Hierarchical Index Structure Research Articles

An Efficient Cross-Modal Privacy-Preserving Image–Text Retrieval Scheme

Preserving the privacy of the ever-increasing multimedia data on the cloud while providing accurate and fast retrieval services has become a hot topic in information security. However, existing relevant schemes still have significant room for improvement in accuracy and speed. Therefore, this paper proposes a privacy-preserving image–text retrieval scheme called PITR. To enhance model performance with minimal parameter training, we freeze all parameters of a multimodal pre-trained model and incorporate trainable modules along with either a general adapter or a specialized adapter, which are used to enhance the model’s ability to perform zero-shot image classification and cross-modal retrieval in general or specialized datasets, respectively. To preserve the privacy of outsourced data on the cloud and the privacy of the user’s retrieval process, we employ asymmetric scalar-product-preserving encryption technology suitable for inner product calculation, and we employ distributed index storage technology and construct a two-level security model. We construct a hierarchical index structure to speed up query matching among massive high-dimensional index vectors. Experimental results demonstrate that our scheme can provide users with secure, accurate, fast cross-modal retrieval service while preserving data privacy.

Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems.

Development of Algorithm and Data Structure for 2D Regions Discrete Model Elements Accelerated Search

This paper is devoted to improving the search request processing productivity for planar discrete models used in engineering software. A data structure has been developed to accelerate the search for discretization elements based on a hierarchical triangular mesh. The developed indexing structure is built by a downward iterative algorithm, which constructs each new level of the hierarchy based on the previous level or indexed triangulation by simplifying it, which ensures that the morphology of the triangulation mesh is preserved throughout all levels of the hierarchical indexing structure. The developed building algorithm ensures the presence of tree-like connections between the levels of the hierarchical triangulation mesh, which allows downward navigation between geometrically close triangles. Search acceleration is achieved by performing a directed search in the top level of the indexing structure and then navigating between levels using downward links until the indexed triangle is found. The program implementation was carried out using C++17, and visualization of triangulation grids and isolines was carried out using the ObjectARX library. Based on the software implementation, an executive library was created.

Retracted] Multisensor Cross‐Media Data Mining Method Assisted by Expert System

The information generated by the multifaceted and multilevel processing of the data of multiple sensors is more meaningful than the information obtained by a single sensor and provides accurate information and decision‐making basis for various application systems. How to apply data mining theory to the multisensor cross‐media field has become a research hotspot. Through the analysis of multisensor cross‐media data, it is of great significance to dig out the important rules, information, or knowledge hidden in it and use it for cross‐media retrieval engine. The rule acquisition is the “bottleneck” problem of the expert system. This paper adopts the data mining method based on the rough set to acquire the rules and improves the basic algorithm of attribute reduction. Using the attribute reduction algorithm and the heuristic value reduction algorithm, the calculation is simplified and the reduction efficiency is improved. In the presentation, according to the characteristics of cross‐media and the application requirements of expert systems, this paper takes the case representation based on features as the basis and classifies cases according to feature attributes. In case retrieval, according to the hierarchical structure of case features, the entire case database is organized into a multilevel hierarchical index structure. In this paper, a cross‐media retrieval engine is constructed from the perspective of classifier design, and the Euclidean distance is used as the similarity matching model of image content. The mutual retrieval of images and audios preliminarily forms the design process of retrieval from one media type to another and establishes a corresponding cross‐media index. The experimental results show that the algorithm has better processing effect and higher accuracy than other algorithms. Different k‐nearest neighbor values were selected in the experiment, and it reached about 96% in the test environment of libsvm toolbox, which is better than the processing results of LE and LLE algorithms.

Enhancing Trajectory-Based Operations for UAVs through Hexagonal Grid Indexing: A Step towards 4D Integration of UTM and ATM

Aviation is expected to face a surge in the number of manned aircraft and drones in the coming years, making it necessary to integrate Unmanned Aircraft System Traffic Management (UTM) into Air Traffic Management (ATM) to ensure safe and efficient operations. This research proposes a novel hexagonal grid-based 4D trajectory representation framework for unmanned aerial vehicle (UAV) traffic management that overcomes the limitations of existing square/cubic trajectory representation methods. The proposed model employs a hierarchical indexing structure using hexagonal cells, enabling efficient ground based strategic conflict detection and conflict free 4D trajectory planning. Additionally, the use of Hexagonal Discrete Global Grid Systems provides a more accurate representation of UAV trajectories, improved sampling efficiency and higher angular resolution. The proposed approach can be used for predeparture conflict free 4D trajectory planning, reducing computational complexity and memory requirements while improving the accuracy of strategic trajectory conflict detection. The proposed framework can also be extended for air traffic flow management trajectory planning, Air Traffic Control (ATC) workload measurement, sector capacity estimation, dynamics airspace sectorization using hexagonal sectors and traffic density calculation, contributing to the development of an efficient UTM system, and facilitating the integration of UAVs into the national airspace system with ATM

A Hierarchical Searchable Encryption Scheme Using Blockchain-Based Indexing

Focusing on the fine-grained access control challenge of multi-user searchable encryption, we propose a hierarchical searchable encryption scheme using blockchain-based indexing (HSE-BI). First, we propose a hierarchical search index structure based on a DAG-type access policy and a stepwise hierarchical key derivation mechanism; which we outsourced to the blockchain network to achieve reliable hierarchical search. We design a dynamic append-only update protocol for the blockchain-based index to deal with adding and deleting files. Secondly, we propose a hierarchical authorization mechanism based on broadcast encryption to achieve fine-grained search permission granting and revoking, which can prevent a malicious server from colluding with corrupted users. The security and complexity analysis shows that HSE-BI achieves optimal search time while satisfying adaptive secure and revocation secure. Our experimental results are encouraging, e.g., compared with the traditional multi-user searchable encryption schemes, HSE-BI’s hierarchical search policy does not impact the search performance visually. The growth rate of the search latency decreases with the increasing number of hierarchical users, which can act as an efficient crypto tool to open up venues for other applications. We demonstrate that HSE-BI is more suitable for actual applications with fine-grained access requirements and can act as an efficient crypto tool to open up venues for other applications.

A New Tag Index Scheme Enables Fast Peptide Retrieval for Protein Identification

Sequence tag index in the field of computational proteomics can be used to facilitate faster open-search-based identification of modified peptides and in-depth analysis of mass spectrometry data. In protein-identification search engines, sequence tag index are playing a prominent role in recent ten years due to fast searching speed. However, in pursuit of less index space consumption, some protein search engines design excessively concise index schemes which lead to higher computational burden. We proposed a new tag index scheme named TIIP with a better balance between space and time complexity. TIIP has a unique two-level hierarchical index structure which allows rapid retrieval of all peptide sequences and their corresponding masses. Theoretically, the index space consumption of TIIP is not much higher compared to the typical tag index schemes, but the time complexity of sequence retrieval can be reduced to O(1), and practically, TIIP has about one million fold improvement in searching speed compared with brute force approach.

A Generic Ontology Framework for Indexing Keyword Search on Massive Graphs

Due to the unstructuredness and the lack of schema information of knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. Recently, various keyword search semantics have been designed. In this paper, we propose a generic ontology-based indexing framework for keyword search, called Bisimulation of Generalized Graph Index (BiG-index BiG-index), to enhance the search performance. The novelties of BiG-index BiG-index reside in using an ontology graph G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ont</sub> GOnt to summarize and index a data graph G G iteratively, to form a hierarchical index structure G. BiG-index BiG-index is generic since it only requires keyword search algorithms to generate query answers from summary graphs having two simple properties. Regarding query evaluation, we transform a keyword search q q into Q according to G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ont</sub> GOnt in runtime. The transformed query is searched on the summary graphs in G. The efficiency is due to the small sizes of the summary graphs and the early pruning of semantically irrelevant subgraphs. To illustrate BiG-index BiG-index's applicability, we show popular indexing techniques for keyword search (e.g., Blinks Blinks and r-clique r-clique) can be easily implemented on top of BiG-index BiG-index. Our extensive experiments show that BiG-index BiG-index reduced the runtimes of popular keyword search work Blinks Blinks by 50.5 percent and r-clique r-clique by 29.5 percent.

Public Sentiment Big Data Query Processing and Optimization with Unified Storage of Source and Meta Data

Public sentiment big data has the characteristics of mass, multi-source, heterogeneity and multi-mode. At present, it mostly adopts separate storage strategies and has low storage and query efficiency. To this end, a distributed storage model is proposed in which structured and unstructured data correspond and are stored uniformly. By establishing a unified storage framework for source metadata, the original source data and characteristic metadata are stored uniformly according to different storage methods. Based on the above storage architecture, a hierarchical index structure is proposed to improve the efficiency of big data query. The unified storage model of source metadata is compared with the common storage methods, and is in the leading position in terms of Block number and query processing efficiency.

Privacy-Preserving Search for a Similar Genomic Makeup in the Cloud

Increasing affordability of genome sequencing and, as a consequence, widespread availability of genomic data opens up new opportunities for the field of medicine, as also evident from the emergence of popular cloud-based offerings in this area, such as Google Genomics [1]. To utilize this data more efficiently, it is crucial that different entities share their data with each other. However, such data sharing is risky mainly due to privacy concerns. In this article, we attempt to provide a privacy-preserving and efficient solution for the “similar patient search” problem among several parties (e.g., hospitals) by addressing the shortcomings of previous attempts. We consider a scenario in which each hospital has its own genomic dataset and the goal of a physician (or researcher) is to search for a patient similar to a given one (based on a genomic makeup) among all the hospitals in the system. To enable this search, we propose a hierarchical index structure to index each hospital’s dataset with low memory requirement. Furthermore, we develop a novel privacy-preserving index merging mechanism that generates a common search index from individual indices of each hospital to significantly improve the search efficiency. We also consider the storage of medical information associated with genomic data of a patient (e.g., diagnosis and treatment). We allow access to this information via a fine-grained access control policy that we develop through the combination of standard symmetric encryption and ciphertext policy attribute-based encryption. Using this mechanism, a physician can search for similar patients and obtain medical information about the matching records if the access policy holds. We conduct experiments on large-scale genomic data and show the high efficiency of the proposed scheme.

Skyline-based dissimilarity of images

Large image collections are being used in many modern applications. In this paper, we aim at capturing the intrinsic dissimilarities of image descriptors in large image collections, i.e., to detect dissimilar (or else diverse) images without defining an explicit similarity or distance measure. Towards this goal, we adopt skyline processing techniques for large image databases, based on their high-dimensional descriptor vectors. The novelty of the proposed methodology lies in the use of skyline techniques empowered by state-of-the-art hashing schemes to enable effective data partitioning and indexing in secondary memory, towards supporting large image databases. The proposed approach is evaluated experimentally by using three real-world image datasets. Performance evaluation results demonstrate that images lying on the skyline have significantly different characteristics, which depend on the type of the descriptor. Thus, these skyline items may be used as seeds to apply clustering in large image databases. In addition, we observe that skyline processing using hash-based indexing structures is significantly faster than index-free skyline computation and also more efficient than skyline computation with hierarchical indexing structures. Based on our results, the proposed approach is both efficient (regarding runtime) and effective (with respect to image diversity) and therefore can be used as a base for more complex data mining tasks such as clustering.

S2R-tree: a pivot-based indexing structure for semantic-aware spatial keyword search

Semantic-aware spatial keyword search is an important technique for digital map services. However, existing indexing and search methods have limited pruning effect due to the high dimensionality in semantic space, causing query efficiency to be a serious issue. To handle this problem, this paper proposes a novel pivot-based hierarchical indexing structure S2R-tree to integrate spatial and semantic information in a seamless way. Instead of indexing objects in the original semantic space, we carefully design a space mechanism to transform the high dimensional semantic vectors to a low dimensional space, so that more effective pruning effect can be achieved. On top of the S2R-tree, an efficient query processing algorithm is further designed, which not only ensures efficient query processing by a set of theoretical bounds, but also returns accurate results despite of the indexing in the low dimensional space. Furthermore, we conduct extensive experiments to evaluate and compare our proposed and baseline methods.

Evaluation model for immersed tunnel health state: A case study of Honggu Tunnel, Jiangxi Province, China

The evaluation of the health state of an immersed tunnel is inevitably affected by the variety of influencing factors, the complex relationship between influencing factors and fuzzy characteristics of health evaluation criteria. A targeted method for analysis of the health conditions of an immersed tunnel in operation period is immature. As a case study of the health monitoring of the largest inland river immersed tunnel-Nanchang Honggu Tunnel, a fuzzy analytic hierarchy process (Fuzzy-AHP) method was carried out in this study, which merged different types of data from multiple sensors to map them into comprehensive evaluation value of the immersed tunnel. The hierarchical structure of the health evaluation index was established, and the layout scheme of monitoring sensors was designed through a comprehensive analysis of influencing factors. Additionally, the entropy weight method was introduced to modify the subjective characteristic of index weight in the fuzzy analytic hierarchy model, and then a Fuzzy-AHP evaluation procedure was built. Finally, the proposed evaluation model and evaluation procedure of immersed tunnel health states were verified by the field monitoring data of the Nanchang Honggu Tunnel. The comprehensive evaluation value VF was compared with the rating scales and countermeasures proposed in this study to determine the tunnel health state and corresponding countermeasures. The results indicate that the evaluation health of the whole tunnel sections of Nanchang Honggu Tunnel in December 2018 was in grade A, and further monitoring was needed. The VF values of sections located at the middle of the river and the final joint were slightly smaller than other sections. The proposed Fuzzy-AHP method can give a feasible and effective evaluation for immersed tunnel health states.

Vector and line quantization for billion-scale similarity search on GPUs

Billion-scale high-dimensional approximate nearest neighbour (ANN) search has become an important problem for searching similar objects among the vast amount of images and videos available online. The existing ANN methods are usually characterized by their specific indexing structures, including the inverted index and the inverted multi-index structure. The inverted index structure is amenable to GPU-based implementations, and the state-of-the-art systems such as Faiss are able to exploit the massive parallelism offered by GPUs. However, the inverted index requires high memory overhead to index the dataset effectively. The inverted multi-index structure is difficult to implement for GPUs, and also ineffective in dealing with database with different data distributions. In this paper we propose a novel hierarchical inverted index structure generated by vector and line quantization methods. Our quantization method improves both search efficiency and accuracy, while maintaining comparable memory consumption. This is achieved by reducing search space and increasing the number of indexed regions. We introduce a new ANN search system, VLQ-ADC, that is based on the proposed inverted index, and perform extensive evaluation on two public billion-scale benchmark datasets SIFT1B and DEEP1B. Our evaluation shows that VLQ-ADC significantly outperforms the state-of-the-art GPU- and CPU-based systems in terms of both accuracy and search speed. The source code of VLQ-ADC is available at https://github.com/zjuchenwei/vector-line-quantization.

Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time.

Quadrant-Based Minimum Bounding Rectangle-Tree Indexing Method for Similarity Queries over Big Spatial Data in HBase.

With the rapid development of mobile devices and sensors, effective searching methods for big spatial data have recently received a significant amount of attention. Owing to their large size, many applications typically store recently generated spatial data in NoSQL databases such as HBase. As the index of HBase only supports a one-dimensional row keys, the spatial data is commonly enumerated using linearization techniques. However, the linearization techniques cannot completely guarantee the spatial proximity of data. Therefore, several studies have attempted to reduce false positives in spatial query processing by implementing a multi-dimensional indexing layer. In this paper, we propose a hierarchical indexing structure called a quadrant-based minimum bounding rectangle (QbMBR) tree for effective spatial query processing in HBase. In our method, spatial objects are grouped more precisely by using QbMBR and are indexed based on QbMBR. The QbMBR tree not only provides more selective query processing, but also reduces the storage space required for indexing. Based on the QbMBR tree index, two query-processing algorithms for range query and kNN query are also proposed in this paper. The algorithms significantly reduce query execution times by prefetching the necessary index nodes into memory while traversing the QbMBR tree. Experimental analysis demonstrates that our method significantly outperforms existing methods.

Finding the optimal location and keywords in obstructed and unobstructed space

The problem of optimal location selection based on reverse k nearest neighbor (R $$k$$ NN) queries has been extensively studied in spatial databases. In this work, we present a related query, denoted as a Maximized Bichromatic Reverse Spatial Textual k Nearest Neighbor (MaxST) query, that finds an optimal location and a set of keywords for an object so that the object is a $$k$$ NN object for as many users as possible. Such a query has many practical applications including advertisements, where the query is to find the location and the text contents to include in an advertisement so that it is relevant to the maximum number of users. The visibility of the advertisements also has an important role in the users’ interests. In this work, we address two instances of the spatial relevance when ranking items: (1) the Euclidean distance and (2) the visibility. We carefully design a series of index structures and approaches to answer the MaxST for both instances. Specifically, we present (1) the Grp-topk approach that requires the computation of the top-k objects for all of the users first and then applies various pruning techniques to find the optimal location and keywords; (2) the Indiv-U approach, where we use similarity estimations to avoid computing the top-k objects of the users that cannot be a final result; and (3) the Index-U approach where we propose a hierarchical index structure over the users to improve pruning. We show that the keyword selection component in MaxST queries is NP-hard and present both approximate and exact solutions for the problem.

Interactive Multiple-User Location-Based Keyword Queries on Road Networks

As the use of smart mobile devices increases, users can connect and coordinate with local people or events that match their interests through geosocial networking applications. Such applications require techniques that support multiple-user location-based keyword (MULK) query processing, which takes a set of geo-locations and a set of keywords as arguments and returns a list of relevant POIs that match the arguments. In geosocial networking, processing this query involves two issues: mining users’ individual preferences and indexing road networks. In this paper, we investigate how to mine users’ individual preferences by enhancing MULK query processing with interactions, resulting in interactive multiple-user location-based keyword query. Furthermore, we propose a hierarchical road network index structure to support efficient query processing. Finally, we conduct a set of experiments that demonstrate the effectiveness and efficiency of our solutions under various parameter settings.

A PR-quadtree based multi-dimensional indexing for complex query in a cloud system

The state-of-the-art indexing mechanisms for distributed cloud data management systems can not support complex queries, such as multi-dimensional query and range query. To solve this problem, we propose a multi-dimensional indexing mechanism named PR-Chord to support complex queries. PR-Chord is composed of the global index named PR-Index and the Chord network. The multi-dimensional space formed by the range of the multi-dimensional data is divided into hyper-rectangle spaces equally. The PR-Index is a hierarchical index structure based on the improved PR quadtree to index these spaces. The complex query is transformed into the query of leaf nodes of PR-Index. We design the algorithms of query, insertion and deletion to support complex queries. Since PR-Index does not store the multi-dimensional data, its maintenance cost is zero. PR-Chord has the advantages of load balancing and simple algorithm. The experiment results demonstrate that PR-Chord has good query efficiency.

Accurate and efficient shape matching approach using vocabularies of multi-feature space representations

Selection of compressed, robust and accurate features is the fundamental ingredient of effective content-based image recognition and retrieval using shape information of objects in the image. In this paper, we present a four-stage system for real-time object recognition and retrieval that employs multiple feature space representation using contour information. In the first stage, we pre-process the shapes to cater for the presence of distortions such as cracks that can significantly distort the contour information of the shape. We then generate multiple feature space representations of shapes to be used later in proposed combination for efficient and accurate retrieval of shapes using a hierarchical indexing structure. To enable real-time image-based shape analysis by enhancing the efficiency and reducing the storage requirement of proposed shape descriptors, we present a quantization approach to generate vocabulary of feature space representation of shapes. These features are then combined in an ensemble for accurate and efficient shape retrieval and recognition in the presence of large shape datasets. The proposed system is evaluated using publicly available shape datasets such as MPEG 7, Swedish leaf and KIMIA 99 datasets. Our approach achieves higher accuracies which are better than state-of-the-art approaches reported in literature whilst looking at a small subset of shapes in dataset.