Pub Date : 2024-12-09DOI: 10.1109/TKDE.2024.3514323
Jian Chen;Hong Gao;Yuhong Shi;Junle Chen;Donghua Yang;Jianzhong Li
Maximizing Influence (Max-Inf) query is a fundamental operation in spatial data management. This query returns an optimal site from a candidate set to maximize its influence. Existing work commonly focuses on outdoor spaces. In practice, however, people spend up to 87% of their daily life inside indoor spaces. The outdoor techniques fall short in indoor spaces due to the complicated topology of indoor spaces. In this paper, we formulate two indoor Max-Inf queries: Top-$k$k Probabilistic Influence Query (T$k$kPI) and Collective-$k$k Probabilistic Influence Query (C$k$kPI) taking probability and mobility factors into consideration. We propose a novel spatial index, IT-tree, which utilizes the properties of indoor venues to facilitate the indoor distance computation, and then applies a trie to further organize the trajectories with similar check-in partitions together, based on their sketch information. This structure is simple but highly effective in pruning the trajectory search space. To process T$k$PI efficiently, we devise subtree pruning and progressive pruning techniques to delicately filter out unnecessary trajectories based on probability bounds and the monotonicity of influence probability. For C$k$PI queries, which is a submodular NP-hard problem, three approximation algorithms are provided with different strategies of computing marginal influence value during the search. Through extensive experiments on several real indoor venues, we demonstrate the efficiency and effectiveness of our proposed algorithms.
{"title":"Maximizing Influence Query Over Indoor Trajectories","authors":"Jian Chen;Hong Gao;Yuhong Shi;Junle Chen;Donghua Yang;Jianzhong Li","doi":"10.1109/TKDE.2024.3514323","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3514323","url":null,"abstract":"Maximizing Influence (Max-Inf) query is a fundamental operation in spatial data management. This query returns an optimal site from a candidate set to maximize its <i>influence</i>. Existing work commonly focuses on outdoor spaces. In practice, however, people spend up to 87% of their daily life inside indoor spaces. The outdoor techniques fall short in indoor spaces due to the complicated topology of indoor spaces. In this paper, we formulate two indoor Max-Inf queries: <i>Top-<inline-formula><tex-math>$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic></alternatives></inline-formula> Probabilistic Influence Query (T<inline-formula><tex-math>$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic></alternatives></inline-formula>PI)</i> and <i>Collective-<inline-formula><tex-math>$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic></alternatives></inline-formula> Probabilistic Influence Query (C<inline-formula><tex-math>$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic></alternatives></inline-formula>PI)</i> taking probability and mobility factors into consideration. We propose a novel spatial index, IT-tree, which utilizes the properties of indoor venues to facilitate the indoor distance computation, and then applies a trie to further organize the trajectories with similar check-in partitions together, based on their sketch information. This structure is simple but highly effective in pruning the trajectory search space. To process T<inline-formula><tex-math>$k$</tex-math></inline-formula>PI efficiently, we devise subtree pruning and progressive pruning techniques to delicately filter out unnecessary trajectories based on probability bounds and the monotonicity of influence probability. For C<inline-formula><tex-math>$k$</tex-math></inline-formula>PI queries, which is a submodular NP-hard problem, three approximation algorithms are provided with different strategies of computing marginal influence value during the search. Through extensive experiments on several real indoor venues, we demonstrate the efficiency and effectiveness of our proposed algorithms.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1294-1310"},"PeriodicalIF":8.9,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-09DOI: 10.1109/TKDE.2024.3513533
Haipeng Ding;Zhewei Wei;Yuhang Ye
Graph Neural Networks (GNNs) have aroused increasing research attention for their effectiveness on graph mining tasks. However, full-batch training methods based on stochastic gradient descent (SGD) require substantial resources since all gradient-required computational processes are stored in the acceleration device. The bottleneck of storage challenges the training of classic GNNs on large-scale datasets within one acceleration device. Meanwhile, message-passing based (spatial) GNN designs usually necessitate the homophily hypothesis of the graph, which easily fails on heterophilous graphs. In this paper, we propose the random walk extension for those message-passing based GNNs, enriching them with spectral powers. We prove that our random walk sampling with appropriate correction coefficients generates an unbiased approximation of the $K$ -order polynomial filter matrix, thus promoting the neighborhood aggregation of the central nodes. Node-wise sampling strategy and historical embedding allow the classic models to be trained with mini-batches, which extends the scalability of the basic models. To show the effectiveness of our method, we conduct a thorough experimental analysis on some frequently-used benchmarks with diverse homophily and scale. The empirical results show that our model achieves significant performance improvements in comparison with the corresponding base GNNs and some state-of-the-art baselines in node classification tasks.
{"title":"Scalable and Effective Graph Neural Networks via Trainable Random Walk Sampling","authors":"Haipeng Ding;Zhewei Wei;Yuhang Ye","doi":"10.1109/TKDE.2024.3513533","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3513533","url":null,"abstract":"Graph Neural Networks (GNNs) have aroused increasing research attention for their effectiveness on graph mining tasks. However, full-batch training methods based on stochastic gradient descent (SGD) require substantial resources since all gradient-required computational processes are stored in the acceleration device. The bottleneck of storage challenges the training of classic GNNs on large-scale datasets within one acceleration device. Meanwhile, message-passing based (spatial) GNN designs usually necessitate the homophily hypothesis of the graph, which easily fails on heterophilous graphs. In this paper, we propose the random walk extension for those message-passing based GNNs, enriching them with spectral powers. We prove that our random walk sampling with appropriate correction coefficients generates an unbiased approximation of the \u0000<inline-formula><tex-math>$K$</tex-math></inline-formula>\u0000-order polynomial filter matrix, thus promoting the neighborhood aggregation of the central nodes. Node-wise sampling strategy and historical embedding allow the classic models to be trained with mini-batches, which extends the scalability of the basic models. To show the effectiveness of our method, we conduct a thorough experimental analysis on some frequently-used benchmarks with diverse homophily and scale. The empirical results show that our model achieves significant performance improvements in comparison with the corresponding base GNNs and some state-of-the-art baselines in node classification tasks.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 2","pages":"896-909"},"PeriodicalIF":8.9,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intelligent logistics relies on accurately predicting the service time, which is a part of time cost in the last-mile delivery. However, service time prediction (STP) is non-trivial given complex delivery circumstances, location heterogeneity, and skewed observations in space, which are not well-handled by existing solutions. In our prior work, we treat STP at each location as a learning task to keep the location heterogeneity, propose a prior knowledge-enhanced meta-learning to tackle skewed observations, and introduce a Transformer-based representation module to encode complex delivery circumstances. Maintaining the design principles of prior work, in this extended paper, we propose MetaSTP + . In addition to fusing the prior knowledge after the meta-learning process, MetaSTP + also injects the prior knowledge before and during the meta-learning process to better tackle skewed observations. More specifically, MetaSTP + completes the support set of tasks with scarce samples from other tasks based on prior knowledge and is equipped with a prior knowledge-aware historical observation encoding module to achieve those purposes accordingly. Experiments show MetaSTP + outperforms the best baseline by 11.2% and 8.4% on two real-world datasets. Finally, an intelligent waybill assignment system based on MetaSTP + is deployed in JD Logistics.
{"title":"Spatial Meta Learning With Comprehensive Prior Knowledge Injection for Service Time Prediction","authors":"Shuliang Wang;Qianyu Yang;Sijie Ruan;Cheng Long;Ye Yuan;Qi Li;Ziqiang Yuan;Jie Bao;Yu Zheng","doi":"10.1109/TKDE.2024.3512582","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3512582","url":null,"abstract":"Intelligent logistics relies on accurately predicting the service time, which is a part of time cost in the last-mile delivery. However, service time prediction (STP) is non-trivial given complex delivery circumstances, location heterogeneity, and skewed observations in space, which are not well-handled by existing solutions. In our prior work, we treat STP at each location as a learning task to keep the location heterogeneity, propose a prior knowledge-enhanced meta-learning to tackle skewed observations, and introduce a Transformer-based representation module to encode complex delivery circumstances. Maintaining the design principles of prior work, in this extended paper, we propose MetaSTP\u0000<sup>+</sup>\u0000. In addition to fusing the prior knowledge after the meta-learning process, MetaSTP\u0000<sup>+</sup>\u0000 also injects the prior knowledge before and during the meta-learning process to better tackle skewed observations. More specifically, MetaSTP\u0000<sup>+</sup>\u0000 completes the support set of tasks with scarce samples from other tasks based on prior knowledge and is equipped with a prior knowledge-aware historical observation encoding module to achieve those purposes accordingly. Experiments show MetaSTP\u0000<sup>+</sup>\u0000 outperforms the best baseline by 11.2% and 8.4% on two real-world datasets. Finally, an intelligent waybill assignment system based on MetaSTP\u0000<sup>+</sup>\u0000 is deployed in JD Logistics.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 2","pages":"936-950"},"PeriodicalIF":8.9,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1109/TKDE.2024.3511714
Canyu Zhang;Feiping Nie;Rong Wang
Unsupervised domain adaptation aims to classify unlabeled data points in the target domain using labeled data points from the source domain, while the distributions of data points in two domains are different. To address this issue, we propose a novel method called the anchor guided unsupervised domain adaptation method (AGDA). We minimize distribution divergence in a latent feature subspace using the Maximum Mean Discrepancy (MMD) criterion. Unlike existing unsupervised domain adaptation methods, we introduce anchor points in the original space and impose domains data to the same anchor points rather than center points to further reduce the domain difference. We optimize the anchor-based graph in the subspace to obtain discriminative transformation matrices. This enables our model to perform better on non-Gaussian distribution than methods focusing on global structure. Furthermore, the sparse anchor-based graph reduces time complexity compared to the fully connected graph, enabling exploration of local structure. Experimental results demonstrate that our algorithm outperforms several state-of-the-art methods on various benchmark datasets.
{"title":"Anchor Guided Unsupervised Domain Adaptation","authors":"Canyu Zhang;Feiping Nie;Rong Wang","doi":"10.1109/TKDE.2024.3511714","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3511714","url":null,"abstract":"Unsupervised domain adaptation aims to classify unlabeled data points in the target domain using labeled data points from the source domain, while the distributions of data points in two domains are different. To address this issue, we propose a novel method called the anchor guided unsupervised domain adaptation method (AGDA). We minimize distribution divergence in a latent feature subspace using the Maximum Mean Discrepancy (MMD) criterion. Unlike existing unsupervised domain adaptation methods, we introduce anchor points in the original space and impose domains data to the same anchor points rather than center points to further reduce the domain difference. We optimize the anchor-based graph in the subspace to obtain discriminative transformation matrices. This enables our model to perform better on non-Gaussian distribution than methods focusing on global structure. Furthermore, the sparse anchor-based graph reduces time complexity compared to the fully connected graph, enabling exploration of local structure. Experimental results demonstrate that our algorithm outperforms several state-of-the-art methods on various benchmark datasets.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1079-1090"},"PeriodicalIF":8.9,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summarization quality evaluation is a non-trivial task in text summarization. Contemporary methods can be mainly categorized into two scenarios: (1) reference-based: evaluating with human-labeled reference summary; (2) reference-free: evaluating the summary consistency of the document. Recent studies mainly focus on one of these scenarios and explore training neural models to align with human criteria and finally give a numeric score. However, the models from different scenarios are optimized individually, which may result in sub-optimal performance since they neglect the shared knowledge across different scenarios. Besides, designing individual models for each scenario caused inconvenience to the user. Moreover, only providing the numeric quality evaluation score for users cannot help users to improve the summarization model, since they do not know why the score is low. Inspired by this, we propose U nified M ulti-scenario S ummarization E valuator (UMSE) and M ulti- A gent S ummarization E valuation E xplainer (MASEE). More specifically, we propose a perturbed prefix tuning method to share cross-scenario knowledge between scenarios and use a self-supervised training paradigm to optimize the model without extra human labeling. Our UMSE is the first unified summarization evaluation framework engaged with the ability to be used in three evaluation scenarios. We propose a multi-agent summary evaluation explanation method MASEE, which employs several LLM-based agents to generate detailed natural language explanations in four different aspects. Experimental results across three typical scenarios on the benchmark dataset SummEval indicate that our UMSE can achieve comparable performance with several existing strong methods that are specifically designed for each scenario. And intensive quantitative and qualitative experiments also demonstrate the effectiveness of our proposed explanation method, which can generate consistent and accurate explanations.
{"title":"Unified Multi-Scenario Summarization Evaluation and Explanation","authors":"Shuo Shang;Zhitao Yao;Hao Fu;Chongyang Tao;Xiuying Chen;Feng Wang;Yongbo Wang;Zhaochun Ren;Shen Gao","doi":"10.1109/TKDE.2024.3509715","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3509715","url":null,"abstract":"Summarization quality evaluation is a non-trivial task in text summarization. Contemporary methods can be mainly categorized into two scenarios: (1) \u0000<italic>reference-based:</i>\u0000 evaluating with human-labeled reference summary; (2) \u0000<italic>reference-free:</i>\u0000 evaluating the summary consistency of the document. Recent studies mainly focus on one of these scenarios and explore training neural models to align with human criteria and finally give a numeric score. However, the models from different scenarios are optimized individually, which may result in sub-optimal performance since they neglect the shared knowledge across different scenarios. Besides, designing individual models for each scenario caused inconvenience to the user. Moreover, only providing the numeric quality evaluation score for users cannot help users to improve the summarization model, since they do not know why the score is low. Inspired by this, we propose \u0000<bold>U</b>\u0000nified \u0000<bold>M</b>\u0000ulti-scenario \u0000<bold>S</b>\u0000ummarization \u0000<bold>E</b>\u0000valuator (UMSE) and \u0000<bold>M</b>\u0000ulti-\u0000<bold>A</b>\u0000gent \u0000<bold>S</b>\u0000ummarization \u0000<bold>E</b>\u0000valuation \u0000<bold>E</b>\u0000xplainer (MASEE). More specifically, we propose a perturbed prefix tuning method to share cross-scenario knowledge between scenarios and use a self-supervised training paradigm to optimize the model without extra human labeling. Our UMSE is the first unified summarization evaluation framework engaged with the ability to be used in three evaluation scenarios. We propose a multi-agent summary evaluation explanation method MASEE, which employs several LLM-based agents to generate detailed natural language explanations in four different aspects. Experimental results across three typical scenarios on the benchmark dataset SummEval indicate that our UMSE can achieve comparable performance with several existing strong methods that are specifically designed for each scenario. And intensive quantitative and qualitative experiments also demonstrate the effectiveness of our proposed explanation method, which can generate consistent and accurate explanations.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 2","pages":"991-1003"},"PeriodicalIF":8.9,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-04DOI: 10.1109/TKDE.2024.3511136
Jiawei Jiang;Hao Huang;Zhigao Zheng;Yi Wei;Fangcheng Fu;Xiaosen Li;Bin Cui
Motif detection is a graph algorithm that detects certain local structures in a graph. Although network motif has been studied in graph analytics, e.g., social network and biological network, it is yet unclear whether network motif is useful for analyzing online transaction network that is generated in applications such as instant messaging and e-commerce. In an online transaction network, each vertex represents a user’s account and each edge represents a money transaction between two users. In this work, we try to analyze online transaction networks with network motifs. We design motif-based vertex embedding that integrates motif counts and centrality measurements. Furthermore, we design a distributed framework to detect motifs in large-scale online transaction networks. Our framework obtains the edge directions using a bi-directional tagging method and avoids redundant detection with a reduced view of neighboring vertices. We implement the proposed framework under the parameter server architecture. In the evaluation, we analyze different kinds of online transaction networks w.r.t the distribution of motifs and evaluate the effectiveness of motif-based embedding in downstream graph analytical tasks. The experimental results also show that our proposed motif detection framework can efficiently handle large-scale graphs.
{"title":"Detecting and Analyzing Motifs in Large-Scale Online Transaction Networks","authors":"Jiawei Jiang;Hao Huang;Zhigao Zheng;Yi Wei;Fangcheng Fu;Xiaosen Li;Bin Cui","doi":"10.1109/TKDE.2024.3511136","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3511136","url":null,"abstract":"Motif detection is a graph algorithm that detects certain local structures in a graph. Although network motif has been studied in graph analytics, e.g., social network and biological network, it is yet unclear whether network motif is useful for analyzing \u0000<italic>online transaction network</i>\u0000 that is generated in applications such as instant messaging and e-commerce. In an online transaction network, each vertex represents a user’s account and each edge represents a money transaction between two users. In this work, we try to analyze online transaction networks with network motifs. We design motif-based vertex embedding that integrates motif counts and centrality measurements. Furthermore, we design a distributed framework to detect motifs in large-scale online transaction networks. Our framework obtains the edge directions using a bi-directional tagging method and avoids redundant detection with a reduced view of neighboring vertices. We implement the proposed framework under the parameter server architecture. In the evaluation, we analyze different kinds of online transaction networks w.r.t the distribution of motifs and evaluate the effectiveness of motif-based embedding in downstream graph analytical tasks. The experimental results also show that our proposed motif detection framework can efficiently handle large-scale graphs.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 2","pages":"584-596"},"PeriodicalIF":8.9,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robustness is paramount for ensuring the reliability of knowledge graph models in safety-sensitive applications. While recent research has delved into adversarial attacks on static knowledge graph models, the exploration of more practical temporal knowledge graphs has been largely overlooked. To fill this gap, we present the Adaptive Temporal Perturbation Framework (ATPF), a novel adversarial attack framework aimed at probing the robustness of temporal knowledge graph (TKG) models. The general idea of ATPF is to inject perturbations into the victim model input to undermine the prediction. First, we propose the Temporal Perturbation Prioritization (TPP) algorithm, which identifies the optimal time sequence for perturbation injection before initiating attacks. Subsequently, we design the Rank-Based Edge Manipulation (RBEM) algorithm, enabling the generation of both edge addition and removal perturbations under black-box setting. With ATPF, we present two adversarial attack methods: the stringent ATPF-hard and the more lenient ATPF-soft, each imposing different perturbation constraints. Our experimental evaluations on the link prediction task for TKGs demonstrate the superior attack performance of our methods compared to baseline methods. Furthermore, we find that strategically placing a single perturbation often suffices to successfully compromise a target link.
{"title":"ATPF: An Adaptive Temporal Perturbation Framework for Adversarial Attacks on Temporal Knowledge Graph","authors":"Longquan Liao;Linjiang Zheng;Jiaxing Shang;Xu Li;Fengwen Chen","doi":"10.1109/TKDE.2024.3510689","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3510689","url":null,"abstract":"Robustness is paramount for ensuring the reliability of knowledge graph models in safety-sensitive applications. While recent research has delved into adversarial attacks on static knowledge graph models, the exploration of more practical temporal knowledge graphs has been largely overlooked. To fill this gap, we present the Adaptive Temporal Perturbation Framework (ATPF), a novel adversarial attack framework aimed at probing the robustness of temporal knowledge graph (TKG) models. The general idea of ATPF is to inject perturbations into the victim model input to undermine the prediction. First, we propose the Temporal Perturbation Prioritization (TPP) algorithm, which identifies the optimal time sequence for perturbation injection before initiating attacks. Subsequently, we design the Rank-Based Edge Manipulation (RBEM) algorithm, enabling the generation of both edge addition and removal perturbations under black-box setting. With ATPF, we present two adversarial attack methods: the stringent ATPF-hard and the more lenient ATPF-soft, each imposing different perturbation constraints. Our experimental evaluations on the link prediction task for TKGs demonstrate the superior attack performance of our methods compared to baseline methods. Furthermore, we find that strategically placing a single perturbation often suffices to successfully compromise a target link.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1091-1104"},"PeriodicalIF":8.9,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-04DOI: 10.1109/TKDE.2024.3511334
Jianjun Li;Yuhui Deng;Jiande Huang;Yi Zhou;Qifen Yang;Geyong Min
Sliding window aggregation, which extracts summaries from data streams, is a core operation in streaming analysis. Though existing sliding window algorithms that perform single eviction and insertion operations can achieve a worst-case time complexity of $O(1)$ for in-order streams, real-world data streams often involve out-of-order data and exhibit burst data characteristics, which pose performance challenges to these sliding window algorithms. To address this challenging issue, we propose Gecko - a novel sliding window aggregation algorithm that supports bulk eviction. Gecko leverages a granular-based eviction strategy for various bulk sizes, enabling efficient bulk eviction while maintaining the performance close to that of in-order stream algorithms for single evictions. For large data bulks, Gecko performs coarse-grained eviction at the chunk level, followed by fine-grained eviction using leftward binary tree aggregation (LTA) as a complementary method. Moreover, Gecko partitions data based on chunks to prevent the impacts of out-of-order data on other chunks, thereby enabling efficient handling of out-of-order data streams. We conduct extensive experiments to evaluate the performance of Gecko. Experimental results demonstrate that Gecko exhibits superior performance over other solutions, which is consistent with theoretical expectations. In real-world data scenarios, Gecko improves the average throughput of the state-of-the-art algorithm b_FiBA by 1.7 times, with a maximum improvement of up to 3.5 times. Gecko also demonstrates the best latency performance among all compared schemes.
{"title":"Gecko: Efficient Sliding Window Aggregation With Granular-Based Bulk Eviction Over Big Data Streams","authors":"Jianjun Li;Yuhui Deng;Jiande Huang;Yi Zhou;Qifen Yang;Geyong Min","doi":"10.1109/TKDE.2024.3511334","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3511334","url":null,"abstract":"Sliding window aggregation, which extracts summaries from data streams, is a core operation in streaming analysis. Though existing sliding window algorithms that perform single eviction and insertion operations can achieve a worst-case time complexity of \u0000<inline-formula><tex-math>$O(1)$</tex-math></inline-formula>\u0000 for in-order streams, real-world data streams often involve out-of-order data and exhibit burst data characteristics, which pose performance challenges to these sliding window algorithms. To address this challenging issue, we propose \u0000<i>Gecko</i>\u0000 - a novel sliding window aggregation algorithm that supports bulk eviction. Gecko leverages a granular-based eviction strategy for various bulk sizes, enabling efficient bulk eviction while maintaining the performance close to that of in-order stream algorithms for single evictions. For large data bulks, Gecko performs coarse-grained eviction at the chunk level, followed by fine-grained eviction using leftward binary tree aggregation (LTA) as a complementary method. Moreover, Gecko partitions data based on chunks to prevent the impacts of out-of-order data on other chunks, thereby enabling efficient handling of out-of-order data streams. We conduct extensive experiments to evaluate the performance of Gecko. Experimental results demonstrate that Gecko exhibits superior performance over other solutions, which is consistent with theoretical expectations. In real-world data scenarios, Gecko improves the average throughput of the state-of-the-art algorithm b_FiBA by 1.7 times, with a maximum improvement of up to 3.5 times. Gecko also demonstrates the best latency performance among all compared schemes.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 2","pages":"698-709"},"PeriodicalIF":8.9,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sequential causal effect estimation has recently attracted increasing attention from research and industry. While the existing models have achieved many successes, there are still many limitations. Existing models usually assume the causal graphs to be sufficient, i.e., there are no latent factors, such as the unmeasured confounders and instrumental variables. However, in real-world scenarios, it is hard to record all of the factors in the observational data, which makes the causally sufficient assumptions not hold. Moreover, existing models mainly focus on discrete treatments rather than continuous ones. To alleviate the above problems, in this paper, we propose a novel C ontinous C ausal M odel by explicitly capturing the L atent F actors (called C$^{2}$2M-LF for short). Specifically, we define a sequential causal graph by simultaneously considering the unmeasured confounders and instrumental variables. Second, we describe the independence that should be satisfied among different variables from the mutual information perspective and further propose our learning objective. Then, we reweight different samples in the continuous treatment space to optimize our model unbiasedly. Beyond the above designs, we also theoretically analyze our model’s causal identifiability and unbiasedness. Finally, we conduct extensive experiments on both simulation and real-world datasets to demonstrate the effectiveness of our proposed model.
{"title":"Sequential Causal Effect Estimation by Jointly Modeling the Unmeasured Confounders and Instrumental Variables","authors":"Zexu Sun;Bowei He;Shiqi Shen;Zhipeng Wang;Zhi Gong;Chen Ma;Qi Qi;Xu Chen","doi":"10.1109/TKDE.2024.3510734","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3510734","url":null,"abstract":"Sequential causal effect estimation has recently attracted increasing attention from research and industry. While the existing models have achieved many successes, there are still many limitations. Existing models usually assume the causal graphs to be sufficient, i.e., there are no latent factors, such as the unmeasured confounders and instrumental variables. However, in real-world scenarios, it is hard to record all of the factors in the observational data, which makes the causally sufficient assumptions not hold. Moreover, existing models mainly focus on discrete treatments rather than continuous ones. To alleviate the above problems, in this paper, we propose a novel \u0000<bold>C</b>\u0000ontinous \u0000<bold>C</b>\u0000ausal \u0000<bold>M</b>\u0000odel by explicitly capturing the \u0000<bold>L</b>\u0000atent \u0000<bold>F</b>\u0000actors (called \u0000<bold>C<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula>M-LF</b>\u0000 for short). Specifically, we define a sequential causal graph by simultaneously considering the unmeasured confounders and instrumental variables. Second, we describe the independence that should be satisfied among different variables from the mutual information perspective and further propose our learning objective. Then, we reweight different samples in the continuous treatment space to optimize our model unbiasedly. Beyond the above designs, we also theoretically analyze our model’s causal identifiability and unbiasedness. Finally, we conduct extensive experiments on both simulation and real-world datasets to demonstrate the effectiveness of our proposed model.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 2","pages":"910-922"},"PeriodicalIF":8.9,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Collective Location Selection (CLS) has received significant research attention in the spatial database community due to its wide range of applications. The CLS problem selects a group of k preferred locations among candidate sites to establish facilities, aimed at collectively attracting the maximum number of users. Existing studies commonly assume every user is located in a fixed position, without considering the competition between peer facilities. Unfortunately, in real markets, users are mobile and choose to patronize from a host of competitors, making traditional techniques unavailable. To this end, this paper presents the first effort on a CLS problem in competition scenarios, called mc$^{2}$2ls , taking into account the mobility factor. Solving mc$^{2}$2ls is a non-trivial task due to its NP-hardness. To overcome the challenge of pruning multi-point users with highly overlapped minimum boundary rectangles (MBRs), we exploit a position count threshold and design two square-based pruning rules. We introduce IQuad-tree, a user-MBR-free index, to benefit the hierarchical and batch-wise properties of the pruning rules. We propose an $(1-frac{1}{e})$ -approximate greedy solution to mc$^{2}$2ls and incorporate a candidate-pruning strategy to further accelerate the computation for handling skewed datasets. Extensive experiments are conducted on real datasets, demonstrating the superiority of our proposed pruning rules and solution compared to the state-of-the-art techniques.
{"title":"MC$^{2}$2LS: Towards Efficient Collective Location Selection in Competition","authors":"Meng Wang;Mengfei Zhao;Hui Li;Jiangtao Cui;Bo Yang;Tao Xue","doi":"10.1109/TKDE.2024.3510100","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3510100","url":null,"abstract":"Collective Location Selection (CLS) has received significant research attention in the spatial database community due to its wide range of applications. The CLS problem selects a group of \u0000<i>k</i>\u0000 preferred locations among candidate sites to establish facilities, aimed at collectively attracting the maximum number of users. Existing studies commonly assume every user is located in a fixed position, without considering the competition between peer facilities. Unfortunately, in real markets, users are mobile and choose to patronize from a host of competitors, making traditional techniques unavailable. To this end, this paper presents the first effort on a CLS problem in competition scenarios, called \u0000<small>mc<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula>ls</small>\u0000, taking into account the mobility factor. Solving \u0000<small>mc<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula>ls</small>\u0000 is a non-trivial task due to its NP-hardness. To overcome the challenge of pruning multi-point users with highly overlapped minimum boundary rectangles (MBRs), we exploit a position count threshold and design two square-based pruning rules. We introduce IQuad-tree, a user-MBR-free index, to benefit the hierarchical and batch-wise properties of the pruning rules. We propose an \u0000<inline-formula><tex-math>$(1-frac{1}{e})$</tex-math></inline-formula>\u0000-approximate greedy solution to \u0000<small>mc<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula>ls</small>\u0000 and incorporate a candidate-pruning strategy to further accelerate the computation for handling skewed datasets. Extensive experiments are conducted on real datasets, demonstrating the superiority of our proposed pruning rules and solution compared to the state-of-the-art techniques.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 2","pages":"766-780"},"PeriodicalIF":8.9,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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