cancer genome including changes in the accessible chromatin regions (ACRs). These alterations could lead to novel cancer vulnerabilities but chromatin accessibility has not been systematically profiled in DepMap. Profiling hundreds of cell lines individually for ACRs would require large resources. Here we show that we can efficiently profile hundreds of cell lines using two single-cell approaches developed by our labs. SHARE-seq is a single-cell high-throughput combinatorial indexing method to measure both chromatin accessibility (scATAC-seq), and gene expression (scRNA-seq) in the same single cell. MIX-Seq is an approach that leverages the ability to pool hundreds of unique cancer cell lines and then apply single-cell genomics across cell populations from each cell line, resolving its identity using single nucleotide polymorphism (SNP) profiles. Here, we used a combination of MIX-seq (pooling cell lines) and SHARE-seq (scATAC-seq and RNA-seq) to simultaneously profile 500 unique cell lines covering 110 lineages within 22 cancer types at the single-cell level. Encouragingly, in a UMAP representation of the data, cancer cell lines cluster by lineage. Interestingly, we identified hundreds of thousands of peaks within cis-regulatory elements (CREs), 1/3 of which are unique to cancer cell lines and not present in normal cell types. Using published tools, we calculated an ATAC-seq gene activity prediction score per cell line. To identify cancer vulnerabilities associated with ACRs changes, we combined the ATAC-seq gene-activity predictions with RNAseq-based features to construct machine-learning models of cancer dependency scores. We found that the prediction accuracy for multiple dependencies improved with the addition of the ATAC-seq-based scores, indicating that chromatin accessibility provides information beyond gene expression. Additional analyses are underway to create other features. Overall, we have generated a large high-quality dataset to simultaneously map the chromatin accessibility in hundreds of cancer cell lines. Importantly, the chromatin accessibility adds to other omics profiling to identify biomarkers of response and understand how changes in chromatin can lead to new vulnerabilities. Citation Format: Patricia Borck, Kirsty Wienand, Fabiana Duarte, Alvin Qin, Ruochi Zhang, Juliana Babu, Simone Zhang, Samuel Maffa, Max Horlbeck, Rojesh Shrestha, Joshua Dempster, Jennifer Roth, Catarina Campbell, Jason Buenrostro, Francisca Vazquez. Identifying cancer vulnerabilities associated with changes in chromatin accessibility by simultaneously profiling hundreds of cancer cell lines with ATAC-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB234.

Reference Values for Indexed Echocardiographic Chamber Sizes in Older Adults: The Multi-Ethnic Study of Atherosclerosis.

Normalization of echocardiographic chamber measurements for body surface area may result in misclassification of individuals with obesity or sarcopenia. Normalization for alternative measures of body size may be preferable, but there remains a dearth of information on their normative values and association with cardiovascular function metrics. A total of 3032 individuals underwent comprehensive 2-dimensional echocardiography at Exam 6 in MESA (Multi-Ethnic Study of Atherosclerosis). In the subgroup of 608 individuals free of cardiopulmonary disease (69.5±7.0 years, 46% male, 48% White, 17% Chinese, 15% Black, 21% Hispanic), normative values were derived for left and right cardiac chamber measurements across a variety of ratiometric (body surface area, body mass index, height) and allometric (height1.6, height2.7) scaling parameters. Normative upper and lower reference values were provided for each scaling parameter stratified across age groups, sex, and race or ethnicity. Among scaling parameters, body surface area and height were associated with the least variability across race and ethnicity categories and height2.7 was associated with the least variability across sex categories. In this diverse cohort of community-dwelling older adults, we provide normative values for common echocardiographic parameters across a variety of indexation methods.

Scientific and methodological approaches to the creation of electronic library content

The article analyzes the principles of designing and forming digital collections of electronic libraries. Basic principles such as integration, strategic planning, quality assurance of digital content and its scientific and methodological support are considered. The need to create an expert council and working groups to determine the profile of electronic libraries and form the content of repositories is substantiated. The need to create an integrated electronic catalog of the cultural heritage of libraries, archives and museums to provide readers with information about the availability of archival documents and museum objects in library funds is emphasized. The main principles of its creation and functioning are given: use of a single data presentation format, use of a single method of description and indexing, use of single authoritative files to ensure control of access points; providing a single search interface. It was concluded that based on the results of theoretical research and practical activities of libraries in the field of digital collection formation, it is advisable to prepare a scientific and practical guide "Formation of digital collections", as well as to develop a program of lectures "Formation and use of digital collections.

Study on Spatio-Temporal Indexing Model of Geohazard Monitoring Data Based on Data Stream Clustering Algorithm

Yunnan Province, residing in the eastern segment of the Qinghai–Tibet Plateau and the western part of the Yunnan–Guizhou Plateau, faces significant challenges due to its intricate geological structures and frequent geohazards. These pose monumental risks to community safety and infrastructure. Unfortunately, conventional spatial indexing methods struggle with the enormous influx of geohazard data, exhibiting inadequacies in efficient spatio-temporal querying and failing to meet the swift response imperatives for real-time geohazard monitoring and early warning mechanisms. In response to these challenges, this study proffers a cutting-edge spatio-temporal indexing model, the BCHR-index, undergirded by data stream clustering algorithms. The operational schema of the BCHR-index model is bifurcated into two stages: real-time and offline. The real-time phase proficiently uses micro-clusters shaped by the CluStream algorithm in unison with a B+ tree to construct indices in memory, thereby satisfying the exigent response necessities for geohazard data streams. Conversely, the offline stage employs the CluStream algorithm and the Hilbert curve to manage heterogeneously distributed spatial objects. Paired with a B+ tree, this framework promotes efficient spatio-temporal querying of geohazard data. The empirical results indicate that the indexing model implemented in this study affords millisecond-level responses when faced with query requests from real-time geohazard data streams. Moreover, in aspects of spatial query efficiency and data-insertion performance, it demonstrates superior results compared to the R-tree and Hilbert-R tree models.