Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting

Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

Embedding-based approximate query for knowledge graph

To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes.This classification transforms the query problem into three constraints,from which approximate information is extracted.Second,candidates are generated by calculating the similarity between embeddings.Finally,a deep neural network model is designed,incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance.This model identifies the distance between nodes using their embeddings and constructs a score function.k nodes are returned as the query results.The results show that the proposed method can return both exact results and approximate matching results.On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse),this method exhibits superior performance in terms of precision and recall,returning results in 0.10 and 0.03 s,respectively.This indicates greater efficiency compared to PathSim and other comparative methods.

Multi-Scale Location Attention Model for Spatio-Temporal Prediction of Disease Incidence

Due to the increasingly severe challenges brought by various epidemic diseases,people urgently need intelligent outbreak trend prediction.Predicting disease onset is very important to assist decision-making.Most of the exist-ing work fails to make full use of the temporal and spatial characteristics of epidemics,and also relies on multi-variate data for prediction.In this paper,we propose a Multi-Scale Location Attention Graph Neural Networks(MSLAGNN)based on a large number of Centers for Disease Control and Prevention(CDC)patient electronic medical records research sequence source data sets.In order to understand the geography and timeliness of infec-tious diseases,specific neural networks are used to extract the geography and timeliness of infectious diseases.In the model framework,the features of different periods are extracted by a multi-scale convolution module.At the same time,the propagation effects between regions are simulated by graph convolution and attention mechan-isms.We compare the proposed method with the most advanced statistical methods and deep learning models.Meanwhile,we conduct comparative experiments on data sets with different time lengths to observe the predic-tion performance of the model in the face of different degrees of data collection.We conduct extensive experi-ments on real-world epidemic-related data sets.The method has strong prediction performance and can be readily used for epidemic prediction.

A method for improving graph queries processing using positional inverted index (P.I.I) idea in search engines and parallelization techniques

The idea of positional inverted index is exploited for indexing of graph database. The main idea is the use of hashing tables in order to prune a considerable portion of graph database that cannot contain the answer set. These tables are implemented using column-based techniques and are used to store graphs of database, frequent sub-graphs and the neighborhood of nodes. In order to exact checking of remaining graphs, the vertex invariant is used for isomorphism test which can be parallel implemented. The results of evaluation indicate that proposed method outperforms existing methods.

Query Acceleration of Graph Databases by ID Caching Technology

In this paper, we approach the design of ID caching technology(IDCT) for graph databases, with the purpose of accelerating the queries on graph database data and avoiding redundant graph database query operations which will consume great computer resources. Traditional graph database caching technology(GDCT)needs a large memory to store data and has the problems of serious data consistency and low cache utilization. To address these issues, in the paper we propose a new technology which focuses on ID allocation mechanism and high-speed queries of ID on graph databases. Specifically, ID of the query result is cached in memory and data consistency is achieved through the real-time synchronization and cache memory adaptation. In addition, we set up complex queries and simple queries to satisfy all query requirements and design a mechanism of cache replacement based on query action time, query times, and memory capacity, thus improving the performance furthermore.Extensive experiments show the superiority of our techniques compared with the traditional query approach of graph databases.

Query Optimization Framework for Graph Database in Cloud Dew Environment

The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of resources.The challenge of query optimization for relational database systems is a combinatorial optimization problem,which renders exhaustive search impossible as query sizes rise.Increases in CPU performance have surpassed main memory,and disk access speeds in recent decades,allowing data compression to be used—strategies for improving database performance systems.For performance enhancement,compression and query optimization are the two most factors.Compression reduces the volume of data,whereas query optimization minimizes execution time.Compressing the database reduces memory requirement,data takes less time to load into memory,fewer buffer missing occur,and the size of intermediate results is more diminutive.This paper performed query optimization on the graph database in a cloud dew environment by considering,which requires less time to execute a query.The factors compression and query optimization improve the performance of the databases.This research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.

Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers.Servers on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure state.The issue of privacy data protection has become an important obstacle to data sharing and usage.How to query outsourcing graph data safely and effectively has become the focus of research.Adjacency query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same time.This work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a solution.In our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud server.Our proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental analysis.The research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

面向远程内存图数据库的应用感知分离式存储设计

图数据在各种应用中日益普及,其因涵盖多种实体类型和存在丰富的关联关系而备受关注.对于图数据库用户而言,高效的图查询服务是保障系统性能的关键因素.随着数据量增加,单机图数据库很难满足将所有数据存储在内存中的需求,而分布式图数据库在拓展性和资源利用率方面受到挑战.基于RDMA的远程内存系统的引入为克服这些挑战提供了一种新的选择,通过分离计算和存储资源,实现了更为灵活的内存使用方式.然而,在使用远程内存的情况下如何最大程度地优化图查询性能成为了当前研究的重点问题.文中首先分析了利用操作系统分页机制透明使用远程内存构建图数据库存在的问题,并在应用层次上设计了远程内存图数据库的存储模型.根据不同数据的特点和访问模式,设计了属性图在远程内存中的存储结构,优化了数据布局和访问路径.实验结果表明,在本地内存受限的情况下,与透明使用远程内存相比,应用感知的设计方式的端到端性能最高提升了12倍.

面向周期边查询的高效图流概要技术

当前图流概要技术不能在小内存下实现高效准确的图流测量,也无法完成周期边查询,为此提出面向周期边查询的图流概要技术——周期交互矩阵(PIM).PIM为混合结构,由存储重边的二维邻接矩阵和存储轻边的三维邻接矩阵组成,提高了内存效率.二维邻接矩阵保留重边标识、权重和时间戳,实时完成包括周期边查询在内的多种查询任务.设计基于权重和时间的替换策略,使用共享哈希技术以提高查询精度和插入查询效率.实验结果表明,PIM在小内存下实时高效地完成了多种图流查询任务,能够准确地召回所有频繁边、频繁点和周期边.对比当前图流概要技术,PIM将查询任务的平均相对误差降低了91.41%~99.54%.

An attention graph stacked autoencoder for anomaly detection of electro-mechanical actuator using spatio-temporal multivariate signals

Health monitoring of electro-mechanical actuator(EMA)is critical to ensure the security of airplanes.It is difficult or even impossible to collect enough labeled failure or degradation data from actual EMA.The autoencoder based on reconstruction loss is a popular model that can carry out anomaly detection with only consideration of normal training data,while it fails to capture spatio-temporal information from multivariate time series signals of multiple monitoring sensors.To mine the spatio-temporal information from multivariate time series signals,this paper proposes an attention graph stacked autoencoder for EMA anomaly detection.Firstly,attention graph con-volution is introduced into autoencoder to convolve temporal information from neighbor features to current features based on different weight attentions.Secondly,stacked autoencoder is applied to mine spatial information from those new aggregated temporal features.Finally,based on the bench-mark reconstruction loss of normal training data,different health thresholds calculated by several statistic indicators can carry out anomaly detection for new testing data.In comparison with tra-ditional stacked autoencoder,the proposed model could obtain higher fault detection rate and lower false alarm rate in EMA anomaly detection experiment.

Probabilistic Top-k Query:Model and Application on Web Traffic Analysis

Top-k ranking of websites according to traffic volume is important for Internet Service Providers(ISPs) to understand network status and optimize network resources. However, the ranking result always has a big deviation with actual rank for the existence of unknown web traffic, which cannot be identified accurately under current techniques. In this paper, we introduce a novel method to approximate the actual rank. This method associates unknown web traffic with websites according to statistical probabilities. Then, we construct a probabilistic top-k query model to rank websites. We conduct several experiments by using real HTTP traffic traces collected from a commercial ISP covering an entire city in northern China. Experimental results show that the proposed techniques can reduce the deviation existing between the ground truth and the ranking results vastly. In addition, we find that the websites providing video service have higher ratio of unknown IP as well as higher ratio of unknown traffic than the websites providing text web page service. Specifically, we find that the top-3 video websites have more than 90% of unknown web traffic. All these findings are helpful for ISPs understanding network status and deploying Content Distributed Network(CDN).

Spark GraphX上的SPARQL查询处理算法

资源描述框架(resource description framework,RDF)由于其表示的灵活性和天然的图数据模型而变得越来越流行。与此同时,RDF数据的数据量也在以惊人的速度增长。由于数据量的增长,在单机上存储和查询RDF数据变得越来越不方便,从而激发了分布式存储查询的需求。学术界在分布式存储查询系统,例如Hadoop、Spark上已经做了大量的工作。基于Hadoop的分布式存储查询方式的主要缺点是中间结果需要被写回磁盘,从而产生大量的I/O操作。提出了一种新的在Spark Graph X上进行SPARQL查询评估的方法SQX,将RDF数据视为一个带标签的属性图,提出了一种新的查询计划生成方案并且通过图并行的方式实现SPARQL查询评估。SQX采用了一种"查询树匹配"+"结果过滤"的方法。针对每一个SPARQL查询,产生相应的查询树和约束条件。在每一轮的超级步中,查询树中的多条边可以被并行处理,对迭代执行完毕后的结果进行过滤,满足约束条件的将作为最终的结果。实验结果表明,算法能够有效处理SPARQL查询并且具有良好的可扩展性。

A graph-based sliding window multi-join over data stream

Join operation is a critical problem when dealing with sliding window over data streams. There have been many optimization strategies for sliding window join in the literature, but a simple heuristic is always used for selecting the join sequence of many sliding windows, which is ineffectively. The graph-based approach is proposed to process the problem. The sliding window join model is introduced primarily. In this model vertex represent join operator and edge indicated the join relationship among sliding windows. Vertex weight and edge weight represent the cost of join and the reciprocity of join operators respectively. Then good query plan with minimal cost can be found in the model. Thus a complete join algorithm combining setting up model, finding optimal query plan and executing query plan is shown. Experiments show that the graph-based approach is feasible and can work better in above environment.

An Arrhythmia Intelligent Recognition Method Based on a Multimodal Information and Spatio-Temporal Hybrid Neural Network Model

Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness.

基于知识图谱的番茄种植管理可视化查询

为提高获取番茄种植管理知识的速度与准确率,研究了以图形式描述番茄在不同环境的种植管理,并基于知识图谱构建了番茄种植管理可视化查询系统。该方法利用“自顶向下”和“自底向上”的模块化CREATE解决了Neo4j的缓慢和准确率问题,并利用PyQt框架构建可视化查询界面,通过问题预处理和语义相似度计算输出最合适的番茄种植管理知识。试验结果表明:该方法的平均响应时间和平均准确率比Cypher查询语言分别提高88.33%及1.97%,可操性比Cypher语言友好。研究结果可以在不同环境下为番茄生产管理提供高质量的种植管理建议。

健身运动知识表达与智能查询系统构建

科学健身运动在促进大众体质健康、心理健康等方面均发挥着重要作用。在数智时代背景下,健身运动知识的智能查询与精准查询成为延伸全民健身公共服务供给,提高全民健身公共服务质量的重要环节。鉴于此,通过知识图谱与大语言模型的协同互补,探索构建知识图谱驱动的健身运动知识智能查询系统,提出涵盖知识抽取与表达、知识整合与管理、知识查询与问答于一体的实施方案,进而为实现精准化健身运动知识智能查询的研究与应用提供参考。

正确性可验证的密文图数据最短路径外包计算方案

地理位置、社交网络等海量图数据应用广泛且包含大量隐私,通常需要安全的外包计算来提供多样化的查询服务。然而,如何设计正确性可验证的图数据外包计算协议仍是公开的难题。为此,提出了加密图数据上正确性可验证的精确最短路径外包计算方案。该方案利用加法同态加密构造密态图数据上的广度优先最短路径计算算法,支持加密图数据的精确最短距离查询外包计算;其次,基于双线性映射累加器构造最短路径外包计算结果的概率正确性验证机制。分析和证明表明,该方案能以概率可靠性实现正确性可验证的精确最短路径的外包计算,具备随机预言模型下的IND-CCA2安全。对比实验结果表明,所提方案相比其他相关方案在安全性、功能性方面有显著优势,性能上较已有可验证图数据外包计算方案在初始化及加密环节、查询环节、验证及解密环节的时间开销分别降低了0.15%~23.19%,12.91%~30.89%和1.13%~18.62%。

中医汤剂知识图谱的构建与样例查询方法研究

利用现代化信息技术为中医药的科研、传播、教学等方面提供有效的研究方法与途径是当前的研究热点,更是未来的发展方向。知识图谱通过其独特的知识展示,为挖掘医学知识中隐藏的关联关系提供方法,为中医药的传承发展提供动力,为中医药的学习研究提供参考。中医方剂作为中医治疗的重要手段之一,扮演着重要的角色。利用知识图谱技术,实现对中药方剂中汤剂类型的经方进行实体和关系的提取,并构建一个中医汤剂知识图谱。通过对中医文献、中医医案、中医方剂数据库、名老中医经验等非结构化及半结构化信息进行深入分析和文本挖掘,将整理好的知识存入Neo4j图数据库中,并使用中医药物图片作为相应中药实体节点显示,帮助理解知识图谱中的实体和关系并发现隐藏的模式、趋势和关联性。设计一种样例查询算法进行查询分析,为中医汤剂知识的学习、浏览及检索提供新思路,为分析挖掘中医药中潜在的关联关系提供新方案,为中医药知识的表达及可视化提供新范式。