Information Systems最新文献_第5页

Special issue on verification, control, and repair in business process management 关于业务流程管理中的验证、控制和修复的专刊

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-02-01 Epub Date: 2025-09-11 DOI: 10.1016/j.is.2025.102619

Matteo Zavatteri, Massimiliano de Leoni, Johann Eder, Manfred Reichert

引用次数: 0

Process-driven visual analysis of cybersecurity capture the flag exercises 流程驱动的网络安全可视化分析夺旗演习

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-02-01 Epub Date: 2025-09-18 DOI: 10.1016/j.is.2025.102627

Radek Ošlejšek, Radoslav Chudovský, Martin Macak

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Reflection on community-diversified influence maximization in social networks 关于社交网络中社区多元化影响力最大化的思考

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-02-01 Epub Date: 2025-09-21 DOI: 10.1016/j.is.2025.102630

Jianxin Li , Taotao Cai , Ke Deng , Timos Sellis , Feng Xia

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A task taxonomy for conformance checking 用于一致性检查的任务分类法

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-02-01 Epub Date: 2025-08-23 DOI: 10.1016/j.is.2025.102605

Jana-Rebecca Rehse , Michael Grohs , Finn Klessascheck , Lisa-Marie Klein , Tatiana von Landesberger , Luise Pufahl

{"title":"A task taxonomy for conformance checking","authors":"Jana-Rebecca Rehse , Michael Grohs , Finn Klessascheck , Lisa-Marie Klein , Tatiana von Landesberger , Luise Pufahl","doi":"10.1016/j.is.2025.102605","DOIUrl":"10.1016/j.is.2025.102605","url":null,"abstract":"<div><div>Conformance checking is a sub-discipline of process mining, which compares observed process traces with a process model to analyze whether the process execution conforms with or deviates from the process design. Organizations can leverage this analysis, for example to check whether their processes comply with internal or external regulations or to identify potential improvements. Gaining these insights requires suitable visualizations, which make complex results accessible and actionable. So far, however, the development of conformance checking visualizations has largely been left to tool vendors. As a result, current tools offer a wide variety of visual representations for conformance checking, but the analytical purposes they serve often remain unclear. However, without a systematic understanding of these purposes, it is difficult to evaluate the visualizations’ usefulness. Such an evaluation hence requires a deeper understanding of conformance checking as an analysis domain. To this end, we propose a task taxonomy, which categorizes the tasks that can occur when conducting conformance checking analyses. This taxonomy supports researchers in determining the purpose of visualizations, specifying relevant conformance checking tasks in terms of their goal, means, constraint type, data characteristics, data target, and data cardinality. Combining concepts from process mining and visual analytics, we address researchers from both disciplines to enable and support closer collaborations.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102605"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904083","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}

引用次数: 0

Dynamic group recommender methodology: Leveraging temporal trust and confidence graphs 动态小组推荐方法：利用时间信任和信心图

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-02-01 Epub Date: 2025-09-03 DOI: 10.1016/j.is.2025.102612

Khadijeh Rahimkhani, Kamran Zamanifar

{"title":"Dynamic group recommender methodology: Leveraging temporal trust and confidence graphs","authors":"Khadijeh Rahimkhani, Kamran Zamanifar","doi":"10.1016/j.is.2025.102612","DOIUrl":"10.1016/j.is.2025.102612","url":null,"abstract":"<div><div>Group recommender systems aim to recommend items to groups with shared interests, aiming to satisfy each member. Managing trust and mutual influence within the group is a key challenge that influences the choice of items by users. These systems generate suggestions for the group based on inter-member trust. A less explored but critical aspect of this trust is its evolution, which can affect the group's item selections. This paper aims to assess the impact of time on trust in group recommendations. We begin by constructing a time-based confidence graph derived from the items selected by the group members. This graph allows us to measure the confidence levels between members and plays a crucial role in identifying their risk tolerance towards new items. Recognizing that members' risk-taking behavior can influence the group, we identify members who significantly affect group decisions. The confidence graph is periodically updated to reflect new user choices and the influence of key members. Ultimately, we introduce a novel method for calculating implicit trust based on similarity and confidence metrics, providing a recommendation list that maximizes group satisfaction based on the computed trust levels. Finally, the proposed method is evaluated using MovieLens100k, MovieLens10M, Epinions and Yelp datasets. The results demonstrate significant improvements in Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Precision, and group satisfaction measures compared to current state-of-the-art techniques.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102612"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027195","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}

引用次数: 0

Compressed consecutive pattern matching 压缩连续模式匹配

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-02-01 Epub Date: 2025-08-28 DOI: 10.1016/j.is.2025.102607

Paweł Gawrychowski , Garance Gourdel , Tatiana Starikovskaya , Teresa Anna Steiner

引用次数: 0

Modern alternatives to hive: A systematic review and single-node benchmark of SQL-on-Hadoop and lakehouse engines hive的现代替代品：对SQL-on-Hadoop和lake - house引擎的系统回顾和单节点基准测试

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-02-01 Epub Date: 2025-10-08 DOI: 10.1016/j.is.2025.102635

Szoke Mark-Andor

{"title":"Modern alternatives to hive: A systematic review and single-node benchmark of SQL-on-Hadoop and lakehouse engines","authors":"Szoke Mark-Andor","doi":"10.1016/j.is.2025.102635","DOIUrl":"10.1016/j.is.2025.102635","url":null,"abstract":"<div><div>This paper presents a systematic review of modern SQL-on-Hadoop and lakehouse engines as alternatives to Apache Hive and complements it with a new experimental benchmark on a single, common platform. Twelve representative engines are analyzed (selected for architectural diversity, ecosystem relevance, and evidence availability) and a compact feature taxonomy is introduced, covering vectorized execution, whole-stage code generation, LLVM/JIT, federated pushdown, cloud columnar caches, and embedded ML primitives. In addition to the qualitative synthesis, 11 engines (all except Databricks Photon) are benchmarked locally on standardized TPC-H queries at two scale factors (SF = 1 and SF = 10) using the same hardware and software environment. All engines were tested on a single node (4 CPU cores, 8 hardware threads) to provide a common, multi-threaded baseline. A multi-node (cloud) evaluation is outside the present scope and is discussed as future work in Section <span><span>6</span></span>. The results show order-of-magnitude differences between engines: native/vectorized engines (e.g., DuckDB, ClickHouse, Impala, Velox-accelerated Presto) achieve substantially lower runtimes and more efficient scale-up than JVM-only stacks (e.g., Spark SQL, Trino) on a single node. Drill failed at SF = 10 due to out-of-memory errors; Phoenix completed SF = 1 but did not complete SF = 10 on an 8GB system (one long query aborting). Photon is discussed from the literature because it cannot be executed outside Databricks. All scripts, data, and a runnable artifact for the benchmark are open-sourced to support reproducibility and reuse. <strong>Scope:</strong> While the primary focus is distributed SQL-on-Hadoop systems, modern single-node engines (e.g., DuckDB, DataFusion) are included where they (i) implement architectural innovations relevant to distributed analytics or (ii) are used in lakehouse deployments. These are explicitly distinguished from distributed systems in the taxonomy and results.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102635"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266394","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}

引用次数: 0

Robust Graph Contrastive Learning for recommender systems: Addressing data sparsity and noise 推荐系统的鲁棒图对比学习：处理数据稀疏性和噪声

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-02-01 Epub Date: 2025-09-13 DOI: 10.1016/j.is.2025.102625

Yongqing Li , Qimeng Yang , Long Yu , ShengWei Tian , Xin Fan

{"title":"Robust Graph Contrastive Learning for recommender systems: Addressing data sparsity and noise","authors":"Yongqing Li , Qimeng Yang , Long Yu , ShengWei Tian , Xin Fan","doi":"10.1016/j.is.2025.102625","DOIUrl":"10.1016/j.is.2025.102625","url":null,"abstract":"<div><div>Graph Contrastive Learning (GCL) enhances recommender systems by leveraging Graph Neural Networks (GNNs) and self-supervised learning (SSL). However, existing methods struggle with data sparsity and noise. We propose Robust Graph Contrastive Learning (RoGCL), a novel framework that generates high-quality contrastive views through dual-perspective generators. The local generator employs Variational Graph Autoencoders (VGAE) to capture micro-level collaborative patterns by sampling from user–item interaction distributions. The global generator utilizes Singular Value Decomposition (SVD) to reconstruct macro-level structures while filtering noise through low-rank approximation. By incorporating Information Bottleneck (InfoBN) to minimize redundancy between views, RoGCL learns robust representations combining local and global collaborative signals. Extensive experiments on Last.FM, Yelp, and BeerAdvocate datasets demonstrate that RoGCL significantly outperforms state-of-the-art methods including Self-supervised Graph Learning (SGL), Neural Collaborative Learning (NCL), and Adaptive Graph Contrastive Learning (AdaGCL). Results show improved Recall@20 by up to 8.7% and NDCG@20 by 5.8% compared to best baselines. Notably, RoGCL exhibits exceptional robustness, maintaining over 90% relative performance with 25% noise injection and showing 37.7% improvement for sparse user groups, making it particularly suitable for real-world applications with imperfect data.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102625"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106041","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}

引用次数: 0

Validating temporal compliance patterns: A unified approach with MTLf over various data models 验证时间遵从性模式：在各种数据模型上使用MTLf的统一方法

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-02-01 Epub Date: 2025-09-11 DOI: 10.1016/j.is.2025.102623

Nesma M. Zaki , Iman M.A. Helal , Ehab E. Hassanein , Ahmed Awad

{"title":"Validating temporal compliance patterns: A unified approach with MTLf over various data models","authors":"Nesma M. Zaki , Iman M.A. Helal , Ehab E. Hassanein , Ahmed Awad","doi":"10.1016/j.is.2025.102623","DOIUrl":"10.1016/j.is.2025.102623","url":null,"abstract":"<div><div>Process mining extracts valuable insights from event data to help organizations improve their business processes, which is essential for their growth and success. By leveraging process mining techniques, organizations gain a comprehensive understanding of their processes’ execution, enabling the discovery of process models, detection of deviations, i.e., conformance checking, identification of bottlenecks, and assessment of performance. Compliance checking, a specific area within conformance checking, ensures that the organizational activities adhere to prescribed process models and regulations. Linear Temporal Logic over finite traces (<span><math><mrow><mi>L</mi><mi>T</mi><msub><mrow><mi>L</mi></mrow><mrow><mi>f</mi></mrow></msub></mrow></math></span> ) is commonly used for conformance checking, but it may not capture all temporal aspects accurately. This paper proposes Metric Temporal Logic over finite traces (<span><math><mrow><mi>M</mi><mi>T</mi><msub><mrow><mi>L</mi></mrow><mrow><mi>f</mi></mrow></msub></mrow></math></span> ) to define explicit time-related constraints effectively in addition to the implicit time-ordering covered by <span><math><mrow><mi>L</mi><mi>T</mi><msub><mrow><mi>L</mi></mrow><mrow><mi>f</mi></mrow></msub></mrow></math></span>. Therefore, it provides a universal formal approach to capture compliance rules. Moreover, we define a minimal set of generic <span><math><mrow><mi>M</mi><mi>T</mi><msub><mrow><mi>L</mi></mrow><mrow><mi>f</mi></mrow></msub></mrow></math></span> formulas and show that they are capable of capturing all the common patterns for compliance rules.</div><div>As compliance validation is largely driven by the data model used to represent the event logs, we provide a mapping from <span><math><mrow><mi>M</mi><mi>T</mi><msub><mrow><mi>L</mi></mrow><mrow><mi>f</mi></mrow></msub></mrow></math></span> to the common data models we found in the literature to encode event logs, namely, the relational and the graph models. A comprehensive study comparing various data models and an empirical evaluation across real-life event logs demonstrate the effectiveness of the proposed approach.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102623"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106043","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}

引用次数: 0

Synthesizing goal models from declarative data-centric process models 从声明性的以数据为中心的流程模型合成目标模型

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-02-01 Epub Date: 2025-09-20 DOI: 10.1016/j.is.2025.102626

Rik Eshuis , Aditya Ghose

{"title":"Synthesizing goal models from declarative data-centric process models","authors":"Rik Eshuis , Aditya Ghose","doi":"10.1016/j.is.2025.102626","DOIUrl":"10.1016/j.is.2025.102626","url":null,"abstract":"<div><div>Knowledge-intensive processes progress towards the achievement of operational goals. These processes typically rely on data to enable data-driven decision making, but also require substantial flexibility to deal with the complex and dynamic environments in which they operate. Consequently, declarative data-centric process modeling languages such as the Case Management Model and Notation (CMMN) have been proposed to model knowledge-intensive processes. However, while such process models allow to express goals, they specify dependencies between the goals only implicitly. This makes the goal-oriented behavior of declarative data-centric process models hard to understand, and therefore obfuscates the goal-oriented behavior of knowledge-intensive processes. This paper defines a structural, semi-automated approach to explicate the goal-oriented aspects of declarative data-centric process models. The approach first derives goal relations from a declarative data-centric process model and next synthesizes these goal relations into a goal model using an algorithm. The approach is supported by a tool and has been evaluated in case studies. Using the approach, implicit goal dependencies in declarative data-centric process models are expressed in goal models. This supports the understanding of goal-oriented aspects of declarative data-centric process models.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"136 ","pages":"Article 102626"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158144","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}

引用次数: 0