標題: Titlebook: Database Systems for Advanced Applications; 28th International C Xin Wang,Maria Luisa Sapino,Hongzhi Yin Conference proceedings 2023 The Ed [打印本頁] 作者: 難受 時間: 2025-3-21 17:06
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)學科排名
書目名稱Database Systems for Advanced Applications網(wǎng)絡公開度
書目名稱Database Systems for Advanced Applications網(wǎng)絡公開度學科排名
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讀者反饋學科排名
作者: ALB 時間: 2025-3-21 20:15
978-3-031-30636-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: probate 時間: 2025-3-22 02:58 作者: Legion 時間: 2025-3-22 05:49 作者: neutralize 時間: 2025-3-22 12:16 作者: 輕彈 時間: 2025-3-22 14:40
https://doi.org/10.1007/978-3-658-05653-73rd generation Intel Xeon Scalable Processors with the 2nd generation Intel Optane PM. eADR ensures that data stored within the CPU caches will be flushed to PM upon the power failure..In the eADR platform, previous PM-based work suffered more read/write amplification and random access problems, and作者: 輕彈 時間: 2025-3-22 21:06 作者: atopic 時間: 2025-3-22 21:42
https://doi.org/10.1007/978-3-658-23067-8orithms use either heuristic or greedy strategy to perform node packing and mainly have 2 limitations: (1) They greedily optimize the short-term but not the overall tree costs. (2) They enforce full-packing of each node. These both limit the built tree structure. To address these limitations, we pro作者: miscreant 時間: 2025-3-23 04:05
https://doi.org/10.1007/978-3-658-23067-8y and sell data through the data market. Meanwhile, a variety of data pricing mechanisms have been proposed. However, since most of them concentrate on relational data, little is known about graph data pricing, particularly incomplete graph data. In this paper, we mainly focus on the pricing problem作者: dithiolethione 時間: 2025-3-23 08:11
https://doi.org/10.1007/978-3-8349-8156-1nd knowledge graphs. As a fundamental task, the label-constrained reachability (LCR) query asks whether a given vertex . can reach another vertex ., only using a restricted set of given edge labels. However, existing works build a heavy index while taking too much time for answering queries online, 作者: Arresting 時間: 2025-3-23 11:29
https://doi.org/10.1007/978-3-8349-8156-1c. However, it is hard to make a precise estimation, which is not only related with the physical join implementations (hash, sort, loop) but also with the corresponding parameters, like the size of the data, the number of partitions, the number of threads in a modern hash join. Existing works rely o作者: configuration 時間: 2025-3-23 14:05 作者: critic 時間: 2025-3-23 19:13
https://doi.org/10.1007/978-1-0716-3211-6on-Gaussian and non-linear properties. Many businesses rely on accurate TS forecasting, under these complications, to help with operational efficiencies. In this paper, we present a novel approach for Hierarchical Time Series (HTS) prediction via trainable attentive reconciliation and Normalizing Fl作者: 里程碑 時間: 2025-3-23 23:39 作者: FLAGR 時間: 2025-3-24 02:44
https://doi.org/10.1007/978-1-0716-3211-6st and accurate MTS anomaly detection methods to support fast troubleshooting in cloud computing, micro-service systems, etc. . is fast in the sense that it reduces the training time by as high as 38.2% compared to the state-of-the-art (SOTA) deep learning methods that focus on training speed. . is 作者: Lipohypertrophy 時間: 2025-3-24 07:17
https://doi.org/10.1007/978-1-0716-3211-6ns of the repaired time series, along with the raw time series, are often stored directly in the system for the users. However, as the scale of data explodes, high storage cost becomes a non-negligible problem. To address this problem, we propose RpDelta, a repaired time series storage strategy, und作者: 施魔法 時間: 2025-3-24 13:52
https://doi.org/10.1007/978-1-0716-3211-6search, trend analysis, and forecasting. In practice, unsupervised learning is strongly preferred owing to sparse labeling. Most existing studies focus on the representation of independent subseries and do not take into consideration the relationships among different subseries. In certain situations作者: 精致 時間: 2025-3-24 14:52
https://doi.org/10.1007/978-1-0716-3211-6d into key points detection tasks due to their significant representation learning ability. However, in contrast to common time series classification and prediction tasks, the target key points correspond to significantly different time-series patterns and account for an extremely small proportion i作者: CLAP 時間: 2025-3-24 19:39
https://doi.org/10.1007/978-1-0716-3211-6ped to uncover the anomaly instances from data. However, the labelled data is always limited and costly for real applications, which adds to the difficulty of identifying various anomalies in multivariate time series. In this paper, we propose a novel active anomaly detection method with sparse neur作者: Vldl379 時間: 2025-3-25 02:51 作者: 聯(lián)合 時間: 2025-3-25 05:58
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/263403.jpg作者: 你敢命令 時間: 2025-3-25 11:06
Selling in International MarketsG-TQA is making a different bottom-up paradigm. Extensive experiments on a widely-used benchmark and online experiments on a practical industry system demonstrate the superiority of SIG-TQA. Currently, our SIG-TQA has been applied to a real-world Table QA system, and its code is available on ..作者: 惡心 時間: 2025-3-25 12:37 作者: homeostasis 時間: 2025-3-25 17:02 作者: 咽下 時間: 2025-3-25 21:50
https://doi.org/10.1007/978-3-658-05653-7M. PFtree reduces memory allocations in critical paths by allocating bulk memory when creating a leaf array. Then, we design an adaptive persistence way based on data block size for PFtree to fully use PM bandwidth. Experimental results show that our proposed PFtree outperforms the radix tree by up 作者: 案發(fā)地點 時間: 2025-3-26 00:47
https://doi.org/10.1007/978-3-658-23067-8-key scenarios. Because of this, we propose a Multi-key LBF (MLBF) data structure, which contains a value-interaction-based multi-key classifier and a multi-key Bloom filter. To reduce FPR, we further propose an Interval-based MLBF, which divides keys into specific intervals according to the data di作者: 清澈 時間: 2025-3-26 07:25
https://doi.org/10.1007/978-3-8349-8156-1eGAT, a heterogonous graph neural network, to fully capture the edge weights (the number of function calls) in the join-graph. The embeddings learned from ReGAT can be used to predict the running time. In addition, we optimize JG2Time with a multi-task model that also predicts the times of function 作者: 阻塞 時間: 2025-3-26 08:34
https://doi.org/10.1007/978-1-0716-3211-6ced Contrastive Learning (BCL), which avoids excessive intra-class compaction of tail classes by introducing a balanced supervised contrastive loss with hierarchical prototypes, resulting in a balanced feature space and better generalization. From the data perspective, we explore the effectiveness o作者: Fibrin 時間: 2025-3-26 13:43 作者: Magisterial 時間: 2025-3-26 16:53 作者: 燦爛 時間: 2025-3-26 21:11
https://doi.org/10.1007/978-1-0716-3211-6turing temporal dependence in MTS, and randomized perturbation for avoiding overfitting of anomalous dependence in the training data. We present simple instantiations of . to attain fast training speed, where we propose a simple randomized perturbation method. The superior performance of . over SOTA作者: Decline 時間: 2025-3-27 04:10
https://doi.org/10.1007/978-1-0716-3211-6e series. Moreover, to deal with the challenge of variable lengths of input subseries of multivariate time series, a temporal pyramid pooling (TPP) method is applied to construct input vectors with equal length. The experimental results show that our model has substantial advantages compared with ot作者: 亞麻制品 時間: 2025-3-27 07:34
https://doi.org/10.1007/978-1-0716-3211-6ith a higher generalization ability; 2) a joint loss function providing both dynamic focal adaptation and probability compensation by extreme value theory. Extensive experiments using both real-world and benchmark datasets are conducted. The results indicate that our method outperforms our rival met作者: Cardiac-Output 時間: 2025-3-27 12:23
https://doi.org/10.1007/978-1-0716-3211-6ctive anomaly detection with the design of sample selection strategy and abnormal feature order generation algorithm, which extracts the important features of instances and reduce the cost of human intelligence. Experimental results on four real-life datasets show SNN-AAD has good detection performa作者: 加劇 時間: 2025-3-27 16:37
Semantic-Driven Instance Generation for?Table Question AnsweringG-TQA is making a different bottom-up paradigm. Extensive experiments on a widely-used benchmark and online experiments on a practical industry system demonstrate the superiority of SIG-TQA. Currently, our SIG-TQA has been applied to a real-world Table QA system, and its code is available on ..作者: IRS 時間: 2025-3-27 18:35 作者: 慢跑鞋 時間: 2025-3-28 01:24 作者: 反饋 時間: 2025-3-28 05:34
PFtree: Optimizing Persistent Adaptive Radix Tree for?PM Systems on?eADR PlatformM. PFtree reduces memory allocations in critical paths by allocating bulk memory when creating a leaf array. Then, we design an adaptive persistence way based on data block size for PFtree to fully use PM bandwidth. Experimental results show that our proposed PFtree outperforms the radix tree by up 作者: lobster 時間: 2025-3-28 08:37 作者: Systemic 時間: 2025-3-28 11:26
JG2Time: A Learned Time Estimator for?Join Operators Based on?Heterogeneous Join-GraphseGAT, a heterogonous graph neural network, to fully capture the edge weights (the number of function calls) in the join-graph. The embeddings learned from ReGAT can be used to predict the running time. In addition, we optimize JG2Time with a multi-task model that also predicts the times of function 作者: Spinal-Tap 時間: 2025-3-28 17:16
Long-Tailed Time Series Classification via?Feature Space Rebalancingced Contrastive Learning (BCL), which avoids excessive intra-class compaction of tail classes by introducing a balanced supervised contrastive loss with hierarchical prototypes, resulting in a balanced feature space and better generalization. From the data perspective, we explore the effectiveness o作者: relieve 時間: 2025-3-28 22:37 作者: paltry 時間: 2025-3-29 01:09 作者: Intruder 時間: 2025-3-29 05:30 作者: Minikin 時間: 2025-3-29 10:39
GP-HLS: Gaussian Process-Based Unsupervised High-Level Semantics Representation Learning of?Multivare series. Moreover, to deal with the challenge of variable lengths of input subseries of multivariate time series, a temporal pyramid pooling (TPP) method is applied to construct input vectors with equal length. The experimental results show that our model has substantial advantages compared with ot作者: Ancillary 時間: 2025-3-29 12:44
Towards Time-Series Key Points Detection Through Self-supervised Learning and Probability Compensatiith a higher generalization ability; 2) a joint loss function providing both dynamic focal adaptation and probability compensation by extreme value theory. Extensive experiments using both real-world and benchmark datasets are conducted. The results indicate that our method outperforms our rival met作者: 駁船 時間: 2025-3-29 17:36
SNN-AAD: Active Anomaly Detection Method for?Multivariate Time Series with?Sparse Neural Networkctive anomaly detection with the design of sample selection strategy and abnormal feature order generation algorithm, which extracts the important features of instances and reduce the cost of human intelligence. Experimental results on four real-life datasets show SNN-AAD has good detection performa作者: 粘 時間: 2025-3-29 23:26 作者: 裂口 時間: 2025-3-30 03:58
0302-9743 D consortium papers are included. The conference presents papers on subjects such as model, graph, learning, performance, knowledge, time, recommendation, representation, attention, prediction, and network..978-3-031-30636-5978-3-031-30637-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: –LOUS 時間: 2025-3-30 06:43 作者: 放肆的我 時間: 2025-3-30 09:19 作者: Indolent 時間: 2025-3-30 12:59 作者: 廢墟 時間: 2025-3-30 19:34 作者: amnesia 時間: 2025-3-30 23:33 作者: GUILE 時間: 2025-3-31 03:22
A Scalable Query Pricing Framework for?Incomplete Graph Datang functions for incomplete graph query respectively. Furthermore, we design feasible pricing algorithms based on subgraph matching to derive each type of query price. Extensive experiments on real graph datasets demonstrate the effectiveness and efficiency of our solutions.作者: absolve 時間: 2025-3-31 05:31 作者: ANTI 時間: 2025-3-31 10:49
https://doi.org/10.1007/978-3-8349-8156-1uce two novel pruning techniques including degree-one reduction and unreachable query filter. Extensive experiments demonstrate that our proposed techniques significantly boost state-of-the-art methods.作者: prediabetes 時間: 2025-3-31 16:20 作者: Hiatal-Hernia 時間: 2025-3-31 21:12 作者: Counteract 時間: 2025-3-31 22:20 作者: Hot-Flash 時間: 2025-4-1 03:08
Fine-Grained Tuple Transfer for?Pipelined Query Execution on?CPU-GPU Coprocessorposed to execute queries with both CPU and GPU in a pipelined approach. In the pipelined query execution, the cross-processor tuple transfer plays a crucial role for the overall query execution performance. The state-of-the-art solution achieves cross-processor tuple transfer using a queue-like data作者: geometrician 時間: 2025-4-1 09:08
Efficient Index-Based Regular Expression Matching with?Optimal Query Plan Treeell commands. Classical methods to support regex matching usually adopt the finite automaton which has a high matching cost. Recent methods solve the regex matching problem by utilizing the positional .-gram inverted index – one of the most widely used index schemes, and all matching results can be 作者: collateral 時間: 2025-4-1 10:41 作者: 關節(jié)炎 時間: 2025-4-1 15:36
Learned Bloom Filter for?Multi-key Membership Testingn for computing systems and networking applications such as web search, mail systems, distributed databases, firewalls, and network routing. Most existing studies for membership testing are built on Bloom filter, a space-efficient and high-security probabilistic data structure. However, traditional
