IEEE Transactions on Knowledge and Data Engineering最新文献

{"title":"Survey and Benchmark of Anomaly Detection in Business Processes","authors":"Wei Guan;Jian Cao;Haiyan Zhao;Yang Gu;Shiyou Qian","doi":"10.1109/TKDE.2024.3484159","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3484159","url":null,"abstract":"Effective management of business processes is crucial for organizational success. However, despite meticulous design and implementation, anomalies are inevitable and can result in inefficiencies, delays, or even significant financial losses. Numerous methods for detecting anomalies in business processes have been proposed recently. However, there is no comprehensive benchmark to evaluate these methods. Consequently, the relative merits of each method remain unclear due to differences in their experimental setup, choice of datasets and evaluation measures. In this paper, we present a systematic literature review and taxonomy of business process anomaly detection methods. Additionally, we select at least one method from each category, resulting in 16 methods that are cross-benchmarked against 32 synthetic logs and 19 real-life logs from different industry domains. Our analysis provides insights into the strengths and weaknesses of different anomaly detection methods. Ultimately, our findings can help researchers and practitioners in the field of process mining make informed decisions when selecting and applying anomaly detection methods to real-life business scenarios. Finally, some future directions are discussed in order to promote the evolution of business process anomaly detection.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"493-512"},"PeriodicalIF":8.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810281","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}

{"title":"ENCODE: Breaking the Trade-Off Between Performance and Efficiency in Long-Term User Behavior Modeling","authors":"Wen-Ji Zhou;Yuhang Zheng;Yinfu Feng;Yunan Ye;Rong Xiao;Long Chen;Xiaosong Yang;Jun Xiao","doi":"10.1109/TKDE.2024.3486445","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3486445","url":null,"abstract":"Long-term user behavior sequences are a goldmine for businesses to explore users’ interests to improve Click-Through Rate (CTR). However, it is very challenging to accurately capture users’ long-term interests from their long-term behavior sequences and give quick responses from the online serving systems. To meet such requirements, existing methods “inadvertently” destroy two basic requirements in long-term sequence modeling: \u0000<bold>R1</b>\u0000) make full use of the entire sequence to keep the information as much as possible; \u0000<bold>R2</b>\u0000) extract information from the most relevant behaviors to keep high relevance between learned interests and current target items. The performance of online serving systems is significantly affected by incomplete and inaccurate user interest information obtained by existing methods. To this end, we propose an efficient two-stage long-term sequence modeling approach, named as \u0000<bold>E</b>\u0000fficie\u0000<bold>N</b>\u0000t \u0000<bold>C</b>\u0000lustering based tw\u0000<bold>O</b>\u0000-stage interest mo\u0000<bold>DE</b>\u0000ling (ENCODE), consisting of offline extraction stage and online inference stage. It not only meets the aforementioned two basic requirements but also achieves a desirable balance between online service efficiency and precision. Specifically, in the offline extraction stage, ENCODE clusters the entire behavior sequence and extracts accurate interests. To reduce the overhead of the clustering process, we design a metric learning-based dimension reduction algorithm that preserves the relative pairwise distances of behaviors in the new feature space. While in the online inference stage, ENCODE takes the off-the-shelf user interests to predict the associations with target items. Besides, to further ensure the relevance between user interests and target items, we adopt the same relevance metric throughout the whole pipeline of ENCODE. The extensive experiment and comparison with SOTA on both industrial and public datasets have demonstrated the effectiveness and efficiency of our proposed ENCODE.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"265-277"},"PeriodicalIF":8.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797935","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}

{"title":"Answering Min-Max Resource-Constrained Shortest Path Queries Over Large Graphs","authors":"Haoran Qian;Weiguo Zheng;Zhijie Zhang;Bo Fu","doi":"10.1109/TKDE.2024.3488095","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3488095","url":null,"abstract":"The constrained shortest path problem is a fundamental and challenging task in applications built on graphs. In this paper, we formalize and study the \u0000<inline-formula><tex-math>$Min$</tex-math></inline-formula>\u0000-\u0000<inline-formula><tex-math>$Max$</tex-math></inline-formula>\u0000 resource-constrained shortest path (\u0000<inline-formula><tex-math>$Min$</tex-math></inline-formula>\u0000-\u0000<inline-formula><tex-math>$Max$</tex-math></inline-formula>\u0000 RCSP) problem, which generalizes the well-studied \u0000<inline-formula><tex-math>$Max$</tex-math></inline-formula>\u0000 RCSP problem. The objective is to find a simple path of minimum cost between two query nodes, subject to resource constraints between minimum and maximum limits. This problem has wide applications in fields such as delay networks and transportation. However, we theoretically prove that computing the optimal solution is NP-hard. We propose a two-stage approach that involves resource-based graph reduction followed by cost-guided path generation. To reduce the cost of expensive acyclicity checking, we introduce the technique of ancestor checking based on the shortest path tree. Furthermore, we present an even faster incremental search approach that considers both the path cost and resource constraints while avoiding acyclicity checking. Extensive experiments on twenty real graphs consistently demonstrate the superiority of our proposed methods, achieving up to two orders of magnitude improvement in time efficiency over the baseline algorithms while producing high-quality solutions.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"60-74"},"PeriodicalIF":8.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797961","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}

{"title":"DIMS: Distributed Index for Similarity Search in Metric Spaces","authors":"Yifan Zhu;Chengyang Luo;Tang Qian;Lu Chen;Yunjun Gao;Baihua Zheng","doi":"10.1109/TKDE.2024.3487759","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3487759","url":null,"abstract":"Similarity search finds objects that are similar to a given query object based on a similarity metric. As the amount and variety of data continue to grow, similarity search in metric spaces has gained significant attention. Metric spaces can accommodate any type of data and support flexible distance metrics, making similarity search in metric spaces beneficial for many real-world applications, such as multimedia retrieval, personalized recommendation, trajectory analytics, data mining, decision planning, and distributed servers. However, existing studies mostly focus on indexing metric spaces on a single machine, which faces efficiency and scalability limitations with increasing data volume and query amount. Recent advancements in similarity search turn towards distributed methods, while they face challenges including inefficient local data management, unbalanced workload, and low concurrent search efficiency. To this end, we propose \u0000<bold>DIMS</b>\u0000, an efficient \u0000<bold>D</b>\u0000istributed \u0000<bold>I</b>\u0000ndex for similarity search in \u0000<bold>M</b>\u0000etric \u0000<bold>S</b>\u0000paces. First, we design a novel three-stage heterogeneous partition to achieve workload balance. Then, we present an effective three-stage indexing structure to efficiently manage objects. We also develop concurrent search methods with filtering and validation techniques that support efficient distributed similarity search. Additionally, we devise a cost-based optimization model to balance communication and computation cost. Extensive experiments demonstrate that DIMS significantly outperforms existing distributed similarity search approaches.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"210-225"},"PeriodicalIF":8.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798048","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}

{"title":"Achieving Efficient and Privacy-Preserving Reverse Skyline Query Over Single Cloud","authors":"Yubo Peng;Xiong Li;Ke Gu;Jinjun Chen;Sajal K. Das;Xiaosong Zhang","doi":"10.1109/TKDE.2024.3487646","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3487646","url":null,"abstract":"Reverse skyline query (RSQ) has been widely used in practice since it can pick out the data of interest to the query vector. To save storage resources and facilitate service provision, data owners usually outsource data to the cloud for RSQ services, which poses huge challenges to data security and privacy protection. Existing privacy-preserving RSQ schemes are either based on a two-cloud model or cannot fully protect privacy. To this end, we propose an efficient privacy-preserving reverse skyline query scheme over a single cloud (ePRSQ). Specifically, we first design a privacy-preserving inner product's sign determination scheme (PIPSD), which can determine whether the inner product of two vectors satisfies a specific relation with 0 without leaking the vectors’ information. Next, we propose a privacy-preserving reverse dominance checking scheme (PRDC) based on symmetric homomorphic encryption. Finally, we achieve ePRSQ based on PIPSD and PRDC. Security analysis shows that PIPSD and PRDC are both secure in the real/ideal world model, and ePRSQ can protect the security of the dataset, the privacy of query requests and query results. Extensive experiments show that ePRSQ is efficient. Specifically, for a 3-dimensional dataset of size 1000, the computational and communication overheads of ePRSQ for a query are 79.47 s and 0.0021 MB, respectively. The efficiency is improved by \u0000<inline-formula><tex-math>$3.78times$</tex-math></inline-formula>\u0000 (300.58 s) and \u0000<inline-formula><tex-math>$928.57times$</tex-math></inline-formula>\u0000 (1.95 MB) respectively compared with PPARS, and by \u0000<inline-formula><tex-math>$61.31times$</tex-math></inline-formula>\u0000 (4872.55 s) and \u0000<inline-formula><tex-math>$407309times$</tex-math></inline-formula>\u0000 (855.35 MB) respectively compared with OPPRS.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"29-44"},"PeriodicalIF":8.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797948","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}

{"title":"Angular Reconstructive Discrete Embedding With Fusion Similarity for Multi-View Clustering","authors":"Jintang Bian;Xiaohua Xie;Chang-Dong Wang;Lingxiao Yang;Jian-Huang Lai;Feiping Nie","doi":"10.1109/TKDE.2024.3487907","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3487907","url":null,"abstract":"Effectively and efficiently mining valuable clustering patterns is a challenging problem when handling large-scale data from diverse sources. Existing approaches adopt anchor graph learning or binary representation embedding to reduce computational complexity. Normally, anchor graph learning can not directly obtain the clustering assignment except adopt the post-processing stage, such as graph cut or k-means clustering. The binary representation embedding neglects the structure information in Hamming space. In order to overcome these limitations, this paper proposes a novel, effective, and efficient angular reconstructive discrete embedding method with fusion similarity for a multi-view clustering (AFMC) that can jointly learn the global and local structure preserving binary representation and clustering assignment. Specifically, we propose to use angular reconstructive error minimization to maintain the global similarity correlation of binary representations of heterogeneous features in a common Hamming space. Moreover, we design a multi-view discrete ridge regression with fusion similarity term to handle the out-of-sample problem and preserve the local manifold structure. In addition, we propose an efficient optimization algorithm with linear computational complexity to solve the non-convex and non-smooth objective function. The experimental results demonstrate that AFMC outperforms several state-of-the-art large-scale multi-view clustering methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"45-59"},"PeriodicalIF":8.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797990","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}

{"title":"CauseRuDi: Explaining Behavior Sequence Models by Causal Statistics Generation and Rule Distillation","authors":"Yao Zhang;Yun Xiong;Yiheng Sun;Yucheng Jin;Caihua Shan;Tian Lu;Hui Song;Shengli Sun","doi":"10.1109/TKDE.2024.3487625","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3487625","url":null,"abstract":"Risk scoring systems have been widely deployed in many applications, which assign risk scores to users according to their behavior sequences. Though many deep learning methods with sophisticated designs have achieved promising results, the black-box nature hinders their applications due to fairness, explainability, and compliance consideration. Rule-based systems are considered reliable in these sensitive scenarios. However, building a rule system is labor-intensive. Experts need to find informative statistics from user behavior sequences, design rules based on statistics and assign weights to each rule. In this paper, we bridge the gap between effective but black-box models and transparent rule models. We propose a two-stage framework, CauseRuDi, that distills the knowledge of black-box teacher models into rule-based student models. We design a Monte Carlo tree search-based statistics generation method that maximizes the correlation or dependence between the generated statistics and the teacher model's outputs. We formulate a sequential move game and a simultaneous move coalitional game to generate multiple statistics. Then statistics are composed into logical rules with our proposed neural logical networks by mimicking the outputs of teacher models. We evaluate CauseRuDi on three real-world public datasets and an industrial dataset to demonstrate its effectiveness.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"116-129"},"PeriodicalIF":8.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797891","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}

{"title":"Hierarchical Deep Document Model","authors":"Yi Yang;John P. Lalor;Ahmed Abbasi;Daniel Dajun Zeng","doi":"10.1109/TKDE.2024.3487523","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3487523","url":null,"abstract":"Topic modeling is a commonly used text analysis tool for discovering latent topics in a text corpus. However, while topics in a text corpus often exhibit a hierarchical structure (e.g., cellphone is a sub-topic of electronics), most topic modeling methods assume a flat topic structure that ignores the hierarchical dependency among topics, or utilize a predefined topic hierarchy. In this work, we present a novel Hierarchical Deep Document Model (HDDM) to learn topic hierarchies using a variational autoencoder framework. We propose a novel objective function, sum of log likelihood, instead of the widely used evidence lower bound, to facilitate the learning of hierarchical latent topic structure. The proposed objective function can directly model and optimize the hierarchical topic-word distributions at all topic levels. We conduct experiments on four real-world text datasets to evaluate the topic modeling capability of the proposed HDDM method compared to state-of-the-art hierarchical topic modeling benchmarks. Experimental results show that HDDM achieves considerable improvement over benchmarks and is capable of learning meaningful topics and topic hierarchies. To further demonstrate the practical utility of HDDM, we apply it to a real-world medical notes dataset for clinical prediction. Experimental results show that HDDM can better summarize topics in medical notes, resulting in more accurate clinical predictions.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"351-364"},"PeriodicalIF":8.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810295","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}

{"title":"Handling Low Homophily in Recommender Systems With Partitioned Graph Transformer","authors":"Thanh Tam Nguyen;Thanh Toan Nguyen;Matthias Weidlich;Jun Jo;Quoc Viet Hung Nguyen;Hongzhi Yin;Alan Wee-Chung Liew","doi":"10.1109/TKDE.2024.3485880","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3485880","url":null,"abstract":"Modern recommender systems derive predictions from an interaction graph that links users and items. To this end, many of today's state-of-the-art systems use graph neural networks (GNNs) to learn effective representations of these graphs under the assumption of homophily, i.e., the idea that similar users will sit close to each other in the graph. However, recent studies have revealed that real-world recommendation graphs are often heterophilous, i.e., dissimilar users will also often sit close to each other. One of the reasons for this heterophilia is shilling attacks that obscure the inherent characteristics of the graph and make the derived recommendations less accurate as a consequence. Hence, to cope with low homophily in recommender systems, we propose a recommendation model called PGT4Rec that is based on a Partitioned Graph Transformer. The model integrates label information into the learning process, which allows discriminative neighbourhoods of users to be generated. As such, the framework can both detect shilling attacks and predict user ratings for items. Extensive experiments on real and synthetic datasets show PGT4Rec as not only providing superior performance in these two tasks but also significant robustness to a range of adversarial conditions.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"334-350"},"PeriodicalIF":8.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797954","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}

{"title":"Expressiveness Analysis and Enhancing Framework for Geometric Knowledge Graph Embedding Models","authors":"Tengwei Song;Long Yin;Yang Liu;Long Liao;Jie Luo;Zhiqiang Xu","doi":"10.1109/TKDE.2024.3486915","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3486915","url":null,"abstract":"Existing geometric knowledge graph embedding methods employ various relational transformations, such as translation, rotation, and projection, to model different relation patterns, which aims to enhance the expressiveness of models. In contrast to current approaches that treat the expressiveness of the model as a binary issue, we aim to delve deeper into analyzing the level of difficulty in which geometric knowledge graph embedding models can represent relation patterns. In this paper, we provide a theoretical analysis framework that measures the expressiveness of the model in relation patterns by quantifying the size of the solution space of linear equation systems. Additionally, we propose a mechanism for imposing relational constraints on geometric knowledge graph embedding models by setting “traps” near relational optimal solutions, which enables the model to better converge to the optimal solution. Empirically, we analyze and compare several typical knowledge graph embedding models with different geometric algebras, revealing that some models have insufficient solution space due to their design, which leads to performance weaknesses. We also demonstrate that the proposed relational constraint operations can improve the performance of certain relation patterns. The experimental results on public benchmarks and relation pattern specified dataset are consistent with our theoretical analysis.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"306-318"},"PeriodicalIF":8.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797991","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}