Information Systems最新文献_第3页

Efficient allocation of shared resources across multiple processes 跨多个进程有效地分配共享资源

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-12-05 DOI: 10.1016/j.is.2025.102663

Kiran Busch, Henrik Leopold

Effective resource allocation is crucial for optimizing business processes. Yet, most existing methods focus solely on single-process optimization, overlooking the interdependencies present in multi-process environments. This limitation results in inefficient resource allocation, and scalability challenges. To address this gap, we propose MuProMAC (Multi-Process Multi-Agent Coordination), a novel reinforcement learning-based method designed to optimize resource allocation across multiple interdependent business processes. Unlike prior methods, MuProMAC is the first online resource allocation method that explicitly models the interdependencies between processes and dynamically balances competing resource demands to minimize global average cycle time. We evaluate our method in five multi-process scenarios with different levels of resource contention, comparing it against state-of-the-art online resource allocation methods and three simple baselines. Our results show that MuProMAC is consistently among the top-performing methods in shared-resource environments. It achieves low cycle times and stable performance across different workload conditions, outperforming existing methods through its strong adaptability to evolving business processes and increasing complexity.

有效的资源分配对于优化业务流程至关重要。然而，大多数现有方法只关注单进程优化，忽略了多进程环境中存在的相互依赖性。这种限制导致资源分配效率低下，并对可伸缩性构成挑战。为了解决这一差距，我们提出了MuProMAC（多进程多代理协调），这是一种新的基于强化学习的方法，旨在优化多个相互依赖的业务流程之间的资源分配。与先前的方法不同，MuProMAC是第一个在线资源分配方法，它显式地建模进程之间的相互依赖关系，并动态平衡竞争资源需求，以最小化全局平均周期时间。我们在五个具有不同资源争用水平的多进程场景中评估了我们的方法，并将其与最先进的在线资源分配方法和三个简单的基线进行了比较。我们的结果表明，在共享资源环境中，MuProMAC始终是性能最好的方法之一。它在不同的工作负载条件下实现了低周期时间和稳定的性能，通过对不断发展的业务流程和不断增加的复杂性的强适应性，优于现有的方法。

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引用次数: 0

Reflection on compliance monitoring in business processes: Functionalities, application, and tool-support 对业务流程中的遵从性监视的反思：功能、应用程序和工具支持

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-12-04 DOI: 10.1016/j.is.2025.102650

Linh Thao Ly , Fabrizio Maria Maggi , Marco Montali , Stefanie Rinderle-Ma , Wil M.P. van der Aalst

Together with Information Systems, we celebrate the journal’s 50th anniversary and the 10th anniversary of our joint work on a systematic framework for compliance monitoring functionalities.

我们与《信息系统》杂志一起庆祝该杂志创刊50周年，以及我们就合规监测功能的系统框架共同开展工作10周年。

引用次数: 0

Reflection on the convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT) 人工智能驱动的物联网中边缘、雾、云的融合与互动思考

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-12-03 DOI: 10.1016/j.is.2025.102662

Farshad Firouzi , Bahar Farahani , Alexander Marinšek

As the Information Systems Journal celebrates its 50th Anniversary, we are honored to reflect on the journey and legacy of our 2022 article, “The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT)”. The paper introduced a unified architectural framework that advanced the integration of computing, intelligence, and connectivity across the edge–fog–cloud continuum, establishing a foundational model for scalable, adaptive, context-aware, and trustworthy AI-enabled systems. This reflection highlights how the work has shaped our research trajectories, influenced developments within the broader scientific community, and guided innovation, education, and industrial practice.

在《信息系统杂志》庆祝创刊50周年之际，我们很荣幸地回顾我们2022年的文章《人工智能驱动的物联网（IoT）中边缘、雾和云的融合和相互作用》的历程和遗产。本文介绍了一个统一的架构框架，该框架推进了跨边缘雾云连续体的计算、智能和连接的集成，为可扩展、自适应、上下文感知和可信赖的人工智能支持系统建立了一个基础模型。这种反思强调了这项工作如何塑造了我们的研究轨迹，影响了更广泛的科学界的发展，并指导了创新、教育和工业实践。

引用次数: 0

Exploring cultural commonsense in multilingual large language models: A survey 探索多语言大语言模型中的文化常识：综述

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-12-01 DOI: 10.1016/j.is.2025.102649

Geleta Negasa Binegde, Huaping Zhang

Large language models (LLMs) have demonstrated impressive proficiency in multilingual natural language processing (NLP), yet they frequently struggle with cultural commonsense—the implicit knowledge shaped by societal norms, traditions, and shared experiences. As these models are deployed in diverse linguistic and cultural settings, their ability to understand and apply cultural commonsense becomes crucial for ensuring fairness, inclusivity, and contextual accuracy. This paper presents a systematic review and a large-scale empirical benchmark for evaluating cultural commonsense in multilingual LLMs. Through a comprehensive evaluation of 15 models on the BLEnD dataset, our analysis reveals a critical performance gap of 64.2% between high-resource and low-resource cultures. The results demonstrate significant disparities across model architectures: encoder-only models show more consistent but lower overall performance compared to decoder-based models. We identify key limitations, including data scarcity, representational bias, and inadequate cross-lingual knowledge transfer. Finally, we propose future research directions, such as culturally diverse dataset curation, hybrid knowledge graph architectures, and fairness-aware fine-tuning. The primary contributions of this work are (1) a systematic review of challenges and mitigation strategies for cultural commonsense; (2) a large-scale empirical benchmark that evaluates 15 multilingual LLMs across 13 languages and 16 countries, revealing significant performance disparities; and (3) concrete findings on the effects of model architecture and the limitations of scale in cultural understanding. This research underscores the urgent need to advance cultural commonsense in multilingual LLMs to ensure the development of fair, inclusive, and contextually accurate AI systems globally.

大型语言模型（llm）在多语言自然语言处理（NLP）方面表现出了令人印象深刻的熟练程度，但它们经常与文化常识（由社会规范、传统和共享经验形成的隐性知识）作斗争。由于这些模型被部署在不同的语言和文化环境中，它们理解和应用文化常识的能力对于确保公平性、包容性和上下文准确性至关重要。本文提出了一个系统的审查和大规模的经验基准评估文化常识在多语言法学硕士。通过对BLEnD数据集上的15个模型进行综合评估，我们的分析显示，高资源文化与低资源文化之间的关键绩效差距为64.2%。结果显示了模型架构之间的显著差异：与基于解码器的模型相比，只有编码器的模型显示出更一致但更低的整体性能。我们确定了关键的限制，包括数据稀缺、代表性偏见和跨语言知识转移不足。最后，我们提出了未来的研究方向，如多元文化数据集管理、混合知识图谱架构和公平感知微调。这项工作的主要贡献是：(1)对文化常识的挑战和缓解策略进行了系统的回顾；(2)对16个国家、13种语言的15名多语种法学硕士进行了大规模的实证基准评估，结果显示出显著的绩效差异；(3)关于模型建筑的作用和尺度在文化理解中的局限性的具体发现。这项研究强调了迫切需要在多语言法学硕士中推进文化常识，以确保在全球范围内开发公平、包容和准确的人工智能系统。

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引用次数: 0

IPDRM: A pyramid-based diffusion and contrastive learning framework for sequential recommendation IPDRM：一个基于金字塔的序列推荐扩散和对比学习框架

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-29 DOI: 10.1016/j.is.2025.102651

Ruijia Guo, Zhiyuan Chen

Sequential recommendation faces critical challenges in handling data sparsity, noise interference, and ineffective intent modeling. To address these issues, this paper proposes a novel Intent-aware Pyramid Diffusion Recommendation Model (IPDRM) that integrates hierarchical intent modeling with conditional diffusion-based augmentation. The framework employs a pyramid structure to capture multi-granular user intents (base-level item features, mid-level temporal patterns, and top-level semantic abstractions) and utilizes intent-conditioned diffusion to generate semantically consistent augmented views. Contrastive learning is then applied to align representations of original and augmented sequences. Extensive experiments on Tmall and Fliggy datasets demonstrate that IPDRM significantly outperforms state-of-the-art baselines, achieving improvements of up to 20.0 % in HR@5 and 22.5 % in NDCG@5. The model exhibits strong robustness in sparse and noisy scenarios, validated through comprehensive ablation studies and parameter sensitivity analyses. This work provides a effective solution for intent-aware sequential recommendation with both theoretical and practical contributions. The code for the paper is available at https://github.com/CLTCGUO/IPDRM.

顺序推荐在处理数据稀疏性、噪声干扰和无效的意图建模方面面临着严峻的挑战。为了解决这些问题，本文提出了一种新的意图感知金字塔扩散推荐模型（IPDRM），该模型将分层意图建模与基于条件扩散的增强相结合。该框架采用金字塔结构来捕获多粒度的用户意图（基本级项目特征、中级时间模式和顶级语义抽象），并利用意图条件扩散来生成语义一致的增强视图。然后应用对比学习来对齐原始序列和增强序列的表示。在天猫和Fliggy数据集上进行的大量实验表明，IPDRM显著优于最先进的基线，在HR@5和NDCG@5分别实现了20.0 %和22.5 %的改进。通过综合烧蚀研究和参数敏感性分析，该模型在稀疏和噪声情况下表现出较强的鲁棒性。本研究为意向感知顺序推荐提供了一种有效的解决方案，具有理论和实践意义。该论文的代码可在https://github.com/CLTCGUO/IPDRM上获得。

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引用次数: 0

Local intrinsic dimensionality and the estimation of convergence order 局部固有维数与收敛阶的估计

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-21 DOI: 10.1016/j.is.2025.102648

Michael E. Houle, Vincent Oria, Hamideh Sabaei

Fixed-point iteration (FPI) is a crucially important technique at the foundation of many scientific and engineering fields, such as numerical analysis, dynamical systems, optimization, and machine learning. In these domains, algorithmic efficiency and stability is often assessed using the notion of convergence order, a quantity whose estimation has typically involved line fitting in log–log space, or finding the limit of an associated function on differences of sequence values. In this paper, we establish a precise equivalence between the convergence order of a fixed-point update function and the local intrinsic dimensionality (LID) of that function once its fixed point is translated to the origin. Building on this insight, we propose a unified framework for re-purposing existing distributional estimators of LID to estimate the convergence order. Of the LID estimators considered, we show that two, the MLE (Hill) estimator and a Bayesian estimator, have practical and convenient closed-form expressions. We further investigate how these estimators of convergence order can be enhanced using Aitken’s

Δ^{2}

method for accelerating convergence in slow scenarios, as well as a Bayesian smoothing layer for reducing variance when the number of samples is small. Empirically, we benchmark our LID-based estimators against classical sequenced-based and curve-fitting methods in three experimental settings: root-finding, general iteration, and machine learning regression. Results indicate that our approaches frequently match or surpass the classical estimators in accuracy, while offering robust performance over a broader range of convergence scenarios.

不动点迭代（FPI）是许多科学和工程领域至关重要的基础技术，如数值分析，动力系统，优化和机器学习。在这些领域中，算法的效率和稳定性通常使用收敛阶的概念来评估，收敛阶的估计通常涉及对数-对数空间中的线拟合，或者在序列值的差异上找到相关函数的极限。本文建立了一个不动点更新函数的收敛阶与该函数的局部固有维数（LID）之间的精确等价。基于这一见解，我们提出了一个统一的框架，用于重新利用现有的LID分布估计器来估计收敛阶。在所考虑的LID估计量中，我们证明了两个估计量，即MLE （Hill）估计量和贝叶斯估计量，具有实用和方便的封闭形式表达式。我们进一步研究了如何使用Aitken的Δ2方法在缓慢场景下加速收敛，以及在样本数量较少时减少方差的贝叶斯平滑层来增强这些收敛顺序的估计量。在经验上，我们将基于lid的估计器与经典的基于序列和曲线拟合方法在三种实验设置中进行了基准测试：寻根、一般迭代和机器学习回归。结果表明，我们的方法在精度上经常匹配或超过经典估计器，同时在更广泛的收敛场景下提供稳健的性能。

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引用次数: 0

Design patterns for GDPR-aware process modeling in BPMN BPMN中gpr感知流程建模的设计模式

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-14 DOI: 10.1016/j.is.2025.102646

Simone Agostinelli , Francesca De Luzi , Fabrizio Maria Maggi , Andrea Marrella , Alessia Volpi

In an increasingly digital world, collecting, processing, and exchanging personal data are critical drivers for enacting enterprise business processes. However, the long-term retention and access of personal data expose organizations to data breaches, in which sensitive and protected data are disclosed and exploited unauthorizedly. To mitigate the damage that data breaches can cause, in the European Union (EU), the right to data privacy is enforced through the General Data Protection Regulation (GDPR), which defines how organizations must store and manage EU citizens’ data. GDPR is highly influencing how organizations approach data privacy, forcing them to rethink and upgrade their business processes to become GDPR compliant, which can be daunting. In this paper, in line with the privacy-by-design principles of GDPR, we propose a methodology that shows how to capture the main privacy GDPR constraints in the form of design patterns and integrate them into business process models specified in BPMN (Business Process Model and Notation). This allows us to achieve full transparency of privacy constraints in business processes, making it possible to ensure their compliance with GDPR at design-time. We adopt a design science research approach to present our methodology and make design decisions explicit. We also introduce GDPR-Pilot, a BPMN editor that assists process designers and Data Controllers in integrating GDPR patterns into existing models. The methodology is evaluated through real-world use cases against structural, usage, and environmental requirements.

在日益数字化的世界中，收集、处理和交换个人数据是制定企业业务流程的关键驱动因素。然而，长期保留和访问个人数据会使组织面临数据泄露的风险，在这种情况下，敏感和受保护的数据会被未经授权地披露和利用。为了减轻数据泄露可能造成的损害，在欧盟（EU），数据隐私权通过《通用数据保护条例》（GDPR）得到执行，该条例规定了组织必须如何存储和管理欧盟公民的数据。GDPR正在高度影响组织处理数据隐私的方式，迫使他们重新思考和升级业务流程，以符合GDPR的要求，这可能令人生畏。在本文中，根据GDPR的设计隐私原则，我们提出了一种方法，该方法展示了如何以设计模式的形式捕获主要的隐私GDPR约束，并将其集成到BPMN（业务流程模型和符号）中指定的业务流程模型中。这使我们能够在业务流程中实现完全透明的隐私约束，从而有可能确保在设计时符合GDPR。我们采用设计科学的研究方法来展示我们的方法，并使设计决策明确。我们还介绍GDPR- pilot，这是一个BPMN编辑器，可帮助流程设计人员和数据控制器将GDPR模式集成到现有模型中。该方法通过实际用例对结构、使用和环境需求进行评估。

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引用次数: 0

New compressed indices for multijoins on graph databases 新的压缩索引在图数据库上的多连接

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-13 DOI: 10.1016/j.is.2025.102647

Diego Arroyuelo , Fabrizio Barisione , Antonio Fariña , Adrián Gómez-Brandón , Gonzalo Navarro

A recent surprising result in the implementation of worst-case-optimal (wco) multijoins in graph databases (specifically, basic graph patterns) is that they can be supported on graph representations that take even less space than a plain representation, and orders of magnitude less space than classical indices, while offering comparable performance. In this paper we uncover a wide set of new wco space–time tradeoffs: we (1) introduce new compact indices that handle multijoins in wco time, and (2) combine them with new query resolution strategies that offer better times in practice. As a result, we improve the average query times of current compact representations by a factor of up to 13 to produce the first 1000 results, and using twice their space, reduce their total average query time by a factor of 2. Our experiments suggest that there is more room for improvement in terms of generating better query plans for multijoins.

最近在图数据库（特别是基本图模式）中实现最坏情况最优（wco）多连接的一个令人惊讶的结果是，它们可以在比普通表示占用更少空间的图表示上得到支持，并且比经典索引占用的空间少几个数量级，同时提供相当的性能。在本文中，我们揭示了一系列新的wco时空权衡：我们(1)引入了新的紧凑索引，在wco时间内处理多连接；(2)将它们与新的查询解析策略结合起来，在实践中提供更好的时间。因此，我们将当前压缩表示的平均查询时间提高了13倍，以生成前1000个结果，并且使用两倍的空间，将它们的总平均查询时间减少了2倍。我们的实验表明，在为多连接生成更好的查询计划方面还有更多的改进空间。

引用次数: 0

The many facets of fairness in recommender systems: Consumers, providers and items 推荐系统公平性的许多方面：消费者、供应商和商品

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-12 DOI: 10.1016/j.is.2025.102643

Reza Shafiloo, Maria Stratigi, Jaakko Peltonen, Thomas Olsson, Kostas Stefanidis

Autonomous decision-making systems, particularly recommender systems, have received increasing attention concerning fairness, i.e., if all stakeholders affected by such a system are treated equally as a result of the recommendations. Existing approaches primarily focus on fairness between two stakeholders – consumers and providers or consumers and items – treating providers and items as the same entity. However, we argue for the treatment of providers and items as distinct stakeholders to offer more comprehensive models of fairness in recommender systems. To this end, we propose a fairness-aware recommender system, CIPFRS, designed to optimize fairness across all three key stakeholders: consumers, providers, and items. We examine consumer fairness regarding their level of interaction with the system; high and low-activity users should be treated equally. Further, all providers should have an equal opportunity for their products to be recommended. Finally, we propose an approach to implement item fairness in each provider’s inventory. We report an extensive evaluation of the proposed solution through three datasets, demonstrating that considering all three stakeholders yields improved recommendations while minimizing bias.

自主决策系统，特别是推荐系统，在公平性方面受到越来越多的关注，即受这种系统影响的所有利益相关者是否因建议而得到平等对待。现有方法主要关注两个利益相关者（消费者和提供者或消费者和物品）之间的公平性，将提供者和物品视为同一实体。然而，我们主张将提供者和项目作为不同的利益相关者来处理，以提供推荐系统中更全面的公平模型。为此，我们提出了一个公平感知的推荐系统，CIPFRS，旨在优化所有三个关键利益相关者：消费者、供应商和物品的公平性。我们根据消费者与系统的互动程度来检验他们的公平性；应该平等对待活跃度高和低的用户。此外，所有供应商都应该有平等的机会推荐他们的产品。最后，我们提出了一种在每个供应商的库存中实现物品公平的方法。我们通过三个数据集对提议的解决方案进行了广泛的评估，证明考虑所有三个利益相关者可以在最大限度地减少偏见的同时改进建议。

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引用次数: 0

Reproducible experiments on visual exploration framework of geospatial vector big data 地理空间矢量大数据可视化探索框架的可重复性实验

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-11 DOI: 10.1016/j.is.2025.102640

Zebang Liu , Anran Yang , Mengyu Ma , Luo Chen , Jiali Zhou , Ning Jing , Jichong Yin , Pranav Kasela , Raúl Martín-Santamaría

This work is a companion reproducibility paper that presents a framework to reproduce our previous experiments and results reported in Liu et al. (2024). In that previous paper, we proposed an efficient visual exploration approach of geospatial vector big data on the web map, which designs the display-driven visualization model and combines the traditional data-driven visualization model to realize the interactive real-time map visualization. Due to the lack of reproducible and extensible implementations of methods, our work introduces a comprehensive reproducibility framework to publicly release our source code, datasets, runtime environment, experiments, and software tools. We provide detailed reproducibility protocols for both the experiments and the software tool. After downloading the dataset and deploying the environment setups, our primary work (Liu et al., 2024) can be successfully reproduced, and the on-site visual exploration for user-provided datasets on the web map can be demonstrated by running a series of execution scripts of the experiments and the software tool. The reproducibility protocol can be created and tested in both Ubuntu machines and Docker containers. Moreover, we introduce and discuss new experimental results by running the reproducibility protocol introduced herein, our work can be considered weakly reproducible, since we were able to validate the ability of our work to interact in real-time and outperform the existing methods, leading us to the same conclusions.

这项工作是一篇可重复性的论文，它提出了一个框架来重现我们之前的实验和Liu等人（2024）报告的结果。在之前的文章中，我们提出了一种高效的web地图地理空间矢量大数据可视化探索方法，设计了显示驱动的可视化模型，并结合传统的数据驱动可视化模型，实现交互式实时地图可视化。由于缺乏可复制和可扩展的方法实现，我们的工作引入了一个全面的可复制框架，以公开发布我们的源代码、数据集、运行时环境、实验和软件工具。我们为实验和软件工具提供了详细的再现性协议。下载数据集并部署环境设置后，我们的主要工作（Liu et al., 2024）可以成功复制，并且可以通过运行一系列实验的执行脚本和软件工具来演示web地图上用户提供的数据集的现场可视化探索。可重复性协议可以在Ubuntu机器和Docker容器中创建和测试。此外，我们通过运行本文介绍的可重复性协议引入并讨论了新的实验结果，我们的工作可以被认为是弱可重复性的，因为我们能够验证我们的工作实时交互的能力，并且优于现有的方法，从而导致我们得出相同的结论。

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引用次数: 0