Information Systems最新文献

Measuring the decentralisation of DeFi development: An empirical analysis of contributor distribution in Lido 衡量DeFi发展的分散性：丽都贡献者分布的实证分析

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

Information Systems

Pub Date : 2026-07-01 Epub Date: 2026-01-29 DOI: 10.1016/j.is.2026.102695

Giuseppe Destefanis , Jiahua Xu , Silvia Bartolucci

Decentralised finance (DeFi) protocols often claim to implement decentralised governance via mechanisms such as decentralised autonomous organisations (DAOs), yet the structure of their development processes is rarely examined in detail. This study presents an in-depth case analysis of the development activity distribution in Lido, a prominent DeFi liquid staking protocol. We analyse 6741 human-generated GitHub actions recorded from September 2020 to February 2025. Using standard inequality metrics – Gini coefficient and Herfindahl–Hirschman Index – alongside contributors’ interaction network and core–periphery modelling, we find that development activity is highly concentrated. Overall, the weighted Gini coefficient reaches 0.82 and the most active contributor alone accounts for 24% of the total activity. Despite an even split between core and peripheral contributors, the core group accounts for 98.1% of all weighted development actions. The temporal analysis shows an increase in concentration over time, with the Gini coefficient rising from 0.686 in the bootstrap phase to 0.817 in the maturity phase. The contributors’ interaction network analysis reveals a hub-and-spoke structure with high centralisation in communication flows. While a case study of a single protocol, Lido represents a critical test of decentralisation claims given its prominence, maturity, and DAO governance structure. These findings demonstrate that open-source DeFi development can exhibit highly concentrated control patterns despite decentralised governance mechanisms, revealing a persistent gap between governance and operational decentralisation.

去中心化金融（DeFi）协议通常声称通过去中心化自治组织（dao）等机制实现去中心化治理，但其开发过程的结构很少得到详细研究。本研究对Lido的开发活动分布进行了深入的案例分析，Lido是一个著名的DeFi液体投注协议。我们分析了从2020年9月到2025年2月记录的6741个人类生成的GitHub行为。使用标准的不平等指标——基尼系数和赫芬达尔-赫希曼指数——以及贡献者的互动网络和核心-外围模型，我们发现发展活动高度集中。总体而言，加权基尼系数达到0.82，仅最活跃的贡献者就占总活跃度的24%。尽管核心贡献者和外围贡献者之间的比例相等，但核心群体占所有加权开发行动的98.1%。时间分析显示，随着时间的推移，浓度呈上升趋势，基尼系数从自举期的0.686上升到成熟期的0.817。作者的交互网络分析揭示了一种在通信流中具有高度集中化的轮辐结构。虽然Lido是单一协议的案例研究，但鉴于其突出性、成熟度和DAO治理结构，它代表了对去中心化主张的关键考验。这些发现表明，尽管分散的治理机制，开源的DeFi开发可以表现出高度集中的控制模式，揭示了治理和操作分散之间的持续差距。

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引用次数: 0

Deep learning approaches for handling noisy data in collaborative filtering: A survey 协同过滤中处理噪声数据的深度学习方法：综述

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

Information Systems

Pub Date : 2026-07-01 Epub Date: 2026-02-12 DOI: 10.1016/j.is.2026.102699

Ouahiba Belgacem , Boudjemaa Boudaa , Abderrahmane Kouadria , Abdelhafid Abouaissa

Collaborative filtering is a cornerstone technique in recommender systems, leveraging user–item interactions to predict preferences and suggest items. The sources of data for these systems can be explicit, where users rate items directly, such as ratings on a scale of 1 to 5, or implicit, where user preferences are inferred from behaviors such as purchases, clicks, time spent, and other activities. However, the effectiveness of these systems can be compromised by noisy data, which may arise from natural inconsistencies or intentional distortions. Addressing this issue, denoising process is crucial for enhancing the accuracy and reliability of recommendations. Recent developments in deep learning have introduced advanced methods for managing both natural and malicious noise in user feedback data. This survey paper provides an in-depth review of the latest deep learning-based techniques for denoising both explicit and implicit feedback. It analyzes the strengths and limitations of existing approaches to offer a comprehensive view for new researchers developing solutions in this area. Additionally, it identifies challenges and open issues that need to be addressed, proposing future research directions to advance this field further. To the best of our knowledge, this is the first survey to systematically address denoising for both types of feedback within a unified framework, highlighting the importance of robust denoising strategies in improving the performance of collaborative filtering systems.

协同过滤是推荐系统的基础技术，利用用户与项目的交互来预测偏好并推荐项目。这些系统的数据源可以是显式的，即用户直接对项目进行评级，例如从1到5的等级进行评级；也可以是隐式的，即从购买、点击、花费的时间和其他活动等行为推断用户偏好。然而，这些系统的有效性可能会受到噪声数据的影响，这些噪声数据可能来自自然不一致或故意扭曲。为了解决这一问题，去噪处理对于提高推荐的准确性和可靠性至关重要。深度学习的最新发展为管理用户反馈数据中的自然和恶意噪声引入了先进的方法。这篇调查论文对最新的基于深度学习的显式和隐式反馈去噪技术进行了深入的回顾。它分析了现有方法的优势和局限性，为在该领域开发解决方案的新研究人员提供了一个全面的观点。此外，它还确定了需要解决的挑战和开放问题，提出了未来的研究方向，以进一步推进这一领域。据我们所知，这是第一个在统一框架内系统地解决两种类型反馈的去噪问题的调查，强调了鲁棒去噪策略在提高协同过滤系统性能方面的重要性。

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引用次数: 0

Efficient data structures for fast and low-cost first-order logic rule mining 高效的数据结构，用于快速和低成本的一阶逻辑规则挖掘

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

Information Systems

Pub Date : 2026-07-01 Epub Date: 2026-01-21 DOI: 10.1016/j.is.2026.102690

Ruoyu Wang , Raymond Wong , Daniel Sun

Logic rule mining discovers association patterns in the form of logic rules from structured data. Logic rules are widely applied in information systems to assist decisions in an interpretable way. However, too many computational resources are required in state-of-the-art systems, as most of these systems optimize rule mining algorithms from the perspectives of algorithms and architecture, while data efficiency has been overlooked. Although some start-of-the-art systems implement customized data structures to improve mining speed, the space overhead of the data structures is unaffordable when processing large-scale knowledge bases. Therefore, in this article, we propose data structures to improve data efficiency and accelerate logic rule mining. Our techniques implicitly represent the Cartesian product of variable substitutions in logic rules and build compact indices for a logic entailment cache. Furthermore, we create a pool and a lookup table for the cache so that cache components will not be repeatedly created. The evaluation results show that over 95% of memory can be reduced by our techniques, and mining procedures have been accelerated by about 20x on average. Most importantly, mining on large-scale knowledge bases is practical on normal hardware where only one thread and 20GB of memory are sufficient even for large-scale knowledge bases.

逻辑规则挖掘是从结构化数据中以逻辑规则的形式发现关联模式。逻辑规则被广泛应用于信息系统中，以一种可解释的方式辅助决策。然而，目前最先进的系统大多从算法和体系结构的角度对规则挖掘算法进行优化，需要大量的计算资源，而忽略了数据效率。尽管一些最先进的系统实现了定制的数据结构来提高挖掘速度，但在处理大规模知识库时，数据结构的空间开销是无法承受的。因此，在本文中，我们提出了提高数据效率和加速逻辑规则挖掘的数据结构。我们的技术隐式地表示了逻辑规则中变量替换的笛卡尔积，并为逻辑蕴涵缓存构建了紧凑的索引。此外，我们还为缓存创建了一个池和一个查找表，这样就不会重复创建缓存组件。评估结果表明，我们的技术可以减少95%以上的内存，并且挖掘过程平均加快了20倍左右。最重要的是，在大型知识库上进行挖掘在普通硬件上是可行的，因为即使对于大型知识库，一个线程和20GB内存也足够了。

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引用次数: 0

HLR-SQL: Human-like reasoning for Text-to-SQL with the human in the loop HLR-SQL：类似于人的文本到sql的推理，人在循环中

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

Information Systems

Pub Date : 2026-06-01 Epub Date: 2026-01-02 DOI: 10.1016/j.is.2025.102670

Timo Eckmann , Matthias Urban , Jan-Micha Bodensohn , Carsten Binnig

Recent LLM-based approaches have achieved impressive results on Text-to-SQL benchmarks such as Spider and Bird. However, these benchmarks do not accurately reflect the complexity typically encountered in real-world enterprise scenarios, where queries often span multiple tables. In this paper, we introduce HLR-SQL, a new approach designed to handle such complex enterprise SQL queries. Unlike existing methods, HLR-SQL imitates Human-Like Reasoning with LLMs by incrementally composing queries through a sequence of intermediate steps, gradually building up to the full query. This is an extended version of Eckmann et al. (2025). The new contributions are centered around incorporating human feedback directly into the reasoning process of HLR-SQL. We evaluate HLR-SQL on a newly constructed benchmark, Spider-HJ, which systematically increases query complexity by splitting tables in the original Spider dataset to raise the average join count needed by queries. Our experiments show that state-of-the-art models experience up to a 70% drop in execution accuracy on Spider-HJ, while HLR-SQL achieves a 9.51% improvement over the best existing approaches on the Spider leaderboard. Finally, we extended HLR-SQL to incorporate human feedback directly into the reasoning process by allowing the LLM to selectively ask for human help when faced with ambiguity or execution errors. We demonstrate that including the human in the loop in this way yields significantly higher accuracy, particularly for complex queries.

最近基于llm的方法在诸如Spider和Bird之类的Text-to-SQL基准测试中取得了令人印象深刻的结果。然而，这些基准测试并不能准确地反映实际企业场景中通常遇到的复杂性，在实际企业场景中，查询通常跨越多个表。在本文中，我们介绍了HLR-SQL，一种用于处理此类复杂企业SQL查询的新方法。与现有方法不同，HLR-SQL通过一系列中间步骤逐步组合查询，逐步构建完整的查询，从而模仿llm的类人推理。这是Eckmann et al.（2025）的扩展版本。新的贡献集中在将人类反馈直接集成到HLR-SQL的推理过程中。我们在新构建的基准Spider- hj上评估了HLR-SQL，该基准通过拆分原始Spider数据集中的表来提高查询所需的平均连接计数，从而系统地增加了查询复杂性。我们的实验表明，最先进的模型在Spider- hj上的执行精度下降了70%，而HLR-SQL在Spider排行榜上比现有的最佳方法提高了9.51%。最后，我们扩展了HLR-SQL，允许LLM在遇到歧义或执行错误时选择性地寻求人工帮助，从而将人工反馈直接纳入推理过程。我们证明，以这种方式将人包含在循环中会产生更高的准确性，特别是对于复杂的查询。

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引用次数: 0

Improving the understandability of declarative process discovery results using easyDeclare 使用easyDeclare提高声明性过程发现结果的可理解性

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

Information Systems

Pub Date : 2026-06-01 Epub Date: 2025-12-22 DOI: 10.1016/j.is.2025.102667

Graziano Blasilli , Lauren S. Ferro , Simone Lenti , Fabrizio Maria Maggi , Andrea Marrella , Tiziana Catarci

Declarative process models allow us to capture the behavior of a business process through temporal constraints on the evolution of process activities. In process mining, declarative process discovery focuses on deriving these constraints from event logs. Although the semantic aspects of declarative processes have been extensively investigated, there has been less focus on designing declarative visual notations that enhance model understanding and support analysts in solving process mining tasks. To improve the human understandability of declarative process models, in this paper, we present easyDeclare, a novel visual notation to specify declarative process models using the Declare language. easyDeclare was developed with consideration of the well-established Moody’s design principles. We conducted extensive user experiments to demonstrate that easyDeclare, when compared with the original graphical representation of Declare, reduces the cognitive load required to interpret Declare models of increasing complexity, making it a promising alternative to enhancing overall comprehension of declarative process discovery tasks.

声明性流程模型允许我们通过流程活动演化的时间约束来捕获业务流程的行为。在流程挖掘中，声明性流程发现侧重于从事件日志中派生这些约束。尽管已经对声明性过程的语义方面进行了广泛的研究，但很少有人关注如何设计声明性可视化符号来增强模型理解并支持分析人员解决过程挖掘任务。为了提高人类对声明性过程模型的可理解性，本文提出了一种新的使用Declare语言来指定声明性过程模型的可视化符号easyDeclare。easyDeclare的开发考虑了穆迪完善的设计原则。我们进行了大量的用户实验来证明，与Declare的原始图形表示形式相比，easyDeclare减少了解释日益复杂的Declare模型所需的认知负荷，使其成为增强声明性过程发现任务的整体理解的有希望的替代方案。

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引用次数: 0

ACTER: Activity Customization through Timely and Explainable Recommendations ACTER：通过及时和可解释的建议定制活动

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

Information Systems

Pub Date : 2026-06-01 Epub Date: 2025-12-11 DOI: 10.1016/j.is.2025.102666

Anna Dalla Vecchia, Niccolò Marastoni, Barbara Oliboni, Elisa Quintarelli

The proliferation of sensors, including wearable devices, has significantly increased the volume of generated data, opening up new opportunities for personalized recommendations. This paper presents ACTER (Activity Customization through Timely and Explainable Recommendations), an integrated framework to provide contextual, timely, explainable, and user-specific recommendations. Thanks to the sequential rule mining algorithm ALBA (AgedLookBackApriori), we extract totally ordered sequential rules to uncover hidden insights from temporal data, ultimately improving a predefined target parameter related to the selected application domain. An aging mechanism is applied to ensure that recommendations remain relevant, giving more weight to newer information while still considering older data. In addition, our framework leverages historical data to also infer personalized, contextual information, allowing us to adapt the predefined context—usually set at the design stage—more dynamically and expressly. The experimental results of the ACTER evaluation confirm that integrating ad-hoc contexts mined from historical data into the recommender system yields more accurate suggestions.

包括可穿戴设备在内的传感器的激增大大增加了生成的数据量，为个性化推荐开辟了新的机会。本文介绍了ACTER（通过及时和可解释的建议进行活动定制），这是一个集成框架，用于提供上下文相关的、及时的、可解释的和特定于用户的建议。得益于顺序规则挖掘算法ALBA (AgedLookBackApriori)，我们提取了完全有序的顺序规则，以从时间数据中发现隐藏的见解，最终改进了与所选应用程序领域相关的预定义目标参数。使用老化机制来确保建议保持相关性，在考虑旧数据的同时给予新信息更多权重。此外，我们的框架还利用历史数据来推断个性化的上下文信息，从而允许我们更动态、更明确地调整预定义的上下文（通常在设计阶段设置）。ACTER评估的实验结果证实，将从历史数据中挖掘的临时上下文集成到推荐系统中可以产生更准确的建议。

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引用次数: 0

Visualizing repetition in process execution variants from partially ordered event data 从部分有序的事件数据中可视化流程执行变体中的重复

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

Information Systems

Pub Date : 2026-06-01 Epub Date: 2025-12-05 DOI: 10.1016/j.is.2025.102664

Ariba Siddiqui , Francesca Zerbato , Daniel Schuster

Operational processes often exhibit concurrency, where the execution of activities can overlap in time. Moreover, repetitions of activities, both intentional (e.g., iterative tasks) and unintentional (e.g., rework) often occur. Existing process mining techniques and visualizations largely assume sequential event data, making it difficult to analyze repetitions in partially ordered event data, which better captures real-world process behavior. We address this gap by introducing a novel arc-diagram-based visualization that highlights recurring activity patterns within individual process execution variants. This approach allows analysts to intuitively detect repetitions that are otherwise obscured in raw data or traditional variant views. We validate the usefulness and ease of use of the proposed visualization through a user study with process mining experts and provide an implementation of our contribution in an open-source tool, supporting practical adoption.

操作流程通常表现为并发性，其中活动的执行可以在时间上重叠。此外，活动的重复，有意的（例如，迭代任务）和无意的（例如，返工）经常发生。现有的流程挖掘技术和可视化在很大程度上假设事件数据是顺序的，这使得分析部分有序事件数据中的重复变得困难，而部分有序事件数据能够更好地捕捉真实的流程行为。我们通过引入一种新颖的基于弧线图的可视化来解决这一差距，该可视化突出了单个流程执行变体中重复出现的活动模式。这种方法允许分析人员直观地检测在原始数据或传统变体视图中被掩盖的重复。我们通过与过程挖掘专家的用户研究验证了所建议的可视化的有用性和易用性，并在开源工具中提供了我们的贡献的实现，支持实际采用。

引用次数: 0

From precision to perception: Human-in-the-loop evaluation of keyword extraction for internet-scale contextual advertising 从精确到感知：互联网规模上下文广告关键字提取的人在循环评估

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

Information Systems

Pub Date : 2026-06-01 Epub Date: 2025-12-11 DOI: 10.1016/j.is.2025.102665

Jingwen Cai , Sara Leckner , Johanna Björklund

Keyword extraction is a foundational task in natural language processing, underpinning countless real-world applications. One of these is contextual advertising, where keywords help predict the topical congruence between ads and their surrounding media contexts to enhance advertising effectiveness. Recent advances in artificial intelligence have improved keyword extraction capabilities but also introduced concerns about computational cost. Moreover, although the end-user experience is of vital importance, human evaluation of keyword extraction performances remains under-explored. This study provides a comparative evaluation of prevalent keyword extraction algorithms with different levels of complexity represented by TF-IDF, KeyBERT, and Llama 2. To evaluate their effectiveness, a mixed-methods approach is employed, combining quantitative benchmarking with qualitative assessments from 855 participants through four survey-based experiments. The findings demonstrate that KeyBERT achieves an effective balance between user preferences and computational efficiency, compared to the other algorithms. We observe a clear overall preference for gold-standard keywords, but there is a misalignment between algorithmic benchmark performance and user ratings. This reveals a long-overlooked gap between traditional precision-focused metrics and user-perceived algorithm efficiency. The study underscores the importance of human-in-the-loop evaluation methodologies and proposes analytical tools to facilitate their implementation.

关键字提取是自然语言处理的一项基础任务，支撑着无数现实世界的应用。其中之一是上下文广告，其中关键词有助于预测广告与周围媒体上下文之间的主题一致性，以提高广告效果。人工智能的最新进展提高了关键字提取能力，但也引入了对计算成本的担忧。此外，尽管最终用户体验至关重要，但关键字提取性能的人类评估仍未得到充分探索。本研究对以TF-IDF、KeyBERT和Llama 2为代表的不同复杂度的流行关键字提取算法进行了比较评价。为了评估其有效性，采用了一种混合方法，通过四个基于调查的实验，将855名参与者的定量基准与定性评估相结合。研究结果表明，与其他算法相比，KeyBERT在用户偏好和计算效率之间实现了有效的平衡。我们观察到对黄金标准关键字的明显总体偏好，但算法基准性能和用户评级之间存在不一致。这揭示了传统的以精度为中心的指标和用户感知的算法效率之间长期被忽视的差距。该研究强调了人在循环评估方法的重要性，并提出了促进其实施的分析工具。

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引用次数: 0

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 : 2026-06-01 Epub 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

A Generalized CALM Theorem for Non-Deterministic Computation in Asynchronous Distributed Systems 异步分布式系统非确定性计算的一个广义CALM定理

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

Information Systems

Pub Date : 2026-06-01 Epub Date: 2026-01-16 DOI: 10.1016/j.is.2026.102691

Tim Baccaert, Bas Ketsman

In most asynchronous distributed systems, consistency is achieved by use of coordination protocols such as Paxos, Raft, and 2PC. In many settings such protocols are too slow, too difficult to implement, or practically infeasible. The CALM theorem, initially conjectured by Hellerstein, is one of the first results characterizing precisely which problems do not require such a coordination protocol. It states that a problem has a consistent, coordination-free distributed implementation if, and only if, the problem is monotone. This was proven for deterministic problems (i.e., queries) and extends slightly beyond monotone queries for systems in which nodes can consult the data partitioning strategy.

In this work, we generalize the CALM Theorem to work for non-deterministic problems such as leader election. Furthermore, we make the theorem applicable to a wider range of distributed systems. The prior variants of the theorem have only-if directions requiring that systems may only access their identifier in the network, the identifiers of other nodes, and the data partitioning strategy. Our generalization allows us to model systems with arbitrary shared information between the nodes (e.g., network topology, leader nodes, …). It additionally allows us to create a coordination spectrum that classifies how much coordination a problem requires based on how much shared information is needed to compute it. Lastly, we apply this generalized theorem to show that the classes of polynomial time problems and coordination-free problems are not equal.

在大多数异步分布式系统中，一致性是通过使用协调协议（如Paxos、Raft和2PC）来实现的。在许多情况下，这样的协议太慢，太难实现，或者实际上不可行。最初由Hellerstein推测的CALM定理，是精确描述哪些问题不需要这种协调协议的第一批结果之一。它指出，当且仅当问题是单调的时，问题具有一致的、不需要协调的分布式实现。这在确定性问题（即查询）中得到了证明，并且稍微超出了节点可以参考数据分区策略的系统的单调查询。在这项工作中，我们推广了CALM定理，使其适用于领导人选举等非确定性问题。此外，我们使该定理适用于更广泛的分布式系统。该定理的先前变体具有only-if方向，要求系统只能访问其在网络中的标识符、其他节点的标识符和数据分区策略。我们的泛化使我们能够对节点之间任意共享信息的系统建模（例如，网络拓扑、领导节点等）。它还允许我们创建一个协调谱，根据计算问题需要多少共享信息来对问题需要多少协调进行分类。最后，我们应用这一广义定理证明了多项式时间问题和无坐标问题是不相等的。

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引用次数: 0