IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERINGIEEE知识与数据工程汇刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING(英文缩写 IEEE T KNOWL DATA EN),ISSN 1041-4347,eISSN 1558-2191,中文译名:IEEE知识与数据工程汇刊 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 9.235 | Q1 |
| 2022 | 8.900 | Q1 |
| 2023 | 8.900 | Q1 |
| 2024 | 10.400 | Q1 |
| 2025 | 11.600 | Q1 |
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 最新收录文献
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1. STORM: Exploiting Spatiotemporal Continuity for Trajectory Similarity Learning in Road Networks.
1. STORM：利用时空连续性进行道路网络中的轨迹相似性学习PMID:日期:2026-01-01[中文摘要] 道路网络中的轨迹相似性对于交通、城市规划和网约车等众多应用至关重要。然而，由于轨迹长度各异，直接在原始轨迹数据上采用相似性度量（如DTW[1]）在大规模场景下变得不切实际。因此，当前研究主要围绕应用深度学习将轨迹嵌入为向量表示（即嵌入），从而使更简单（且可索引）的相似性度量（如欧几里得距离）得以应用。现有研究要么独立于下游任务对轨迹进行嵌入，要么针对特定相似性度量专门定制嵌入。前者具有通用性且便于微调以适应各种度量，而后者通常能产生更有效的结果，但需要针对不同但相似的度量重新配置。此外，两种方法均忽略了轨迹数据内在的时空连续性，导致轨迹建模效果欠佳。我们的目标是解决建模中的局限性，兼取两类方法之长。首先，我们通过预训练生成嵌入，与任何特定相似性度量解耦。随后，通过一个精细但较简单的微调过程，我们增强嵌入以 encapsulate 特定相似性度量的细微特征。此外，我们方法的一个重要方面在于捕捉时空连续性的轨迹建模，其主要由面向轨迹的路段嵌入和Transformer编码器组成，后者通过路网约束轨迹中固有的时空语义得到增强。实验结果表明，在逼近多种轨迹相似性度量方面，我们的方法优于两类方法中现有的最先进模型。
[英文摘要] Trajectory similarity in road networks is pivotal for numerous applications in transportation, urban planning, and ridesharing. However, due to the varying lengths of trajectories, employing similarity metrics directly on raw trajectory data (e.g., DTW [1]) becomes impractical at scale. Therefore, current research primarily revolves around applying deep learning to embed trajectories into vector representations, i.e., embeddings, enabling the application of simpler (and indexable) similarity metrics such as Euclidean distance. Existing research either involves embedding trajectories independent of the downstream tasks, or tailors the embedding specifically for a designated similarity metric. While the former offers versatility and allows for easy fine-tuning to accommodate various metrics, the latter typically yields more effective results but necessitates reconfiguration for different, yet similar metrics. Moreover, both approaches neglect the intrinsic spatiotemporal continuity in trajectory data, resulting in suboptimal trajectory modeling. Our objective is to address the limitations in modeling and have the best of the two worlds. Initially, we generate an embedding through pre-training, decoupled from any particular similarity metric. Subsequently, through a meticulous yet less complex fine-tuning process, we enhance the embedding to encapsulate the nuances of a designated similarity metric. Moreover, a significant aspect of our approach lies in our trajectory modeling that captures spatiotemporal continuity, which mainly consists of a trajectory-oriented road segment embedding and a Transformer encoder enhanced by spatiotemporal semantics inherent in road network-constrained trajectories. Our experimental results demonstrate the superiority of our approach in approximating multiple trajectory similarity metrics over existing state-of-the-art models from both categories of approaches.
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2. Data Synthesis Reinvented: Preserving Missing Patterns for Enhanced Analysis.
PMID:日期:2025-04-22Synthetic data is being widely used as a replacement or enhancement for real data in fields as diverse as healthcare, telecommunications, and finance. Unlike real data, which represents actual people and objects, synthetic data is generated from an estimated distribution that retains key statistical properties of the real data. This makes synthetic data attractive for sharing while addressing privacy, confidentiality, and autonomy concerns. Real data often contains missing values that hold important information about individual, system, or organizational behavior. Standard synthetic data generation methods eliminate missing values as part of their pre-processing steps and thus completely ignore this valuable source of information. Instead, we propose methods to generate synthetic data that preserve both the observable and missing data distributions; consequently, retaining the valuable information encoded in the missing patterns of the real data. Our approach handles various missing data scenarios and can easily integrate with existing data generation methods. Extensive empirical evaluations on diverse datasets demonstrate the effectiveness of our approach as well as the value of preserving missing data distribution in synthetic data.
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3. Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity.
3. Cafe：利用缺失数据异质性改进联邦数据插补PMID:日期:2025-01-30Federated learning (FL), a decentralized machine learning approach, offers great performance while alleviating autonomy and confidentiality concerns. Despite FL's popularity, how to deal with missing values in a federated manner is not well understood. In this work, we initiate a study of federated imputation of missing values, particularly in complex scenarios, where missing data heterogeneity exists and the state-of-the-art (SOTA) approaches for federated imputation suffer from significant loss in imputation quality. We propose Cafe, a personalized FL approach for missing data imputation. Cafe is inspired from the observation that heterogeneity can induce differences in observable and missing data distribution across clients, and that these differences can be leveraged to improve the imputation quality. Cafe computes personalized weights that are automatically calibrated for the level of heterogeneity, which can remain unknown, to develop personalized imputation models for each client. An extensive empirical evaluation over a variety of settings demonstrates that Cafe matches the performance of SOTA baselines in homogeneous settings while significantly outperforming the baselines in heterogeneous settings.
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4. Hierarchical Active Learning with Label Proportions on Data Regions.
PMID:日期:2024-12Learning classification models from real-world data often requires substantial human effort devoted to instance annotation. As the instance-based annotating process can be very time-consuming and costly, we propose a novel active learning framework that builds classification models from human-annotated . A region is defined by a set of conjunctive patterns that are formed by value ranges over the input features. A region label is a human assessment of the class in the data population covered by the region. By leveraging algorithms, regions and their class proportions can be used to train instance-based classification models. However, the key challenge is that in practice, very few regions are defined already. Therefore, to identify regions important for model learning, we design a (HAL) framework, which actively builds a hierarchy of regions. Similar to the decision-tree learning process, our approach progressively divides the input data space into smaller sub-regions, solicits labels for the new regions, and retrains the base classification model with all the leaf regions. And we further develop a (forest) solution, which builds multiple shallower hierarchies that have more informative, diverse, and simpler regions. We evaluate our HAL framework on numerous impactful classification datasets as well as on a real user study - on the survival analysis of colorectal cancer patients. The results demonstrate that region-based active learning methods can learn high-quality classifiers from very few labeled regions. Hence, our framework is shown very effective in reducing the human annotation effort needed for building classification models.
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5. A Neural Database for Answering Aggregate Queries on Incomplete Relational Data.
5. 一种用于在不完整关系数据上回答聚合查询的神经数据库PMID:日期:2023-10-27Real-world datasets are often incomplete due to data collection cost, privacy considerations or as a side effect of data integration/preparation. We focus on answering aggregate queries on such datasets, where data incompleteness causes the answers to be inaccurate. To address this problem, assuming typical relational data, existing work generates synthetic data to complete the database, a challenging task, especially in the presence of bias in observed data. Instead, we propose a paradigm shift by learning to directly estimate query answers, circumventing the difficult data generation step. Our approach, dubbed NeuroComplete, learns to answer queries in three steps. First, NeuroComplete generates a set of queries for which accurate answers can be computed given the incomplete dataset. Next, it embeds queries in a feature space, through which each query is effectively represented with the portion of the database that contributes to the query answer. Finally, it trains a neural network in a supervised learning fashion: both query features (input) and correct answers (labels) are known. The learned model generates accurate answers to new queries at test time, exploiting the generalizability of the learned model in the embedding space. Extensive experimental results on real datasets show up to 4 times for AVG queries and 10 times for COUNT queries error reduction compared with the state-of-the-art.
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6. Pushing ML Predictions Into DBMSs.
6. 将机器学习预测推送到DBMS中PMID:日期:2023-04-24In the past decade, many approaches have been suggested to execute ML workloads on a DBMS. However, most of them have looked at in-DBMS ML from a training perspective, whereas ML inference has been largely overlooked. We think that this is an important gap to fill for two main reasons: (1) in the near future, every application will be infused with some sort of ML capability; (2) behind every web page, application, and enterprise there is a DBMS, whereby in-DBMS inference is an appealing solution both for efficiency (e.g., less data movement), performance (e.g., cross-optimizations between relational operators and ML) and governance. In this article, we study whether DBMSs are a good fit for prediction serving. We introduce a technique for translating trained ML pipelines containing both featurizers (e.g., one-hot encoding) and models (e.g., linear and tree-based models) into SQL queries, and we compare in-DBMS performance against popular ML frameworks such as Sklearn and ml.net. Our experiments show that, when pushed inside a DBMS, trained ML pipelines can have performance comparable to ML frameworks in several scenarios, while they perform quite poorly on text featurization and over (even simple) neural networks.
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7. Weakly Supervised Concept Map Generation through Task-Guided Graph Translation.
PMID:日期:2023-03-06Recent years have witnessed the rapid development of concept map generation techniques due to their advantages in providing well-structured summarization of knowledge from free texts. Traditional unsupervised methods do not generate task-oriented concept maps, whereas deep generative models require large amounts of training data. In this work, we present (Graph Translation-based Document To Graph), an automatic concept map generation framework that leverages generalized NLP pipelines to derive semantic-rich initial graphs, and translates them into more concise structures under the weak supervision of downstream task labels. The concept maps generated by can provide interpretable summarization of structured knowledge for the input texts, which are demonstrated through human evaluation and case studies on three real-world corpora. Further experiments on the downstream task of document classification show that beats other concept map generation methods. Moreover, we specifically validate the labeling efficiency of in the label-efficient learning setting and the flexibility of generated graph sizes in controlled hyper-parameter studies.
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8. {"_":"M: Mixed Models With Preferences, Popularities and Transitions for Next-Basket Recommendation.","sup":["2"]}
8. {"_":"混合模型：结合偏好、流行度和转移的下一次购物篮推荐。","sup":["2"]}PMID:日期:2022-01-13Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans) to better model the transition patterns among items. We compared M with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.
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9. Identifying Anomalies while Preserving Privacy.
PMID:日期:2021-11-22Identifying anomalies in data is vital in many domains, including medicine, finance, and national security. However, privacy concerns pose a significant roadblock to carrying out such an analysis. Since existing privacy definitions do not allow good accuracy when doing outlier analysis, the notion of sensitive privacy has been recently proposed to deal with this problem. Sensitive privacy makes it possible to analyze data for anomalies with practically meaningful accuracy while providing a strong guarantee similar to differential privacy, which is the prevalent privacy standard today. In this work, we relate sensitive privacy to other important notions of data privacy so that one can port the technical developments and private mechanism constructions from these related concepts to sensitive privacy. Sensitive privacy critically depends on the underlying anomaly model. We develop a novel n-step lookahead mechanism to efficiently answer arbitrary outlier queries, which provably guarantees sensitive privacy if we restrict our attention to common a class of anomaly models. We also provide general constructions to give sensitively private mechanisms for identifying anomalies and show the conditions under which the constructions would be optimal.
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10. Domain-specific Topic Model for Knowledge Discovery in Computational and Data-Intensive Scientific Communities.
10. 面向计算与数据密集型科学社区知识发现的领域特定主题模型PMID:日期:2021-07-01Shortened time to knowledge discovery and adapting prior domain knowledge is a challenge for computational and data-intensive communities such as e.g., bioinformatics and neuroscience. The challenge for a domain scientist lies in the actions to obtain guidance through query of massive information from diverse text corpus comprising of a wide-ranging set of topics when: investigating new methods, developing new tools, or integrating datasets. In this paper, we propose a novel "domain-specific topic model" (DSTM) to discover latent knowledge patterns about relationships among research topics, tools and datasets from exemplary scientific domains. Our DSTM is a generative model that extends the Latent Dirichlet Allocation (LDA) model and uses the Markov chain Monte Carlo (MCMC) algorithm to infer latent patterns within a specific domain in an unsupervised manner. We apply our DSTM to large collections of data from bioinformatics and neuroscience domains that include more than 25,000 of papers over the last ten years, featuring hundreds of tools and datasets that are commonly used in relevant studies. Evaluation experiments based on generalization and information retrieval metrics show that our model has better performance than the state-of-the-art baseline models for discovering highly-specific latent topics within a domain. Lastly, we demonstrate applications that benefit from our DSTM to discover intra-domain, cross-domain and trend knowledge patterns.