標題: Titlebook: Database Systems for Advanced Applications; 29th International C Makoto Onizuka,Jae-Gil Lee,Kejing Lu Conference proceedings 2024 The Edito [打印本頁] 作者: Glitch 時間: 2025-3-21 19:23
書目名稱Database Systems for Advanced Applications影響因子(影響力)
書目名稱Database Systems for Advanced Applications影響因子(影響力)學(xué)科排名
書目名稱Database Systems for Advanced Applications網(wǎng)絡(luò)公開度
書目名稱Database Systems for Advanced Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Database Systems for Advanced Applications被引頻次
書目名稱Database Systems for Advanced Applications被引頻次學(xué)科排名
書目名稱Database Systems for Advanced Applications年度引用
書目名稱Database Systems for Advanced Applications年度引用學(xué)科排名
書目名稱Database Systems for Advanced Applications讀者反饋
書目名稱Database Systems for Advanced Applications讀者反饋學(xué)科排名
作者: FACT 時間: 2025-3-22 00:09 作者: 后來 時間: 2025-3-22 03:33 作者: 根除 時間: 2025-3-22 06:42 作者: Budget 時間: 2025-3-22 11:31
om Raw-ETA. After that, we design a SPETAformer block to ensure that the extract module can capture the multidimensional correlation of the above features. The recurrent module further enhances the learning ability of the MIPM in the temporal domain by week, daily, recent, and weather four different作者: glamor 時間: 2025-3-22 14:44
Don Syme,Adam Granicz,Antonio Cisterninorelationships. We also incorporate transformer-encoder or RNN modules to enhance the ability to retain temporal dynamics for time series feature extraction. Several comparisons and ablation experiments on three multivariate time series datasets have been conducted. Our results demonstrate that TimeG作者: glamor 時間: 2025-3-22 20:52 作者: 排出 時間: 2025-3-22 23:05 作者: CRANK 時間: 2025-3-23 05:02
Marketing Your Business Worldwideew entities’ data via neighbors merging. Secondly, we propose an informative temporal memory buffer to help the model emphasize valuable timestamps extracted using influence functions within time intervals. This memory buffer allows INF-GNN to discern influential temporal patterns. Finally, RI loss 作者: 使顯得不重要 時間: 2025-3-23 05:48 作者: sulcus 時間: 2025-3-23 11:57
termed as ., which combines POI data and road network data to generate the distribution of traffic flows. Our model has two novel modules: a graph reconstruction module and a POI supervised contrastive module. The graph reconstruction module includes a .-NN graph builder and a .-NN graph aggregator作者: observatory 時間: 2025-3-23 17:07
https://doi.org/10.1007/978-3-642-59504-2 propose a self-adaptive . solution with a built-in suboperator cost model that dynamically selects the best . strategy at runtime according to the data skew of the target query. We implement the solution in the commercial shared-nothing ., namely CockroachDB and empirical study justifies that the s作者: 財產(chǎn) 時間: 2025-3-23 18:17 作者: Microaneurysm 時間: 2025-3-24 00:05 作者: interference 時間: 2025-3-24 04:54 作者: 胰臟 時間: 2025-3-24 07:59
MIPM: A Multidimensional Information Perception Model for?Estimating Time of?Arrival on?Real Road Neom Raw-ETA. After that, we design a SPETAformer block to ensure that the extract module can capture the multidimensional correlation of the above features. The recurrent module further enhances the learning ability of the MIPM in the temporal domain by week, daily, recent, and weather four different作者: harangue 時間: 2025-3-24 12:02
TimeGAE: A Multivariate Time-Series Generation Method via?Graph Auto Encoderrelationships. We also incorporate transformer-encoder or RNN modules to enhance the ability to retain temporal dynamics for time series feature extraction. Several comparisons and ablation experiments on three multivariate time series datasets have been conducted. Our results demonstrate that TimeG作者: 改正 時間: 2025-3-24 16:06
Beyond SweepLine: Efficient MaxRS Queries over?Inaccurate Location Datar algorithm to address two more complex scenarios: the dynamic MaxRS (DMaxRS) query, which accounts for the varying locations of objects over time, and the MaxRS query on trajectory data (MaxRST), where each object is represented by a sequence of points, not just a singular point. Our experimental r作者: Dri727 時間: 2025-3-24 20:15
Flexible Contact Correlation Learning on?Spatio-Temporal Trajectories potential contact positions using a soft selection module. The contact scores are then derived from the embeddings of the contact trajectory parts. Experiments on two real-world datasets show that ST-TCN outperforms baseline solutions and exhibits superior efficiency in terms of both running time a作者: diabetes 時間: 2025-3-25 02:46 作者: FOLLY 時間: 2025-3-25 05:18 作者: 未開化 時間: 2025-3-25 10:02
POI-Based Traffic Generation via?Supervised Contrastive Learning on?Reconstructed Graph termed as ., which combines POI data and road network data to generate the distribution of traffic flows. Our model has two novel modules: a graph reconstruction module and a POI supervised contrastive module. The graph reconstruction module includes a .-NN graph builder and a .-NN graph aggregator作者: 議程 時間: 2025-3-25 11:45 作者: 水槽 時間: 2025-3-25 17:14
https://doi.org/10.1007/978-3-642-72814-3or calculation pruning. We conduct extensive experiments using three diverse datasets from different domains (ranging from taxis to trucks to pedestrians), which verifies the efficiency of our method.作者: 吹牛者 時間: 2025-3-25 23:42 作者: 新奇 時間: 2025-3-26 01:41 作者: oxidize 時間: 2025-3-26 06:09
0302-9743 lications, DASFAA 2024, held in Gifu, Japan, in July 2024...The total of 147 full papers, along with 85 short papers, presented together in this seven-volume set was carefully reviewed and selected from 722 submissions...Additionally, 14 industrial papers, 18 demo papers and 6 tutorials are included作者: staging 時間: 2025-3-26 09:17 作者: 符合規(guī)定 時間: 2025-3-26 13:54
Artificial Neural Networks in Excel,en, . learns time-aware encodings of these trajectories by a newly proposed time-aware recurrent unit. Moreover, a popularity-weighted attention mechanism is proposed to complete the missing locations. Extensive experiments on four datasets show that . outperforms competitive baselines with up to 25% relative improvements.作者: Regurgitation 時間: 2025-3-26 20:20 作者: 刺穿 時間: 2025-3-27 00:12
Preparing Your Service for Exporttial patterns of traffic flow. Notably, STS2ANet simultaneously learns the tightly coupled spatial-temporal patterns and their divergence over time, resulting in accurate OD prediction. Extensive experiments have been conducted in a real-world dataset, and the results demonstrate the performance superiority of STS2ANet against baselines.作者: 游行 時間: 2025-3-27 03:02
https://doi.org/10.1007/978-981-16-2968-6results by matching with recent traffic data. We conduct experiments on two real-world datasets, and the results demonstrate that STMGF outperforms all baseline models and achieves state-of-the-art performance.作者: violate 時間: 2025-3-27 06:06
Trajectory Completion via?Context-Guided Neural Filtering and?Encodingen, . learns time-aware encodings of these trajectories by a newly proposed time-aware recurrent unit. Moreover, a popularity-weighted attention mechanism is proposed to complete the missing locations. Extensive experiments on four datasets show that . outperforms competitive baselines with up to 25% relative improvements.作者: 阻撓 時間: 2025-3-27 09:52 作者: BYRE 時間: 2025-3-27 14:56 作者: arbovirus 時間: 2025-3-27 21:11
STMGF: An Effective Spatial-Temporal Multi-granularity Framework for?Traffic Forecastingresults by matching with recent traffic data. We conduct experiments on two real-world datasets, and the results demonstrate that STMGF outperforms all baseline models and achieves state-of-the-art performance.作者: 灰心喪氣 時間: 2025-3-27 22:28 作者: 發(fā)微光 時間: 2025-3-28 05:44 作者: Spinal-Fusion 時間: 2025-3-28 06:18
Trajectory Completion via?Context-Guided Neural Filtering and?Encodingoften highly sparse and incomplete, which has become a key bottleneck that limits the applicability of trajectory analysis techniques. While many existing sequential models are seemingly applicable to the trajectory completion problem, they often suffer severely from data sparsity and irregularity a作者: 陳列 時間: 2025-3-28 14:29 作者: 葡萄糖 時間: 2025-3-28 16:34
On Compressing Historical Cliques in?Temporal Graphsd bioinformatics. Many real-world graphs change over time, with edges arriving continuously, and each edge has a timestamp representing the arrival time of that edge; such graphs are also known as temporal graphs. All maximal cliques in all snapshots since all possible historical moments are called 作者: 沒血色 時間: 2025-3-28 22:36 作者: 體貼 時間: 2025-3-29 00:58
MIPM: A Multidimensional Information Perception Model for?Estimating Time of?Arrival on?Real Road Nees. Thus, despite many existing works focusing on improving the efficiency and accuracy of the transportation system, however, few of them can handle multidimensional features on road networks. In this paper, we focus on a famous problem of the intelligent transportation system named estimated time 作者: 代理人 時間: 2025-3-29 06:21 作者: demote 時間: 2025-3-29 10:03
TimeGAE: A Multivariate Time-Series Generation Method via?Graph Auto Encoders this issue, time series generation methods have emerged as a promising approach to alleviate data scarcity. However, most existing methods do not explicitly consider multivariate time series, thereby failing to fully exploit the potential spatial dependencies among different variables. The ability作者: 過分自信 時間: 2025-3-29 13:54 作者: 積極詞匯 時間: 2025-3-29 18:33
Simulating Individual Infection Risk over?Big Trajectory Data attention. They are helpful in predicting epidemic transmission trends and mitigating the spread of infectious diseases. In this light, we study a new problem of Individual Infection Risk Assessment (IIRA) on the basis of fine-grained trajectory data. The problem aims to quantify the infection risk作者: aptitude 時間: 2025-3-29 19:56
Flexible Contact Correlation Learning on?Spatio-Temporal Trajectoriesch or tracing, aiming to identify all trajectories in contact with a query trajectory. However, these studies only consider spatial contacts at specific timestamps, and highly rely on precise data with consistent sampling rates and aligned timestamps. In light of these limitations, we investigate th作者: depreciate 時間: 2025-3-30 02:47
Inductive Spatial Temporal Prediction Under Data Drift with?Informative Graph Neural Networkms, stock markets). However, external events (e.g., urban structural growth, market crash) and emerging new entities (e.g., locations, stocks) can undermine prediction accuracy by inducing data drift over time. Most existing studies extract invariant patterns to counter data drift but ignore pattern作者: 內(nèi)行 時間: 2025-3-30 06:43 作者: 環(huán)形 時間: 2025-3-30 08:50
Streaming Trajectory Segmentation Based on?Stay-Point Detection offline data, thus cannot segment trajectory streams efficiently. In this paper, we propose a novel and efficient streaming trajectory segmentation algorithm based on stay point detection. Our algorithm can dynamically update in real time based on input points, eliminating the need for scanning the作者: Insul島 時間: 2025-3-30 16:20 作者: CLEAR 時間: 2025-3-30 17:43
POI-Based Traffic Generation via?Supervised Contrastive Learning on?Reconstructed Graphflow without using historical traffic data. Since road network and Point of Interest (POI) data can provide a more comprehensive picture of traffic patterns, most previous methods use both or either of them to generate traffic flow. However, roadnet graph in real-world has bias and abnormal structur作者: anatomical 時間: 2025-3-30 23:58 作者: 缺陷 時間: 2025-3-31 01:58
