Information Systems最新文献

英文中文

BBQ-Tree: A unified classifier and regressor combining Boolean and quantum logic decisions BBQ-Tree：一个统一的分类器和回归器，结合了布尔和量子逻辑决策

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

Information Systems

Pub Date : 2025-09-29 DOI: 10.1016/j.is.2025.102632

Alexander Stahl, Ingo Schmitt

This article provides a detailed explanation of the BBQ-Tree, a unified logic-based model that integrates both classical Decision Trees and Quantum-Logic Decision Trees into a generalized framework for classification and regression. As it combines these paradigms, the BBQ-Tree effectively addresses problems with both linear and curved decision boundaries while prioritizing interpretability. We provide a detailed description of the underlying concepts, a possible training algorithm, experimental evaluations and the incorporation of regression functionality, broadening its applicability beyond classification tasks. Strategies for efficient training and model optimization are also presented. Experimental results demonstrate that the BBQ-Tree produces compact, interpretable models capable of revealing data trends, while achieving accuracy comparable to Decision Trees. Furthermore, its new regression capabilities highlight its versatility and performance across a wider range of tasks.

bbq树是一种统一的基于逻辑的模型，它将经典决策树和量子逻辑决策树集成到一个通用的分类和回归框架中。由于它结合了这些范例，BBQ-Tree在优先考虑可解释性的同时，有效地解决了线性和弯曲决策边界的问题。我们提供了基本概念的详细描述，一种可能的训练算法，实验评估和回归功能的结合，将其适用性扩展到分类任务之外。提出了有效训练和模型优化的策略。实验结果表明，BBQ-Tree产生了紧凑、可解释的模型，能够揭示数据趋势，同时达到与决策树相当的准确性。此外，它的新回归功能突出了它在更广泛的任务范围内的通用性和性能。

引用次数: 0

In system alignments we trust! Explainable alignments via projections 在系统校准中，我们信任！可解释的排列通过投影

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

Information Systems

Pub Date : 2025-09-25 DOI: 10.1016/j.is.2025.102631

Dominique Sommers, Natalia Sidorova, Boudewijn van Dongen

Alignments are a well-known process mining technique for reconciling system logs and normative process models. Evidence of certain behaviors in a real system may only be present in one representation – either a log or a model – but not in the other. Since processes involve multiple entities, such as objects and resources performing different tasks with objects, the interaction of these entities must be taken into account in the alignments. Additionally, both logged and modeled representations of reality may be imprecise and only partially represent some of these entities, but not all. In this paper, we introduce the concept of “relaxations” through projections for alignments to deal with partially correct models and logs. Relaxed alignments help to distinguish between trustworthy and untrustworthy content of the two representations (the log and the model) to achieve a better understanding of the underlying process and expose quality issues.

校准是一种众所周知的过程挖掘技术，用于协调系统日志和规范过程模型。真实系统中某些行为的证据可能只存在于一种表示中——日志或模型——而不存在于另一种表示中。由于流程涉及多个实体，例如使用对象执行不同任务的对象和资源，因此必须在对齐中考虑这些实体的交互。此外，记录和建模的现实表示都可能是不精确的，并且只能部分地表示其中的一些实体，而不是全部。在本文中，我们通过投影引入“松弛”的概念来处理部分正确的模型和日志。宽松的对齐有助于区分两种表示（日志和模型）的可信和不可信内容，从而更好地理解底层流程并暴露质量问题。

引用次数: 0

Evaluating the lifecycle economics of AI: The levelized cost of artificial intelligence (LCOAI) 评估人工智能的生命周期经济学：人工智能的平均成本（LCOAI）

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

Information Systems

Pub Date : 2025-09-24 DOI: 10.1016/j.is.2025.102634

Eliseo Curcio

As artificial intelligence (AI) becomes foundational to enterprise infrastructure, organizations face growing challenges in accurately assessing the full economic implications of AI deployment. Existing metrics such as API token costs, GPU-hour billing, or Total Cost of Ownership (TCO) fail to capture the complete lifecycle costs of AI systems and provide limited comparability across deployment models. This paper introduces the Levelized Cost of Artificial Intelligence (LCOAI), a standardized economic metric designed to quantify the total capital (CAPEX) and operational (OPEX) expenditures per unit of productive AI output, normalized by valid inference volume. Analogous to established metrics like the Levelized Cost of Electricity (LCOE) and the Levelized Cost of Hydrogen (LCOH) in the energy sector, LCOAI provides a rigorous, transparent framework for evaluating and comparing AI deployment strategies. We define the LCOAI methodology in detail and apply it to four representative scenarios OpenAI GPT-4.1 API, Anthropic Claude Haiku API, a self-hosted LLaMA-2–13B deployment, and a cloud-hosted LLaMA-2–13B deployment demonstrating how LCOAI captures critical trade-offs in scalability, investment planning, and cost optimization. Extensive sensitivity analyses further explore the impact of inference volume, CAPEX, and OPEX variability on lifecycle economics. The results illustrate the practical utility of LCOAI in procurement, infrastructure planning, and automation strategy, and establish it as a foundational benchmark for AI economic analysis. Policy implications and directions for future refinement, including integration of environmental and performance-adjusted cost metrics, are also discussed.

随着人工智能（AI）成为企业基础设施的基础，企业在准确评估AI部署的全部经济影响方面面临着越来越大的挑战。现有的指标，如API令牌成本、gpu小时计费或总拥有成本（TCO），无法捕捉人工智能系统的完整生命周期成本，并且在部署模型之间提供有限的可比性。本文介绍了人工智能的平准化成本（LCOAI），这是一种标准化的经济指标，旨在量化每单位生产性人工智能产出的总资本（CAPEX）和运营（OPEX）支出，并通过有效推理量进行规范化。与能源领域的平准化电力成本（LCOE）和氢平准化成本（LCOH）等既定指标类似，LCOAI为评估和比较人工智能部署策略提供了一个严格、透明的框架。我们详细定义了LCOAI方法，并将其应用于四个代表性场景：OpenAI GPT-4.1 API、Anthropic Claude Haiku API、自托管LLaMA-2-13B部署和云托管LLaMA-2-13B部署，展示了LCOAI如何在可扩展性、投资规划和成本优化方面实现关键权衡。广泛的敏感性分析进一步探讨了推理量、CAPEX和OPEX可变性对生命周期经济学的影响。结果说明了LCOAI在采购、基础设施规划和自动化战略中的实际效用，并将其建立为人工智能经济分析的基础基准。还讨论了未来改进的政策影响和方向，包括环境和绩效调整成本指标的整合。

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引用次数: 0

SaaMS: The synopses-as-a-microservice paradigm for scalable adaptive streaming analytics across the cloud to edge continuum SaaMS：概要即微服务范例，用于从云到边缘连续体的可扩展自适应流分析

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

Information Systems

Pub Date : 2025-09-23 DOI: 10.1016/j.is.2025.102629

Georgios Panagiotis Kalfakis, Nikos Giatrakos

The use of data synopses in Big streaming Data analytics can offer 3 types of scalability: (i) horizontal scalability, for scaling with the volume and velocity of Big streaming Data, (ii) vertical scalability, for scaling with the number of processed streams, and (iii) federated scalability, i.e. reducing the communication cost for performing global analytics across a number of geo-distributed data centers or devices in IoT settings. Despite the aforementioned virtues of synopses, no state-of-the-art Big Data framework or IoT platform provides a native API for stream synopses supporting all three types of required scalability. In this work, we fill this gap by introducing a novel system and architectural paradigm, namely Synopses-as-a-MicroService (SaaMS), for both parallel and geo-distributed stream summarization at scale. SaaMS is developed on Apache Kafka and Kafka Streams and can provide all the required types of scalability together with (i) the ability to seamlessly perform adaptive resource allocation with zero downtime for the running analytics and (ii) the ability to run both across powerful computer clusters and Java-enabled IoT devices. Therefore, SaaMS is directly deployable from applications that either operate on powerful clouds or across the cloud to edge continuum.

在大流数据分析中使用数据概要可以提供3种类型的可扩展性：(i)水平可扩展性，用于根据大流数据的数量和速度进行扩展；（ii）垂直可扩展性，用于根据处理流的数量进行扩展；（iii）联合可扩展性，即减少跨多个地理分布式数据中心或物联网设置中的设备执行全球分析的通信成本。尽管有上述概要的优点，但没有最先进的大数据框架或物联网平台为流概要提供原生API，支持所有三种类型所需的可扩展性。在这项工作中，我们通过引入一种新的系统和架构范例来填补这一空白，即概要即微服务（SaaMS），用于大规模并行和地理分布式流汇总。SaaMS是在Apache Kafka和Kafka Streams上开发的，可以提供所有所需类型的可扩展性，以及(i)无缝执行自适应资源分配的能力，零停机时间，以及（ii）跨强大的计算机集群和支持java的物联网设备运行的能力。因此，可以从运行在强大的云上或跨云到边缘连续体的应用程序直接部署sam。

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引用次数: 0

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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-09-21 DOI: 10.1016/j.is.2025.102630

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

To celebrate the 50th Anniversary of the Information Systems Journal, we are delighted to share our research reflections on the article “Community-diversified influence maximization in social networks” published at Information Systems in 2020. Our reflections will highlight the impact of this article on the authors’ research trajectories, its influence on the broader research community, and its contributions to industry practice.

为庆祝《信息系统》杂志创刊50周年，我们很高兴在此分享我们对《信息系统》杂志2020年发表的文章《社区多元化影响最大化》的研究思考。我们的反思将突出本文对作者研究轨迹的影响，对更广泛的研究界的影响，以及对行业实践的贡献。

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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-09-20 DOI: 10.1016/j.is.2025.102626

Rik Eshuis , Aditya Ghose

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.

知识密集型流程朝着业务目标的实现迈进。这些流程通常依赖于数据来实现数据驱动的决策，但也需要很大的灵活性来处理它们所处的复杂和动态环境。因此，已经提出了以声明性数据为中心的流程建模语言，如案例管理模型和表示法（CMMN）来对知识密集型流程进行建模。然而，尽管这样的流程模型允许表达目标，但它们只是隐式地指定了目标之间的依赖关系。这使得声明性数据中心流程模型的面向目标行为难以理解，从而混淆了知识密集型流程的面向目标行为。本文定义了一种结构化、半自动化的方法来解释声明性数据中心流程模型的面向目标方面。该方法首先从声明性的以数据为中心的流程模型中派生目标关系，然后使用算法将这些目标关系综合到目标模型中。该方法得到了一个工具的支持，并在案例研究中进行了评估。使用该方法，声明性数据中心流程模型中的隐式目标依赖关系在目标模型中表示。这支持理解声明性数据中心流程模型的面向目标方面。

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引用次数: 0

GPT-5 and open-weight large language models: Advances in reasoning, transparency, and control GPT-5和开放权重大型语言模型：推理、透明度和控制方面的进展

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

Information Systems

Pub Date : 2025-09-18 DOI: 10.1016/j.is.2025.102620

Maikel Leon

The rapid evolution of Generative Pre-trained Transformers (GPTs) has revolutionized natural language processing, enabling models to generate coherent text, solve mathematical problems, write code, and even reason about complex tasks. This paper presents a scientific review of GPT-5, OpenAI’s latest flagship model, and examines its innovations in comparison to previous generations of GPT. We summarize the model’s architecture and features, including hierarchical routing, expanded context windows, and enhanced tool-use capabilities, and survey empirical evidence of improved performance on academic benchmarks. A dedicated section discusses the release of open-weight mixture-of-experts models (GPT-OSS), describing their technical design, licensing, and comparative performance. Our analysis synthesizes findings from recent literature on long-context evaluation, cognitive biases, medical summarization, and hallucination vulnerability, highlighting where GPT-5 advances the state of the art and where challenges remain. We conclude by discussing the implications of open-weight models for transparency and reproducibility and propose directions for future research on evaluation, safety, and agentic behavior.

生成预训练变形器（gpt）的快速发展彻底改变了自然语言处理，使模型能够生成连贯的文本，解决数学问题，编写代码，甚至对复杂任务进行推理。本文对OpenAI最新旗舰模型GPT-5进行了科学回顾，并将其与前几代GPT进行了比较。我们总结了模型的架构和特征，包括分层路由、扩展的上下文窗口和增强的工具使用能力，并调查了在学术基准上改进性能的经验证据。专门的一节讨论了开放式专家混合模型（GPT-OSS）的发布，描述了它们的技术设计、许可和比较性能。我们的分析综合了近期文献中关于长期情境评估、认知偏差、医学总结和幻觉脆弱性的发现，突出了GPT-5在哪些方面取得了进展，哪些方面仍存在挑战。最后，我们讨论了开重模型对透明度和可重复性的影响，并提出了评估、安全性和代理行为的未来研究方向。

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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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-09-18 DOI: 10.1016/j.is.2025.102627

Radek Ošlejšek, Radoslav Chudovský, Martin Macak

Hands-on training sessions become a standard way to develop and increase knowledge in cybersecurity. As practical cybersecurity exercises are strongly process-oriented with knowledge-intensive processes, process mining techniques and models can help enhance learning analytics tools. The design of our open-source analytical dashboard is backed by guidelines for visualizing multivariate networks complemented with temporal views and clustering. The design aligns with the requirements for post-training analysis of a special subset of cybersecurity exercises — supervised Capture the Flag games. Usability is demonstrated in a case study using trainees’ engagement measurement to reveal potential flaws in training design or organization.

实践培训课程成为开发和增加网络安全知识的标准方式。由于实际的网络安全演习是具有知识密集型过程的强烈过程导向的，过程挖掘技术和模型可以帮助增强学习分析工具。我们的开源分析仪表板的设计是由多变量网络可视化的指导方针支持的，并辅以时间视图和聚类。该设计符合网络安全演习的一个特殊子集的训练后分析要求-监督夺旗游戏。可用性在一个案例研究中得到证明，该案例研究使用受训人员的参与测量来揭示培训设计或组织中的潜在缺陷。

引用次数: 0

Predicting multidimensional cubes through intentional analytics 通过有意分析预测多维数据集

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

Information Systems

Pub Date : 2025-09-17 DOI: 10.1016/j.is.2025.102628

Matteo Francia , Stefano Rizzi , Matteo Golfarelli , Patrick Marcel

In an attempt to streamline exploratory data analysis of multidimensional cubes, the Intentional Analytics Model ha been proposed as a way to unite OLAP and analytics by allowing users to indicate their analysis intentions and returning cubes enhanced with models. Five intention operators were envisioned to this end; in this work we focus on the predict operator, whose goal is to estimate the missing values of a cube measure starting from known values of the same measure or other measures using different regression models. Although prediction tasks such as forecasting and imputation are routinary for analysts, the added value of our approach is (i) to encapsulate them in a declarative, concise, natural language-like syntax; (ii) to automate the selection of the best measures to be used and the computation of the models, and (iii) to automate the evaluation of the interest of the models computed. First we propose a syntax and a semantics for predict and discuss how enhanced cubes are built by (i) predicting the missing values for a measure based on the available information via one or more models and (ii) highlighting the most interesting prediction. Then we test the operator implementation, proving that its performance is in line with the interactivity requirement of OLAP session and that accurate predictions can be returned.

为了简化多维数据集的探索性数据分析，有意分析模型被提出作为一种统一OLAP和分析的方法，允许用户表明他们的分析意图并返回经过模型增强的数据集。为此，设想了五个意图运营商；在这项工作中，我们专注于预测算子，其目标是从使用不同回归模型的相同度量或其他度量的已知值开始估计立方体度量的缺失值。虽然预测任务，如预测和imputation对分析师来说是常规的，但我们的方法的附加价值是(i)将它们封装在声明性的，简洁的，自然语言般的语法中；（ii）自动选择要使用的最佳度量和模型的计算，以及（iii）自动评估所计算的模型的利益。首先，我们提出了预测的语法和语义，并讨论了如何通过(i)通过一个或多个模型根据可用信息预测度量的缺失值以及（ii）突出显示最有趣的预测来构建增强多维数据集。然后对算子实现进行了测试，证明其性能符合OLAP会话的交互性要求，并能返回准确的预测结果。

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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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-09-13 DOI: 10.1016/j.is.2025.102625

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

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.

图对比学习（GCL）通过利用图神经网络（gnn）和自监督学习（SSL）来增强推荐系统。然而，现有的方法与数据稀疏性和噪声作斗争。我们提出稳健图对比学习（RoGCL），这是一个通过双视角生成器生成高质量对比视图的新框架。局部生成器采用变分图自编码器（VGAE）从用户-项目交互分布中采样来捕获微观层面的协作模式。全局生成器利用奇异值分解（SVD）重构宏观结构，同时通过低秩逼近滤波噪声。通过结合信息瓶颈（InfoBN）来最小化视图之间的冗余，RoGCL学习结合本地和全局协作信号的鲁棒表示。对Last进行了广泛的实验。FM、Yelp和BeerAdvocate数据集表明，RoGCL显著优于最先进的方法，包括自监督图学习（SGL）、神经协作学习（NCL）和自适应图对比学习（AdaGCL）。结果显示，与最佳基线相比，Recall@20提高了8.7%，NDCG@20提高了5.8%。值得注意的是，RoGCL表现出了出色的鲁棒性，在25%噪声注入的情况下保持了90%以上的相对性能，并且在稀疏用户组中显示了37.7%的改进，使其特别适合具有不完美数据的实际应用。

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