Information Processing & Management最新文献_第5页

Q-value guided Text-to-SQL generation: Structured reasoning meets efficient inference exploration q值引导的文本到sql生成：结构化推理满足高效推理探索

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-06-01 Epub Date: 2026-01-15 DOI: 10.1016/j.ipm.2025.104607

Min Tang , Lixin Zou , Shujie Cui , Weiqing Wang , Zhe Jin , Chengliang Li , Shiuan-Ni Liang

{"title":"Q-value guided Text-to-SQL generation: Structured reasoning meets efficient inference exploration","authors":"Min Tang , Lixin Zou , Shujie Cui , Weiqing Wang , Zhe Jin , Chengliang Li , Shiuan-Ni Liang","doi":"10.1016/j.ipm.2025.104607","DOIUrl":"10.1016/j.ipm.2025.104607","url":null,"abstract":"<div><div>Translating natural language queries into executable SQL queries (Text-to-SQL) is essential for human-database interaction but remains a challenging task, especially for complex queries. Existing methods based on large language models (LLMs) often rely on supervised fine-tuning or iterative refinement based on execution success. However, these methods overlook errors that occur during the generation process, leading to error propagation issues. To address this, we propose the <em>Q</em>-value guided Text-to-SQL, which trains a <em>Q</em>-value network to evaluate SQL potential and avoid generation errors. Specifically, QSQL decomposes the complex SQL generation process into simpler and helpful intermediate steps, such as selection, schema linking, sub-query generation, and combination. Then, we adopt Monte Carlo Tree Search (MCTS) to estimate <em>Q</em>-values of each step and incorporate rule-based consensus filtering to eliminate inconsistent/low-quality data, removing the need for extra human annotations. Finally, a <em>Q</em>-value probe network approximates the estimated values for efficient generation. Experimental results on the <span>Spider</span> and <span>Bird</span> benchmarks show that QSQL achieves a superior balance between execution accuracy and inference efficiency. Compared to a baseline MCTS approach, QSQL improves execution accuracy by <strong>9%</strong> while reducing inference time to just <strong>11.9%</strong>.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 4","pages":"Article 104607"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correlation guided multi-teacher distillation for lightweight image retrieval 基于相关性的多教师蒸馏轻量图像检索

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-06-01 Epub Date: 2026-01-18 DOI: 10.1016/j.ipm.2025.104583

Xiangkun Ban , Guang-Hai Liu , Zhou Lu , Bo-Jian Zhang

{"title":"Correlation guided multi-teacher distillation for lightweight image retrieval","authors":"Xiangkun Ban , Guang-Hai Liu , Zhou Lu , Bo-Jian Zhang","doi":"10.1016/j.ipm.2025.104583","DOIUrl":"10.1016/j.ipm.2025.104583","url":null,"abstract":"<div><div>Image retrieval based on knowledge distillation has enormous potential for lightweight image retrieval. However, existing methods often transfer redundant or inconsistent knowledge to a single student model, resulting in it still rely on the teacher model during the inference phase. To address this problem, an efficient knowledge distillation method based on the multi-teacher distillation framework is proposed to form the basis for lightweight image retrieval, namely the correlation guided distillation (CGD), where correlation coefficient fusion and Cosine-based product quantization are two novel components. The former effectively mitigates noise and conflicting supervision in multi-teacher fusion, the latter encodes rich directional semantics to enhance structural alignment. The whitened teacher features are fused via using correlation coefficient fusion method, which is used to guide the student model through the structural similarity supervision provided by Cosine-based product quantization. Extensive experiments on several benchmark datasets demonstrate that our distillation framework achieves superior performance compared with the latest teacher-dependent methods under the same settings. In terms of the mAP scores with the M and H protocol setups, the performances are improved by 4.1% and 4.5% on the ROxford dataset, and by 2.1% and 4.4% on the RParis dataset. On the large-scale variants with 1 M distractor images, the respective improvements reach 7.9% and 9.0% on ROxford+1M, and 3.3% and 6.6% on RParis+1M. Furthermore, when using only the student model, our CGD method is up to 48.83 × faster than the teacher-dependent methods, without reducing retrieval performance.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 4","pages":"Article 104583"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural embeddings of collaboration networks predict citation impact and innovation 协作网络的神经嵌入预测引文影响和创新

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-06-01 Epub Date: 2026-01-20 DOI: 10.1016/j.ipm.2026.104634

Chengyu Li , Wenlong Yang , Meiling Li , Yang Wang

{"title":"Neural embeddings of collaboration networks predict citation impact and innovation","authors":"Chengyu Li , Wenlong Yang , Meiling Li , Yang Wang","doi":"10.1016/j.ipm.2026.104634","DOIUrl":"10.1016/j.ipm.2026.104634","url":null,"abstract":"<div><div>Scientific collaboration is a key driver of breakthroughs in contemporary science. While prior research has primarily focused on team diversity based on individual attributes, quantifying structural diversity based on collaborative relationships at the individual paper level remains underexplored. In this study, we apply neural embedding techniques to quantify structural diversity at the paper level and examine its association with research outcomes. Leveraging the node2vec and GraphSAGE algorithms, we embed each scientist into a dense vector space using the Microsoft Academic Graph dataset, covering 4,772,781 papers written by 5,162,397 scientists from 1995 to 2016. We show that our embedding model effectively classifies scientists across scientific domains and accurately predicts their primary scientific domains. Crucially, our analysis reveals that structural diversity is strongly associated with citation impact, novelty, and disruption at the individual paper level. Specifically, a 1SD increase in structural diversity is associated with a 7.6%, 24.7%, and 2.9% increase in citation impact, and the odds of publishing novel and disruptive papers, respectively. These findings are generalizable across multiple model specifications, time spans, different team sizes, and scientific domains. Finally, our analysis reveals that structural diversity exhibits the strongest correlation with citation impact, novelty, and disruption among all examined diversity measures. Our study highlights the importance of fostering structurally diverse collaborations and has policy implications for institutions, funders, and governments aiming to support impactful and innovative research.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 4","pages":"Article 104634"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Less is more: Towards green code large language models via unified structural pruning 少即是多：通过统一的结构修剪实现绿色代码大型语言模型

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-06-01 Epub Date: 2026-01-01 DOI: 10.1016/j.ipm.2025.104580

Guang Yang , Yu Zhou , Xiangyu Zhang , Wei Cheng , Ke Liu , Xiang Chen , Terry Yue Zhuo , Taolue Chen

{"title":"Less is more: Towards green code large language models via unified structural pruning","authors":"Guang Yang , Yu Zhou , Xiangyu Zhang , Wei Cheng , Ke Liu , Xiang Chen , Terry Yue Zhuo , Taolue Chen","doi":"10.1016/j.ipm.2025.104580","DOIUrl":"10.1016/j.ipm.2025.104580","url":null,"abstract":"<div><div>The extensive application of Large Language Models (LLMs) in generative coding tasks has raised concerns due to their high computational demands and energy consumption. Unlike previous structural pruning methods designed for classification models that deal with low-dimensional classification logits, generative Code LLMs produce high-dimensional token logit sequences, making traditional pruning objectives inherently limited. Moreover, existing single-component pruning approaches further constrain the effectiveness when applied to generative Code LLMs. In response, we propose <span>Flab-Pruner</span>, an innovative unified structural pruning method that combines vocabulary, layer, and Feed-Forward Network (FFN) pruning. This approach effectively reduces model parameters while maintaining performance. Additionally, we introduce a customized code instruction data strategy for coding tasks to enhance the performance recovery efficiency of the pruned model. Through extensive evaluations on three state-of-the-art Code LLMs across multiple generative coding tasks, the results demonstrate that <span>Flab-Pruner</span> retains 97% of the original code generation performance on average after pruning 22% of the parameters, and achieves the same or even better performance after post-training. The pruned models exhibit significant improvements in storage, GPU usage, computational efficiency, and environmental impact, while maintaining well robustness. Our research provides a sustainable solution for green software engineering and promotes the efficient deployment of LLMs in real-world generative coding intelligence applications.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 4","pages":"Article 104580"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SCTNet: Structured and causality-guided spatiotemporal diffusion network for unsupervised traffic accident detection 面向无监督交通事故检测的结构化因果导向时空扩散网络

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-06-01 Epub Date: 2025-12-30 DOI: 10.1016/j.ipm.2025.104598

Huilin Liu , Xiaolong Hu , Yu Jiang , Tianyue Wan , Wanqi Ma

{"title":"SCTNet: Structured and causality-guided spatiotemporal diffusion network for unsupervised traffic accident detection","authors":"Huilin Liu , Xiaolong Hu , Yu Jiang , Tianyue Wan , Wanqi Ma","doi":"10.1016/j.ipm.2025.104598","DOIUrl":"10.1016/j.ipm.2025.104598","url":null,"abstract":"<div><div>Accurate detections of traffic accidents are challenging, and existing detection methods struggle to maintain robustness to diverse conditions. To address this, we propose a structured and causality-guided spatiotemporal diffusion network (SCTNet) for unsupervised traffic accident detection. The SCTNet framework integrates dual-phase patch sampling (DPPS) to mitigate sampling bias between training and testing phases. Spatiotemporal causal graph fusion (STCGF) captures the causal dependencies among interacting agents, and a structured spatiotemporal noise (SSTN) mechanism enhances temporal sensitivity and context consistency. The diffusion-based dual-stream design enables the fusion of visual and motion information for robust spatiotemporal representation learning. Experiments conducted on two traffic datasets show that SCTNet achieves higher detection accuracy and stronger cross-domain generalization than existing methods. More generally, our study contributes to data-driven decision making and research on intelligent information systems in complex, dynamic transportation environments. The source code is available at <span><span>https://github.com/Jasoncode0115/SCTNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 4","pages":"Article 104598"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepMark: A proactive deep learning-based watermarking model for tamper detection and localization across images and videos DeepMark：一种主动的基于深度学习的水印模型，用于图像和视频的篡改检测和定位

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-06-01 Epub Date: 2026-01-07 DOI: 10.1016/j.ipm.2025.104600

Iram Abrar, Javaid A. Sheikh

{"title":"DeepMark: A proactive deep learning-based watermarking model for tamper detection and localization across images and videos","authors":"Iram Abrar, Javaid A. Sheikh","doi":"10.1016/j.ipm.2025.104600","DOIUrl":"10.1016/j.ipm.2025.104600","url":null,"abstract":"<div><div>The current DeepMark model is used to address the challenge of surviving routine transformations compression, resizing, and color adjustments while reliably exposing malicious edits. It embeds multi-bit payloads (32-512 bits) into both images and video frames through a structured process from unaltered embedding to aggressive benign edits, after which targeted malicious attacks are applied to reveal tampering. The framework was tested on multiple datasets, including CelebA, MIRFlickr, and UCF-101, using U-Net, ResNet, CNN, and LSTM. For a 64-bit message, U-Net achieves excellent visual quality (PSNR 39.44 dB, SSIM 0.981) and nearly perfect recovery under benign conditions (bit accuracy > 99.8%). In contrast, its recovery accuracy falls to about 50% when facing malicious changes, successfully differentiating between benign and malicious transformations. The tamper classifier trained on features from this setup reached an AUC of 100%, outperforming existing state-of-the-art methods. Other models demonstrated varied trade-offs: CNN attained PSNR ∼39.04 dB/0.97 SSIM at smaller payloads but collapsed beyond 128 bits; LSTM peaked at ∼25.82 dB/0.73 SSIM; and ResNet at ∼25.17dB/0.74 SSIM with > 98% benign BRA. Additionally, DeepMark generates pixel-level tamper maps and includes an ablation over payload size and loss-weighting λ to guide system tuning. This versatile approach offers high-fidelity watermarking, precise tamper localization, and reliable distinction between benign edits and malicious tampering across both images and video.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 4","pages":"Article 104600"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive overlap penalization and probabilistic modeling in hypergraph influence maximization 超图中的自适应重叠惩罚和概率建模影响最大化

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-06-01 Epub Date: 2026-01-03 DOI: 10.1016/j.ipm.2025.104594

Lingyu Wu , Cong Li , Bo Qu , Xiang Li

{"title":"Adaptive overlap penalization and probabilistic modeling in hypergraph influence maximization","authors":"Lingyu Wu , Cong Li , Bo Qu , Xiang Li","doi":"10.1016/j.ipm.2025.104594","DOIUrl":"10.1016/j.ipm.2025.104594","url":null,"abstract":"<div><div>Influence maximization (IM) algorithms aim to iteratively identify a seed set that could maximize the spreading range. In this paper, we concentrate on the hypergraph influence maximization (HyperIM) problem. The overlapping neighborhood caused by the higher-order interactions leads to an overestimation of the diffusion capability of candidate nodes. Moreover, selecting the candidate node with a high infected probability as a new seed node is low payoff with a small influence range gain. Thus, we develop adaptive metrics and propose two algorithms, i.e., high adaptive contact efficiency (HACE) algorithm and high contact with a low infected probability (HCLI) algorithm. First, we penalize the contribution of the neighborhood of the seed set to the evaluation of the influence gain to the seed set to correct the impact of overlapping influence. Additionally, the proposed HACE algorithm uses the being contacted capability to reveal the infected possibility of candidate nodes, while the proposed HCLI algorithm estimates the global infected probability of nodes. The experiments and analysis on eight real-world hypergraphs demonstrate the better balance of the HACE and HCLI algorithms than the state-of-the-art (SOTA) algorithms in selecting influential seed set and ensuring computational efficiency. Compared with the existing SOTA algorithms, HACE and HCLI run at least ten times faster than SOTA, and at most nearly 70 times faster. On large-scale hypergraphs, the HACE and HCLI algorithms still show great computational efficiency and significantly improved performances compared with other low-time complexity algorithms.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 4","pages":"Article 104594"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph embedding-based dual-channel quality prediction method for yarn manufacturing system 基于图嵌入的纱线制造系统双通道质量预测方法

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-06-01 Epub Date: 2025-12-30 DOI: 10.1016/j.ipm.2025.104592

Xiaohu Zheng , Siqi Du , Zhifeng Liu , Jian Wang , Shunli Hou , Ke Wang

{"title":"Graph embedding-based dual-channel quality prediction method for yarn manufacturing system","authors":"Xiaohu Zheng , Siqi Du , Zhifeng Liu , Jian Wang , Shunli Hou , Ke Wang","doi":"10.1016/j.ipm.2025.104592","DOIUrl":"10.1016/j.ipm.2025.104592","url":null,"abstract":"<div><div>Yarn quality is a critical control object in the textile industry. Accurate quality prediction not only reduces costs but also provides data support for process optimization. However, the high-dimensional and noisy data in the yarn production process cause poor model predictive capability, and existing methods struggle to capture the complex interrelationships between processes. To address this, a dual-channel quality prediction method based on graph embedding is proposed. Specifically, a process-oriented heterogeneous network is constructed to represent the production process nodes and their collaborative relationships in the form of a directed heterogeneous graph. Based on this graph structure, a dynamically adjustable embedding module is designed to generate node embeddings with good interpretability for the process flow. A dual-channel quality prediction architecture is then designed for quality prediction in scenarios with noisy data. The proposed method is experimentally validated on cotton production data from an enterprise with 608 data samples. The results show that the proposed method demonstrates the best overall performance in predicting different yarn quality indicators. The mean square error, mean absolute error and root mean square error are reduced by 45.3%, 34.5% and 25.2% on average, respectively. This provides a new modeling reference for quality prediction in manufacturing scenarios with clear process flows.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 4","pages":"Article 104592"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cognitive alignment network: Integrating sensory-perceptual cues for predicate similarity discrimination 认知对齐网络：谓词相似性判别的感官-知觉线索整合

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-06-01 Epub Date: 2025-12-30 DOI: 10.1016/j.ipm.2025.104577

Na Tian , Qihang Jia , Wenna Liu , Xiangfu Ding , Wencang Zhao

{"title":"Cognitive alignment network: Integrating sensory-perceptual cues for predicate similarity discrimination","authors":"Na Tian , Qihang Jia , Wenna Liu , Xiangfu Ding , Wencang Zhao","doi":"10.1016/j.ipm.2025.104577","DOIUrl":"10.1016/j.ipm.2025.104577","url":null,"abstract":"<div><div>Scene graph generation is a crucial task in visual scene understanding and reasoning, but its performance is often limited by the long-tail distribution of relationships. The high similarity of predicate semantics makes tail predicates easily covered by high-frequency head predicates, leading to semantic confusion and a decline in rare relationship recognition performance. To address it, we propose the Cognitive Alignment Network, which draws inspiration from the cognitive psychology mechanism of sensation and perception for alleviating predicate semantic confusion. It explicitly models the reasoning process by separating coarse-grained sensory capture from fine-grained perceptual reasoning. The sensory sensitive module enhances the extraction of object features from visual stimuli, while the perceptual reinforcement module improves the relationship instances between similar predicates to ensure fine-grained semantic distinctions without altering the underlying meaning. Experiments show that the mR@K of CANet outperforms the current state-of-the-art method by 2.6% on Visual Genome and its <em>Score<sub>wtd</sub></em> improves by 1.4% on Open Images V6. Visualization results also validate that incorporating cognitive mechanisms into scene graphs can effectively mitigate the long-tail problem and enhance the model’s generalization and reasoning capabilities in real-world scenes.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 4","pages":"Article 104577"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DS_HURNSP: An effective method for mining high utility repeated negative sequential patterns from data streams DS_HURNSP：从数据流中挖掘高效用重复负序列模式的有效方法

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2026-06-01 Epub Date: 2026-01-22 DOI: 10.1016/j.ipm.2026.104637

Xiangjun Dong , Yicong Zhen , Ping Qiu , Jing Chi , Lei Guo , Wenpeng Lu , Long Zhao , Yongshun Gong , Yuhai Zhao

{"title":"DS_HURNSP: An effective method for mining high utility repeated negative sequential patterns from data streams","authors":"Xiangjun Dong , Yicong Zhen , Ping Qiu , Jing Chi , Lei Guo , Wenpeng Lu , Long Zhao , Yongshun Gong , Yuhai Zhao","doi":"10.1016/j.ipm.2026.104637","DOIUrl":"10.1016/j.ipm.2026.104637","url":null,"abstract":"<div><div>Mining high utility repeated negative sequential patterns (HURNSPs) from data streams is an important method for data stream analysis. However, the existing methods in this topic don’t consider negative events and repeated events, which results in weak decision-making effectiveness. So in this paper, we propose an effective algorithm DS_HURNSP for mining HURNSPs from data streams based on a sliding window model. First, we propose an effective utility-list prefix tree structure to store high utility repeated positive sequential patterns (HURPSPs). Second, we construct a utility mapping set based on a hash-table structure to enable rapid querying of HURPSPs information. Finally, we propose a two-stage computation method to compute the utility of high utility repeated negative sequential candidates (HURNSCs) by mapping them to the set of HURPSPs, avoiding rescanning database. Extensive experiments on six datasets show that the DS_HURNSP algorithm generates tens to thousands of times as many HURNSPs as the baseline method, and reduces the average runtime by more than half.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 4","pages":"Article 104637"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0