Proceedings of the 18th International Symposium on Spatial and Temporal Data最新文献

An Interactive Map-based System for Visually Exploring Goods Movement based on GPS Traces 一种基于GPS轨迹的交互式地图视觉探索货物运动系统

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609975

Reza Safarzadeh, Yunli Wang, Sun Sun, Xin Wang

引用次数: 0

Evaluation of Vessel CO2 Emissions Methods using AIS Trajectories 利用AIS轨迹评估船舶二氧化碳排放方法

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609960

Song Wu, K. Torp, M. Sakr, E. Zimányi

引用次数: 0

Recommending the Least Congested Indoor-Outdoor Paths without Ignoring Time 在不忽略时间的前提下，推荐最不拥挤的室内外路径

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609969

Vasilis Ethan Sarris, Panos K. Chrysanthis, Constantinos Costa

引用次数: 0

Traffic Spatial-Temporal Prediction Based on Neural Architecture Search 基于神经结构搜索的交通时空预测

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609962

Dongran Zhang, Gang Luo, Jun Li

{"title":"Traffic Spatial-Temporal Prediction Based on Neural Architecture Search","authors":"Dongran Zhang, Gang Luo, Jun Li","doi":"10.1145/3609956.3609962","DOIUrl":"https://doi.org/10.1145/3609956.3609962","url":null,"abstract":"Traffic spatial-temporal prediction is essential for intelligent transportation systems. However, the current approach relies heavily on expert knowledge and time-consuming manual modeling. Neural architecture search can build models adaptively, but it is rarely used for traffic spatial-temporal prediction, nor is it designed specifically for traffic spatial-temporal feature. In response to the above problems, we propose neural architecture search spatial-temporal prediction (NASST), which is a method to automatically generate a traffic spatial-temporal prediction network by performing a differentiable neural network architecture search in an optimized search space. First, we adopt a differentiable neural architecture search method to continuously relax the discrete traffic spatial-temporal prediction model architecture search, and adopt a fusion strategy of comprehensive concatenate and addition (CA) to achieve efficient neural architecture search. Second, we optimize the search space and introduce a series of classic traffic spatial-temporal feature extraction modules, which are more in line with the architectural requirements of traffic spatial-temporal prediction network. Finally, our model is validated on two public traffic datasets and achieves the best predictions. Compared with traditional manual modeling methods, our method can realize the automatic search of high-precision predictive model architectures, which improves the modeling efficiency.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"859 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133746504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DAISTIN: A Data-Driven AIS Trajectory Interpolation Method 一种数据驱动的AIS轨迹插值方法

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609961

Búgvi Benjamin Magnussen, Nikolaj Bläser, Huan Lu

{"title":"DAISTIN: A Data-Driven AIS Trajectory Interpolation Method","authors":"Búgvi Benjamin Magnussen, Nikolaj Bläser, Huan Lu","doi":"10.1145/3609956.3609961","DOIUrl":"https://doi.org/10.1145/3609956.3609961","url":null,"abstract":"The Automatic Identification System (AIS) provides global vessel positioning data used in a variety of maritime applications. However, AIS suffers from transmission signal gaps, which causes vessels to disappear from AIS records for prolonged periods and poses a major challenge for the use of AIS data. In this paper, we propose a novel Data-driven AIS Trajectory INterpolation method (DAISTIN) to address AIS signal gaps. DAISTIN first makes use of massive raw AIS data to delicately construct a graph that well represents vessel movements. Next, given a gap between two locations A and B in an AIS trajectory, DAISTIN searches the graph for the shortest path from A to B and uses the path to interpolate the vessel’s whereabouts in between. To cope with large amounts of AIS data, we design a geometric sampling method for DAISTIN to select representative AIS data points for the graph construction. Finally, we design a postprocessing step for DAISTIN to fine-tune the quality of interpolated results. We conduct extensive experiments to compare DAISTIN with selected existing methods. The results verify the superiority of DAISTIN in terms of multiple performance metrics.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Primitive for Processing Temporal Joins 处理时态连接的新原语

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609968

Meghdad Mirabi, Leila Fathi, Anton Dignös, J. Gamper, Carsten Binnig

引用次数: 0

VoCC: Vortex Correlation Clustering Based on Masked Hough Transformation in Spatial Databases 基于掩蔽Hough变换的空间数据库涡旋相关聚类

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609971

Nelson Tavares de Sousa, Yannick Wölker, M. Renz, A. Biastoch

{"title":"VoCC: Vortex Correlation Clustering Based on Masked Hough Transformation in Spatial Databases","authors":"Nelson Tavares de Sousa, Yannick Wölker, M. Renz, A. Biastoch","doi":"10.1145/3609956.3609971","DOIUrl":"https://doi.org/10.1145/3609956.3609971","url":null,"abstract":"A special focus in data mining is to identify agglomerations of data points in spatial or spatio-temporal databases. Multiple applications have been presented to make use of such clustering algorithms. However, applications exist, where not only dense areas have to be identified, but also requirements regarding the correlation of the cluster to a specific shape must be met, i.e. circles. This is the case for eddy detection in marine science, where eddies are not only specified by their density, but also their circular-shaped rotation. Traditional clustering algorithms lack the ability to take such aspects into account. In this paper, we introduce Vortex Correlation Clustering which aims to identify those correlated groups of objects oriented along a vortex. This can be achieved by adapting the Circle Hough Transformation, already known from image analysis. The presented adaptations not only allow to cluster objects depending on their location next to each other, but also allows to take the orientation of individual objects into considerations. This allows for a more precise clustering of objects. A multi-step approach allows to analyze and aggregate cluster candidates, to also include final clusters, which do not perfectly satisfy the shape condition. We evaluate our approach upon a real world application, to cluster particle simulations composing such shapes. Our approach outperforms comparable methods of clustering for this application both in terms of effectiveness and efficiency.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128113646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Scalable Unified System for Seeding Regionalization Queries 用于播种区域化查询的可扩展统一系统

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609980

Hussah Alrashid, A. Magdy

引用次数: 0

Towards Workload Trend Time Series Probabilistic Prediction via Probabilistic Deep Learning 基于概率深度学习的工作负荷趋势时间序列概率预测

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609979

Li Ruan, Heng Guo, Yunzhi Xue, Tao Ruan, Yuetiansi Ji, Limin Xiao

{"title":"Towards Workload Trend Time Series Probabilistic Prediction via Probabilistic Deep Learning","authors":"Li Ruan, Heng Guo, Yunzhi Xue, Tao Ruan, Yuetiansi Ji, Limin Xiao","doi":"10.1145/3609956.3609979","DOIUrl":"https://doi.org/10.1145/3609956.3609979","url":null,"abstract":"The workloads of autonomous driving traffic accident cloud data centers exhibit high variance and uncertainty. Accurate modeling and prediction of the variance and uncertainty of cloud workloads are crucial for the realization of reliable resource management in cloud data centers. Existing solutions are point prediction methods that can not capture the variance and uncertainty of the cloud workloads. In this paper, we propose a workload probabilistic prediction method with deep learning to model and predict the variance and uncertainty of cloud workload. Our method is a hybrid deep learning model which combines exponential smoothing, bidirectional long short-term memory (BLSTM) and quantile regression. First, a cloud workload pre-processing method based on exponential smoothing is proposed to smooth the high variance feature of cloud workloads. Then, a BLSTM based cloud workload algorithm is introduced. Finally, a differentiable quantile loss function is introduced into the prediction model to generate predictions of multiple quantiles. The experimental results on the Google cluster trace show that our method outperforms other four baseline models.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116111509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Energy Aware Adaptive Clustering Protocol for Energy Harvesting Wireless Sensor Networks 能量采集无线传感器网络的能量感知自适应聚类协议

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609958

Ning Li, Winston K.G. Seah, Zhengyu Hou, Bing Jia, Baoqi Huang, Wuyungerile Li

{"title":"An Energy Aware Adaptive Clustering Protocol for Energy Harvesting Wireless Sensor Networks","authors":"Ning Li, Winston K.G. Seah, Zhengyu Hou, Bing Jia, Baoqi Huang, Wuyungerile Li","doi":"10.1145/3609956.3609958","DOIUrl":"https://doi.org/10.1145/3609956.3609958","url":null,"abstract":"Wireless sensor network (WSN) has many applications, such as, military scenarios, habitat monitoring and home security. In recent years, with the advancement of energy harvesting (EH) technology, nodes can obtain available energy from the surrounding environment for their own use, thus extending their lifetimes. Under these conditions, research aimed at improving the WSN lifecycle has further shifted towards improving the performance of the network, albeit subject to unique energy harvesting constraints. This paper proposes an energy prediction algorithm for the devices and an Energy and Density Adaptive Clustering (EDAC) protocol to improve network throughput and transmission ratio for EH-powered WSNs. Based on the EH characteristics, we first employed Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (Bi-LSTM) algorithm for energy prediction, then we divide the energy of the sensor nodes into three levels: low, medium, and high energy levels. At high energy levels, nodes can be selected as cluster head nodes, while at low energy levels, nodes must sleep and charge. EDAC first uses the K-Means clustering algorithm to dynamically cluster the surviving nodes in each round and sets a threshold to partition the clustering density. On this basis, a new adaptive cluster head election formula is proposed for cluster head election based on the energy levels of nodes, the predicted energy of the next stage, and the density of clusters. In the stable communication stage of the network, we introduce a \"backup cluster head\" to temporarily forward the remaining data packets within the cluster when the current cluster head expires. Our simulation results show that our algorithm significantly improves throughput and data transfer rate compared to the traditional and improved clustering protocols.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125325212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0