Query Problem Research Articles

An adaptive time-varying neural network for solving K optimal time-varying destroy locations query problem

The time-varying and destroying are two real existing factors that are generally ignored in the existing k optimal locations query study, which makes the existing study unapplicable to real-world environments. This study presents a novel adaptive time-varying neural network (ATNN) to solve the k optimal locations query problem on time-varying destroy networks. ATNN is a structure-adaptive neural network that is composed of newly designed adaptive time-varying neurons; it does not require training and can automatically construct or adjust a topological structure according to different topologies and scales of time-varying and destroying networks. Adaptive time-varying neurons consist of five layers: wave receiving layer, state verification layer, state storage layer, wave generation layer, and wave sending layer, all those five layers implements the information exchange and processing between neurons as well as the representation of the time-varying and destroying properties of the network. The proposed algorithm has been theoretically proven through time-complexity analysis, correctness analysis, and a numerical example, and its performance has been further confirmed through experiments on public road network datasets.

Efficient Algorithms for Range Mode Queries in the Big Data Era

The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem expands upon this concept by preprocessing an array A containing n natural numbers. This allows for the swift determination of the mode within any subarray A[a..b], thus optimizing the computation of the mode for a multitude of range queries. The efficacy of this process bears considerable importance in data analytics and retrieval across diverse platforms, including but not limited to online shopping experiences and financial auditing systems. This study is dedicated to exploring and benchmarking different algorithms and data structures designed to tackle the RMQ problem. The goal is to not only address the theoretical aspects of RMQ but also to provide practical solutions that can be applied in real-world scenarios, such as the optimization of an online shopping platform’s understanding of customer preferences, enhancing the efficiency and effectiveness of data retrieval in large datasets.

Generalized Quantum Signal Processing

Quantum signal processing (QSP) and quantum singular value transformation (QSVT) currently stand as the most efficient techniques for implementing functions of block-encoded matrices, a central task that lies at the heart of most prominent quantum algorithms. However, current QSP approaches face several challenges, such as the restrictions imposed on the family of achievable polynomials and the difficulty of calculating the required phase angles for specific transformations. In this paper, we present a generalized quantum signal processing (GQSP) approach, employing general SU(2) rotations as our signal-processing operators, rather than relying solely on rotations in a single basis. Our approach lifts all practical restrictions on the family of achievable transformations, with the sole remaining condition being that |P|≤1, a restriction necessary due to the unitary nature of quantum computation. Furthermore, GQSP provides a straightforward recursive formula for determining the rotation angles needed to construct the polynomials in cases where P and Q are known. In cases where only P is known, we provide an efficient optimization algorithm capable of identifying in under a minute of GPU time, a corresponding Q for polynomials of degree on the order of 107. We further illustrate GQSP simplifies QSP-based strategies for Hamiltonian simulation, offer an optimal solution to the ϵ-approximate fractional query problem that requires O((1/δ)+log(1/ϵ)) queries to perform where O(1/δ) is a proved lower bound, and introduces novel approaches for implementing bosonic operators. Moreover, we propose a novel framework for the implementation of normal matrices, demonstrating its applicability through synthesis of diagonal matrices, as well as the development of a new algorithm for convolution through synthesis of circulant matrices using only O(dlogN+log2N) 1 and 2-qubit gates for a filter of lengths d. Published by the American Physical Society 2024

A wave delay neural network for solving label-constrained shortest route query on time-varying communication networks.

The focus of the research is on the label-constrained time-varying shortest route query problem on time-varying communication networks. To the best of our knowledge, research on this issue is still relatively limited, and similar studies have the drawbacks of low solution accuracy and slow computational speed. In this study, a wave delay neural network (WDNN) framework and corresponding algorithms is proposed to effectively solve the label-constrained time-varying shortest routing query problem. This framework accurately simulates the time-varying characteristics of the network without any training requirements. WDNN adopts a new type of wave neuron, which is independently designed and all neurons are parallelly computed on WDNN. This algorithm determines the shortest route based on the waves received by the destination neuron (node). Furthermore, the time complexity and correctness of the proposed algorithm were analyzed in detail in this study, and the performance of the algorithm was analyzed in depth by comparing it with existing algorithms on randomly generated and real networks. The research results indicate that the proposed algorithm outperforms current existing algorithms in terms of response speed and computational accuracy.

SecCT: Secure and scalable count query models on encrypted genomic data

Recently, due to the continued reduction in DNA sequencing cost, large-scale genetic samples are being gathered for accelerating predispositions to specific diseases, tailoring treatment of efficient drugs and therapies, etc. Massive genetic samples are encrypted-and-then-delegated to a public cloud to both save investment and maintenance costs and prevent the potential leakage of sensitive information. However, such a manner compromises the serviceability of a public cloud, since encryption inevitably breaks the semantic information of genetic samples. Secure count query of single-nucleotide polymorphisms (SNPs), as a kernel component for GWASs and related genomic analysis, is attracting much more attention. Existing methods lack provable security, suffer low efficiency caused by multiple interactions with the cloud, etc. In this paper, a secure virtual CT-Tree (secure vCT-Tree) is carefully constructed to confuse the tree structure by introducing a hash function and a Paillier system. Furthermore, by delegating the secure vCT-Tree to the cloud, concrete models (i.e., SecCT and SecCT+) are presented to resolve secure count query problems on-the-fly. SecCT+ is a solution based on trusted execution environment while SecCT is a pure software solution. Both models advance the provable security of genetic research and are proven to be secure under the adaptive chosen keyword (query) attack (IND-CKA2) model. Furthermore, massive experiments are evaluated on realistic data to show the superiority of SecCT and SecCT+.

I/O Efficient Label-Constrained Reachability Queries in Large Graphs

Computing the reachability between two vertices in a graph is a fundamental problem in graph data analysis. Most of the existing works assume that the edges in the graph have no labels, but in many real application scenarios, edges naturally come with edge-labels, and label constraints may be placed on the edges appearing on a valid path between two query vertices. Therefore, we study the label-constrained reachability (LCR) queries in this paper, where we are given a source vertex s , a target vertex t , a label set Δ, and the goal is to check whether there exists any path from s to t such that all the labels of edges on the path belong to Δ. A plethora of methods have been proposed in the literature to support the LCR queries. All these methods take the assumption that the graph is resident in the main memory of a machine. Nevertheless, the graphs in many real application scenarios are generally big and may not reside in memory. In these cases, existing methods suffer from serious scalability problem, i.e., result in huge I/O costs. Motivated by this, in this paper, we study the I/O efficient LCR query problem and aim to efficiently answer the LCR queries when the graph cannot fit in the main memory. To achieve this goal, we propose a reduction-based indexing approach. We introduce two elegant graph reduction operators which aims to reduce the size of the graph loaded in memory while preserving the LCR information among the remaining vertices. With these two operators, we devise an index named LCR-Index and propose algorithms to adaptively construct the index based on the available memory. Equipped with LCR-Index, we can answer a LCR query by only scanning the LCR-Index sequentially. Experiments demonstrate our query processing algorithm can handle graphs with billions of edges.

Efficient processing of coverage centrality queries on road networks

Coverage Centrality is an important metric to evaluate vertex importance in road networks. However, current solutions have to compute the coverage centrality of all the vertices together, which is resource-wasting, especially when only some vertices centrality is required. In addition, they have poor adaption to the dynamic scenario because of the computation inefficiency. In this paper, we focus on the coverage centrality query problem and propose a method that efficiently computes the centrality of single vertices without relying on the underlying graph being static by employing the intra-region pruning, inter-region pruning, and top-down search. We further propose the bottom-up search and mixed search to improve efficiency. Experiments validate the efficiency and effectiveness of our algorithms compared with the state-of-the-art method.

Sharing Queries with Nonequivalent User-defined Aggregate Functions

This article presents Sharing User-Defined Aggregate Function (SUDAF), a declarative framework that allows users to write User-defined Aggregate Functions (UDAFs) as mathematical expressions and use them in Structured Query Language statements. SUDAF rewrites partial aggregates of UDAFs using built-in aggregate functions and supports efficient dynamic caching and reusing of partial aggregates. Our experiments show that rewriting UDAFs using built-in functions can significantly speed up queries with UDAFs, and the proposed sharing approach can yield up to two orders of magnitude improvement in query execution time. The article studies also an extension of SUDAF to support sharing partial results between arbitrary queries with UDAFs. We show a connection with the problem of query rewriting using views and introduce a new class of rewritings, called SUDAF rewritings, which enables to use views that have aggregate functions different from the ones used in the input query. We investigate the underlying rewriting-checking and rewriting-existing problem. Our main technical result is a reduction of these problems to, respectively, rewriting-checking and rewriting-existing of the so-called aggregate candidates , a class of rewritings that has been deeply investigated in the literature.

A Swell Neural Network Algorithm for Solving Time-Varying Path Query Problems with Privacy Protection

In this paper, a swell neural network (SNN) algorithm was proposed for solving time-varying path query (TVPQ) problems with privacy protection with the following goals: (i) querying the K-nearest paths with time limitations in a time-varying scenario, and (ii) protecting private information from neighborhood attacks. The proposed SNN is a network in which the optimal paths can be calculated at the same time with no need for training. For TVPQ, a node is considered a neuron, and time-varying means that an edge has different costs in different time windows. For SNN, the query paths are swell sets from the start to the target within an upper limit. An encrypted index is designed for privacy protection. The evaluation of the efficiency and accuracy of the SNN was carried out based on New York road instances.

FusionQuery: On-demand Fusion Queries over Multi-source Heterogeneous Data

Centralised data management systems (e.g., data lakes) support queries over multi-source heterogeneous data. However, the query results from multiple sources commonly involve between-source conflicts, which makes query results unreliable and confusing and degrades the usability of centralised data management systems. Therefore, resolving the between-sourced conflicts is one of the most important problems for centralised data management systems. To solve it, many batch data fusion-based methods have been proposed, which require traversing all the data in the centralised data management systems and cause scalability and flexibility issues. To address these issues, this paper explores the problem of on-demand fusion queries, where the between-sourced conflicts are solved with only the query-related data; moreover, we propose an efficient on-demand fusion query framework, FusionQuery, which consists of a query stage and a fusion stage. In the query stage, we frame the heterogeneous data query problem as a knowledge graph matching problem and present a line graph-based method to accelerate it. In the fusion stage, we develop an Expectation Maximization-style algorithm to iteratively updates data veracity and source trustworthiness. Furthermore, we design an incremental estimation method of source trustworthiness to address the lack of sufficient observations. Extensive experiments on two real-world datasets demonstrate that FusionQuery outperforms state-of-the-art data fusion methods in terms of both effectiveness and efficiency.

Hierarchical pattern-based complex query of temporal knowledge graph

The complex query is an essential type of knowledge graph query, which aims to deal with the complex relationships among multiple triples involved in query problems. However, the existing query methods often directly search the target entities in large knowledge graphs, resulting in unsatisfactory query results. In addition, most of the methods focus on static knowledge graphs, ignoring the critical semantic information contained in time elements in query questions. Therefore, this paper proposes a complex query model for temporal knowledge graphs based on the hierarchical pattern. Firstly, aiming at the multi-level semantic relations in complex query problems, we use the entity mapping method to generate the corresponding query graphs. This method can accurately express the semantic relations by learning the constraints, such as time and quantity, in the query problem by manual rules. In addition, to reduce the impact of noisy data on query results, a complex query algorithm based on a hierarchical pattern is proposed. By decomposing the large-scale temporal knowledge graph into smaller knowledge sub-graphs, the search range of target results is effectively reduced. Finally, extensive experiments demonstrate that our model has certain advantages in executing complex queries.

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.

S_IDS: An efficient skyline query algorithm over incomplete data streams

The efficient processing of mass stream data has attracted wide attention in the database field. The skyline query on the sensor data stream can monitor multiple targets in real time, to avoid abnormal events such as fire and explosion, which is very useful in the practical application of sensor data monitoring. However, real-world stream data may often contain incomplete data attributes due to faulty sensing devices or imperfect data collection techniques. Skyline queries over incomplete data streams may lead to a lack of transitivity and loop domination issues. To solve the problem of the skyline query over incomplete data streams, firstly, this paper uses differential dependency rule (DD) to fill the missing attribute values of data in the incomplete data stream. Then, the hierarchical grid index (HGrid) is introduced into the field of skyline query to improve pruning efficiency. In the process of skyline calculation, this paper only keeps as few calculation results as possible for the data that may affect the result to avoid a large number of repeated calculations. Thus, S_IDS (Skyline query algorithm over Incomplete Data Stream) is proposed to query skyline results with high confidence from the incomplete data stream. Finally, by comparing with the most advanced skyline query algorithms over incomplete data streams, the correctness and efficiency of the proposed S_IDS algorithm are proved.

Refined statistical attacks against searchable symmetric encryption using non-indexed documents

Searchable symmetric encryption(SSE) schemes allow clients to search encrypted data stored on remote servers but ensure efficient retrieval by revealing specific information about the queries, such as access patterns. Honest but curious servers can then use these leakages to infer keywords queried by users. However, most attack schemes can only achieve considerable recovery accuracy(over 30%) under favorable conditions. When the auxiliary information is weak, the inference attack schemes using statistical information can achieve the same or even better recovery accuracy. In this paper, an attack based on statistical information is proposed. Our attack iteratively solves the quadratic recovery query problem using a linear optimization solver. With a tiny number of known queries(which can be deleted by the server), the query recovery accuracy can reach about 95% without knowing the exact background knowledge of the documents stored by the client. This process not only makes our scheme outperforms other attack schemes in accuracy but also makes our attack execution more efficient than other schemes, up to a maximum difference of 1000 seconds. The attack can still achieve considerable accuracy even when the defense is applied to the SSE scheme; defenses can help the SSE scheme obfuscate the pattern leakages.

Graph-Indexed kNN Query Optimization on Road Network

The nearest neighbors query problem on road networks constitutes a crucial aspect of location-oriented services and has useful practical implications; e.g., it can locate the k-nearest hotels. However, researches who study road networks still encounter obstacles due to the method’s inherent limitations with respect to object mobility. More popular methods employ indexes to store intermediate results to improve querying time efficiency, but these other methods are often accompanied by high time costs. To balance the costs of time and space, a lightweight flow graph index is proposed to reduce the quantity of candidate nodes, and with this index the results of a kNN query can be efficiently obtained. Experiments on real road networks confirm the efficiency and accuracy of our optimized algorithm.

Research on Time-Aware Group Query Method with Exclusion Keywords

Aiming at the problem that the existing spatial keyword group query problem did not consider the query requirements with exclusion keywords and time attributes, a time-aware group query problem with exclusion keywords (TEGSKQ) is proposed for the first time. To solve this problem effectively, this paper proposes a query method based on the EKTIR-Tree index and dominating group (EKTDG). This method first proposes the EKTIR-tree index, which incorporates Huffman coding and integrates Bloom filters to deal with excluded keywords in order to improve the hit rate of keyword queries, significantly improving the query efficiency and reducing the storage occupancy. Then, the Candidate algorithm is proposed based on the EKTIR-tree index to filter out the spatial–textual objects that meet the query’s keywords and time requirements, narrowing the search space for subsequent queries on a large scale. To address the problem of the low efficiency of existing algorithms based on a spatial distance query, a distance-dominating group is defined and a pruning algorithm based on a spatial distance-dominating group is proposed, which is a refining process of query results and greatly improves the search efficiency of the query. Theoretical and experimental studies show that the proposed method can better handle group queries with exclusion keywords based on time awareness.

Optimal schemes for combinatorial query problems with integer feedback

Optimal schemes for combinatorial query problems with integer feedback

An efficient approach for faster matching of approximate patterns in graphs

Graphs have proven to be an efficient problem representation scheme in many real-world applications and can serve to address mining of patterns in large volumes of data. This work addresses the data management issue in graph databases for shortest path queries, verification of reachability, and pattern matching queries. The key issue is to match all possible patterns in a large data graph that matches a user-provided pattern, known as the query graph. The large size of the graph makes the problem complex when searching for all patterns that are similar to the user’s query. For a given query, a match occurs when a graph with n vertices has the same label as the corresponding vertices in the query graph. This work addresses such pattern matching query problems on large graphs and presents an efficient algorithm to mine all patterns that satisfies the given query. The current work presents a novel Fast Graph Pattern (FGP) mining algorithm that performs a label-specific walk on a graph and stores all vertices that are visited during the walk and hashes them on a labeled root. Experiments are performed using five benchmark datasets, and the proposed approach is compared with three state-of-the-art methods. The obtained results suggest better performance of the current proposal by achieving five times quicker results.

A Review of Cuckoo Filters for Privacy Protection and Their Applications

As the global digitalization process continues, information is transformed into data and widely used, while the data are also at risk of serious privacy breaches. The Cuckoo filter is a data structure based on the Cuckoo hash. It encrypts data when it is used and can achieve privacy protection to a certain extent. The Cuckoo filter is an alternative to the Bloom filter, with advantages such as support for deleting elements and efficient space utilization. Cuckoo filters are widely used and developed in the fields of network engineering, storage systems, databases, file systems, distributed systems, etc., because they are often used to solve collection element query problems. In recent years, many variants of the Cuckoo filter have emerged based on ideas such as improving the structure and introducing new technologies in order to accommodate a variety of different scenarios, as well as a huge collection. With the development of the times, the improvement of the structure and operation logic of the Cuckoo filter itself has become an important direction for the research of aggregate element query.

Seismic Data Query Algorithm Based on Edge Computing

Edge computing can reduce the transmission pressure of wireless networks in earthquakes by pushing computing functionalities to network edges and avoiding the data transmission to cloud servers. However, this also leads to the scattered storage of data content in each edge server, increasing the difficulty of content search. This paper investigates the seismic data query problem supported by edge computing. We first design a lookup mechanism based on bloom filter, which can quickly determine if there is the information that we need on a particular edge server. Then, the MEC-based data query problem is formulated as an optimization problem whose goal is to minimize the long-term average task delay with the constraints of computing capacity of edge servers. To reduce the complexity of problem, we further transform it as a Markov Decision Process by defining state space, action space and reward function. A novel DQN-based seismic data query algorithm is proposed to solve problem effectively. Extensive simulation-based testing shows that the proposed algorithm performances better when compared with two state-of-the-art solutions.