Index Structure Research Articles

Learning Non-monotone Optimal Individualized Treatment Regimes

We propose a new modeling and estimation approach to select the optimal treatment regime from different options through constructing a robust estimating equation. The method is protected against misspecification of the propensity score model, the outcome regression model for the non-treated group, or the potential non-monotonic treatment difference model. Our method also allows residual errors to depend on covariates. A single index structure is incorporated to facilitate the nonparametric estimation of the treatment difference. We then identify the optimal treatment through maximizing the value function. Theoretical properties of the treatment assignment strategy are established. We illustrate the performance and effectiveness of our proposed estimators through extensive simulation studies and a real dataset on the effect of maternal smoking on baby birth weight.

Querying Structural Diversity in Streaming Graphs

Structural diversity of a vertex refers to the diversity of connections within its neighborhood and has been applied in various fields such as viral marketing and user engagement. The paper studies querying the structural diversity of a vertex for any query time windows in streaming graphs. Existing studies are limited to static graphs which fail to capture vertices' structural diversities in snapshots evolving over time. We design an elegant index structure to significantly reduce the index size compared to the basic approach. We propose an optimized incremental algorithm to update the index for continuous edge arrivals. Extensive experiments on real-world streaming graphs demonstrate the effectiveness of our framework.

Efficient Privacy-Preserving Spatial Data Query in Cloud Computing

With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatial data is outsourced to the cloud server for reducing the local high storage and computing burdens, but at the same time causes security issues. Thus, extensive privacy-preserving spatial data query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) to encrypt data, but ASPE has proven to be insecure against known plaintext attack. And the existing schemes require users to provide more information about query range and thus generate a large amount of ciphertexts, which causes high storage and computational burdens. To solve these issues, based on enhanced ASPE designed in our conference version, we first propose a basic Privacy-preserving Spatial Data Query (PSDQ) scheme by using a new unified index structure, which only requires users to provide less information about query range. Then, we propose an enhanced PSDQ scheme (PSDQ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> ) by using Geohash-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R$</tex-math></inline-formula> -tree structure (called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$GR$</tex-math></inline-formula> -tree) and efficient pruning strategy, which greatly reduces the query time. Formal security analysis proves that our schemes achieve Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our schemes are efficient in practice.

MSTGI: a multi-scale spatio-temporal grid index model for remote-sensing big data retrieval

ABSTRACT To promote the transformation of remote sensing (RS) data into geoscience knowledge, it is necessary to provide better data discovery capabilities, especially when large amounts of RS data have been accumulated. Spatio-temporal range query is one of them to enable data discovery on RS images by the spatio-temporal range. Although existing RS image indexing methods are suitable for data discovery based on spatio-temporal range queries, they do not fully consider the problem of low data retrieval efficiency when the temporal and spatial range scales are unbalanced. To address this problem, we propose a multi-scale spatio-temporal grid index model (MSTGI). First, we divide the time dimension into three levels according to the granularity of year, month and day, and build an independent grid index structure for the object at different levels. Second, we design an adaptive hierarchical indexing strategy to perform parallel retrieval in an appropriate combination of partitions. MSTGI linearizes the grid obtained after global geospatial subdivision using Hilbert curves. Our experimental results, obtained on the Landsat series satellite dataset, reveal that the proposed method improves query efficiency levels by approximately 12.582% and 87.754% compared with GeoSOT-ST and AGMC at various spatial scales, respectively, and reaches 100% recall.

Competitiveness of regional labor markets as a determinant of international migration: A nexus empirical study

A large-scale migration from Ukraine caused by russian military aggression has triggered new security challenges to the national and regional economies. The paper aims to assess the competitiveness of regional labor markets and examine its nexus with international out-migration (on the example of the Carpathian region of Ukraine). The research methods include a composite approach (assessment of labor market competitiveness), the theory of sensitivity (identification of degree of sensitivity of composite and partial indices to changes in indicators), nonlinear regression (determination of dependence of the international out-migration on labor market competitiveness), and econometric nonlinear optimization (calculation of critical values of migration). The information and analytical basis comprises indicators reflecting the development of labor markets and international migrations in the oblasts of the Carpathian region in 2008–2021. The study reveals that the level of labor market competitiveness in the region mainly remained below the national average. Two groups of indicators dominated the structure of composite indices of labor market competitiveness in the region’s oblasts: institutional capacity (indicators measuring the ability of regulatory institutions and labor market entities to provide adequate support to the unemployed and promote employment) and labor motivation (wage and income indicators). Only Lviv oblast demonstrated resilience and growth of competitiveness, which was reflected in moderate out-migration. Meanwhile, the intensity of international out-migration in Zakarpattia and Chernivtsi oblasts (with worse labor market parameters) exceeded the estimated critical values. The paper confirms that low competitiveness of the labor market is a factor increasing migration losses. AcknowledgmentThis study has been conducted with the support of the Virtual Ukraine Institute for Advanced Study (VUIAS) Fellowship Programme, 2023/2024.

Visibility-Based R-Tree Spatial Index for Consistent Visualization in Indoor and Outdoor Scenes

(1) Background: The smart city management system, with GIS technology as its core, is based on realistic visualization of multiple types of 3D model data syntheses. However, the efficiency barriers to achieving smooth and continuous visualization from outdoor scenes to small indoor scenes remain a challenge. (2) Methods: This paper uses the visibility prediction method to obtain potential visual sets at three levels—outdoor, indoor and outdoor connection, and indoor—and constructs an R-tree spatial index structure for organizing potential visual sets. By integrating these potential visible sets with spatial indexes, scene visualization can be carried out effectively. (3) Results: A near-reality indoor and outdoor scene was used for experimentation, resulting in stable 10% fluctuation visual frame rates around 90 FPS. (4) Conclusions: Spatial indexing methods that combine potential visible sets can effectively solve the continuity and stability problem of indoor and outdoor scene visualization in smart city management systems.

An efficient visual exploration approach of geospatial vector big data on the web map

The visual exploration of geospatial vector data has become an increasingly important part of the management and analysis of geospatial vector big data (GVBD). With the rapid growth of data scale, it is difficult to realize efficient visual exploration of GVBD by current visualization technologies even if parallel distributed computing technology is adopted. To fill the gap, this paper proposes a visual exploration approach of GVBD on the web map. In this approach, we propose the display-driven computing model and combine the traditional data-driven computing method to design an adaptive real-time visualization algorithm. At the same time, we design a pixel-quad-R tree spatial index structure. Finally, we realize the multilevel real-time interactive visual exploration of GVBD in a single machine by constructing the index offline to support the online computation for visualization, and all the visualization results can be calculated in real-time without the external cache occupation. The experimental results show that the approach outperforms current mainstream visualization methods and obtains the visualization results at any zoom level within 0.5 s, which can be well applied to multilevel real-time interactive visual exploration of the billion-scale GVBD.

SALI: A Scalable Adaptive Learned Index Framework based on Probability Models

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is the learned index, which uses a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04x compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+.

Privacy-Preserving Image Retrieval with Multi-Modal Query

Abstract The ever-growing multi-modal images pose great challenges to local image storage and retrieval systems. Cloud computing provides a solution to large-scale image data storage but suffers from privacy issues and lacks the support for multi-modal image retrieval. To address these, a searchable encryption-empowered privacy-preserving multi-modal image retrieval method is proposed. First, we design a hybrid image retrieval framework that fuses visual features and textual features at a decision level and further supports similar image retrieval and multi-keyword image retrieval. Second, we construct a new hybrid inverted index structure to distinguish high-frequency terms from low-frequency terms and index them through hierarchical index trees and data blocks, respectively, which greatly improves query efficiency. Third, we design a prime encoding-based multi-keyword query method that converts mapping operations in bloom filters into inner product calculations, and further implements secure multi-keyword image query. Experiments against the Baseline schemes are conducted to verify the performance of the scheme in terms of high efficiency.

Validity and reliability evaluation of the Turkish version of the Clinical Interpersonal Reactivity Index.

This study was conducted to evaluate the validity and reliability of the Turkish version of the Clinical Interpersonal Reactivity Index, an instrument to assess nurses' empathy. A practical empathic approach is valuable for planning nursing interventions suitable for the patient's conditions and needs and increasing patient satisfaction. This is a methodological study. The study was carried out at Muğla Sıtkı Koçman University Training and Research Hospital, recruiting a total of 358 nurses between January and February 2023. Exploratory and confirmatory factor analyses were used to determine and validate the factor structure of the Clinical Interpersonal Reactivity Index. The exploratory factor analysis extracted three factors. According to the confirmatory factor analysis, all goodness of fit measures were within acceptable limits. Cronbach's α values were 0.89 for the total scale with 17 items and between 0.73 and 0.81 for the factors. In test-retest analyses, all intra-class correlation coefficients were higher than 0.7, and the correlations between the first and second applications of the scale were between 0.61 and 0.73. The Turkish version of the Clinical Interpersonal Reactivity Index can be applied as a valid and reliable measurement tool to evaluate empathy in Turkish nurses.

Maturation of Female Yellow Rasbora (&lt;i&gt;Rasbora Lateristriata&lt;/i&gt; Bleeker, 1854) Using Oodev at Different Doses in Feed

The current high demand for Yellow rasbora (Rasbora lateristriata) is not supported by the availability of captured Yellow rasbora in nature. Aquaculture is the most rational way of utilizing biological natural resources. In intensive aquaculture, it is necessary to optimize all processes that occur in aquaculture, including hatchery. However, the common problem that often happens in hatchery activities is spawning which depends on the season. The hormonal manipulation technique is an appropriate way to stimulate gonadal maturation. Oodev is a hormonal combination of pregnant mare serum gonadotropin and anti-dopamine to stimulate gonadal maturation. The purpose of this study was to determine the effectiveness of using the Oodev with different doses in feed to accelerate gonad maturation of female Yellow rasbora. The study was carried out with four treatments and three replications in 21 days with different doses of Oodev, such as; A (Feed without Oodev), B (0.5 mL/kg feed), C (1 mL/kg feed) and D (2 mL/kg feed). The parameters observed in this study were gonad maturity level, histological structure of ovary, gonadosomatic index, fecundity, and diameter of eggs. The results showed that the dose of Oodev at 1.0 mL/kg feed was an effective dose to optimize the gonad maturity of female Yellow rasbora. This is proven by the highest results shown on all parameters, such as; the maturity level in the IV phase, histological structure of the ovary which showed the dominance of the oocyte maturation phase, gonadosomatic index of 14.014%, the fecundity of 721 eggs, and egg diameter of 0.865 mm. In conclusion, using Oodev in feed at a dose of 1.0 ml/kg of feed for 21 days is an effective dose to optimize the maturation of female Yellow rasbora.

Relating neighborhood deprivation to childhood obesity in the ABCD study: Evidence for theories of neuroinflammation and neuronal stress.

We evaluated whether relationships between area deprivation (ADI), body mass index (BMI) and brain structure (e.g., cortical thickness, subcortical volume) during preadolescence supported the immunologic model of self-regulation failure (NI) and/or neuronal stress (NS) theories of overeating. The NI theory proposes that ADI causes structural alteration in the brain due to the neuroinflammatory effects of overeating unhealthy foods. The NS theory proposes that ADI-related stress negatively impacts brain structure, which causes stress-related overeating and subsequent obesity. Data were gathered from the Adolescent Brain Cognitive Development Study (9 to 12 years old; n = 3,087, 51% male). Linear mixed-effects models identified brain regions that were associated with both ADI and BMI; longitudinal associations were evaluated with mediation models. The NI model included ADI and BMI at 9 to 10 years old and brain data at 11 to 12 years old. The NS model included ADI and brain data at 9 to 10 years old and BMI at 11 to 12 years old. BMI at 9 to 10 years old partially mediated the relationship between ADI and ventral diencephalon (DC) volume at 11 to 12 years old. Additionally, the ventral DC at 9 to 10 years old partially mediated the relationship between ADI and BMI at 11 to 12 years old, even in youth who at baseline, were of a healthy weight. Results were unchanged when controlling for differences in brain structure and weight across the 2-years. Greater area deprivation may indicate fewer access to resources that support healthy development, like nutritious food and nonstressful environments. Our findings provide evidence in support of the NI and NS theories of overeating, specifically, with greater ADI influencing health outcomes of obesity via brain structure alterations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

PPT-LBS: Privacy-preserving top-k query scheme for outsourced data of location-based services

Location-based service (LBS) is enjoying a great popularity with the fast growth of mobile Internet. As the volume of data increases dramatically, an increasing number of location service providers (LSPs) are moving LBS data to cloud platforms for benefit of affordability and stability. However, while cloud server provides convenience and stability, it also leads to data security and user privacy leakage. Aiming at the problems of insufficient privacy protection and inefficient query in the existing LBS data outsourcing schemes, this paper presents a novel privacy-preserving top-k query for outsourcing situations. Firstly, to ensure data security of LSP and privacy of the user, the enhanced asymmetric scalar-product preserving encryption and public key searchable encryption have been adopted to encrypt outsourced data and LBS query, which can effectively lower the computational cost and realize the privacy protection search. Secondly, an efficient and secure index structure is constructed by using a coded quadtree and the bloom filter, so that the cloud server can quickly locate the user’s query region to improve retrieval efficiency. Finally, the formal security analysis is given under the random oracle model, and the performance is evaluated by experiments which demonstrates that our scheme is preferable to existing schemes.

BonsaiKV: Towards Fast, Scalable, and Persistent Key-Value Stores with Tiered, Heterogeneous Memory System

Emerging NUMA/CXL-based tiered memory systems with heterogeneous memory devices such as DRAM and NVMM deliver ultrafast speed, large capacity, and data persistence all at once, offering great promise to high-performance in-memory key-value stores. To fully unleash the performance potential of such memory systems, this paper presents BonsaiKV, a key-value store that makes the best use of different components in a tiered memory system. The core of BonsaiKV is a tri-layer hierarchical storage architecture that separates data indexing, persistence, and scalability from each other and realizes each of them within a specialized software-hardware layer. We design BonsaiKV with a set of novel techniques, including collaborative tiered indexing, NVMM congestion control mechanisms, fine-grained data striping, and NUMA-aware data management, to leverage hardware strengths and tackle device deficiencies. We compare BonsaiKV with state-of-the-art NVMM-optimized key-value stores and persistent index structures using a variety of YCSB workloads. Evaluation results demonstrate that BonsaiKV outperforms others by up to 7.69×, 19.59×, and 12.86× in read-, write- and scan-intensive scenarios, respectively.

Algorithmic Complexity Attacks on Dynamic Learned Indexes

Learned Index Structures (LIS) view a sorted index as a model that learns the data distribution, takes a data element key as input, and outputs the predicted position of the key. The original LIS can only handle lookup operations with no support for updates, rendering it impractical to use for typical workloads. To address this limitation, recent studies have focused on designing efficient dynamic learned indexes. ALEX, as the first and one of the representative dynamic learned index structures, enables dynamism by incorporating a series of design choices, including adaptive key space partitioning, dynamic model retraining, and sophisticated engineering and policies that prioritize read/write performance. While these design choices offer improved average-case performance, the emphasis on flexibility and performance increases the attack surface by allowing adversarial behaviors that maximize ALEX's memory space and time complexity in worst-case scenarios. In this work, we present the first systematic investigation of algorithmic complexity attacks (ACAs) targeting the worst-case scenarios of ALEX. We introduce new ACAs that fall into two categories, space ACAs and time ACAs, which target the memory space and time complexity, respectively. First, our space ACA on data nodes exploits ALEX's gapped array layout and uses Multiple-Choice Knapsack (MCK) to generate an optimal adversarial insertion plan for maximizing the memory consumption at the data node level. Second, our space ACA on internal nodes exploits ALEX's catastrophic cost mitigation mechanism, causing an out-of-memory (OOM) error with only a few hundred adversarial insertions. Third, our time ACA generates pathological insertions to increase the disparity between the actual key distribution and the linear models of data nodes, deteriorating the runtime performance by up to 1, 641× compared to ALEX operating under legitimate workloads.

A Survey on Searchable Symmetric Encryption

Outsourcing data to the cloud has become prevalent, so Searchable Symmetric Encryption (SSE), one of the methods for protecting outsourced data, has arisen widespread interest. Moreover, many novel technologies and theories have emerged, especially for the attacks on SSE and privacy-preserving. But most surveys related to SSE concentrate on one aspect (e.g., single keyword search, fuzzy keyword search) or lack in-depth analysis. Therefore, we revisit the existing work and conduct a comprehensive analysis and summary. We provide an overview of state-of-the-art in SSE and focus on the privacy it can protect. Generally, (1) we study the work of the past few decades and classify SSE based on query expressiveness. Meanwhile, we summarize the existing schemes and analyze their performance on efficiency, storage space, index structures, and so on.; (2) we complement the gap in the privacy of SSE and introduce in detail the attacks and the related defenses; (3) we discuss the open issues and challenges in existing schemes and future research directions. We desire that our work will help novices to grasp and understand SSE comprehensively. We expect it can inspire the SSE community to discover more crucial leakages and design more efficient and secure constructions.

An efficient flattened index structure with lazy restructuring and hotness awareness

Hotspot issue is ubiquitous in the in-memory key-value stores, which seriously affects its performance. The efficiency of the index structure is the key to the performance of the in-memory key-value store, and the access of hotspot data must be through the search of the index structure. However, the existing index structures do not consider the hotspot issue, they treat and manage all the data equally, and cannot break through the performance bottleneck of the system. Index structures of type B＋-Tree are common in in-memory key-value stores, but the existing B＋-tree index structures do not consider the frequent structure change caused by node restructuring, which will seriously degrade the performance of the index structure. In this paper, we propose LH-Index, an efficient flattened index structure with lazy restructuring and hotness awareness. First, we use a search method combining linear search and binary search based on a partitioning strategy and endow nodes in LH-Index with a hotness awareness ability to search hotspot data quickly. Meanwhile, we use a lazy restructuring strategy for LH-Index to reduce the performance degradation due to frequent structure change. In addition, in order to meet the requirements of most scenarios, LH-Index is extended to DRAM/NVM hybrid memory by separate storage. Our evaluation shows that LH-Index achieves up to 1.85× search throughput and the lowest tail latency relative to other index structures under highly-skewed workloads, and it also has the highest throughput under mixed workloads.

STAR: A Cache-based Stream Warehouse System for Spatial Data

The proliferation of mobile phones and location-based services has given rise to an explosive growth in spatial data. To enable spatial data analytics, spatial data needs to be streamed into a data stream warehouse system that can provide real-time analytical results over the most recent and historical spatial data in the warehouse. Existing data stream warehouse systems are not tailored for spatial data. In this article, we introduce the STAR system. STAR is a distributed in-memory data stream warehouse system that provides low-latency and up-to-date analytical results over a fast-arriving spatial data stream. STAR supports both snapshot and continuous queries that are composed of aggregate functions and ad hoc query constraints over spatial, textual, and temporal data attributes. STAR implements a cache-based mechanism to facilitate the processing of snapshot queries that collectively utilizes the techniques of query-based caching (i.e., view materialization) and object-based caching. Moreover, to speed up processing continuous queries, STAR proposes a novel index structure that achieves high efficiency in both object checking and result updating. Extensive experiments over real datasets demonstrate the superior performance of STAR over existing systems.

Estimation of atmospheric refractive index structure constant using an InGaAs/InP single-photon detector.

Remote sensing of atmospheric refractive index structure constant ($\boldsymbol{C}_{\boldsymbol{n}}^2$) using lidar incorporating a single-photon detector (SPD) is proposed. The influence of turbulence on the fiber coupling efficiency with different fiber modes is analyzed. $\boldsymbol{C}_{\boldsymbol{n}}^2$ can be derived from the ratio of the backscattering signals counted on single-mode and multimode fiber-coupling channels of the SPD. In the experiment, by eliminating the shot noise effect on the fluctuation of the ratio, the lowest coupling ratio is used to retrieve $\boldsymbol{C}_{\boldsymbol{n}}^2$ and demonstrated by comparing to the results measured from a large aperture scintillometer (LAS). Good agreement between results from the LAS and the lidar is achieved. The correlation coefficients are 0.90, 0.89, and 0.89, under three different weather conditions.

Privacy-aware document retrieval with two-level inverted indexing

Previous work on privacy-aware ranking has addressed the minimization of information leakage when scoring top k documents, and has not studied on how to retrieve these top documents and their features for ranking. This paper proposes a privacy-aware document retrieval scheme with a two-level inverted index structure. In this scheme, posting records are grouped with bucket tags and runtime query processing produces query-specific tags in order to gather encoded features of matched documents with a privacy protection during index traversal. To thwart leakage-abuse attacks, our design minimizes the chance that a server processes unauthorized queries or identifies document sharing across posting lists through index inspection or across-query association. This paper presents the evaluation and analytic results of the proposed scheme to demonstrate the tradeoffs in its design considerations for privacy, efficiency, and relevance.