Information Systems最新文献

{"title":"Measuring the decentralisation of DeFi development: An empirical analysis of contributor distribution in Lido","authors":"Giuseppe Destefanis ,&nbsp;Jiahua Xu ,&nbsp;Silvia Bartolucci","doi":"10.1016/j.is.2026.102695","DOIUrl":"10.1016/j.is.2026.102695","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"139 ","pages":"Article 102695"},"PeriodicalIF":3.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081680","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}

{"title":"Deep learning approaches for handling noisy data in collaborative filtering: A survey","authors":"Ouahiba Belgacem ,&nbsp;Boudjemaa Boudaa ,&nbsp;Abderrahmane Kouadria ,&nbsp;Abdelhafid Abouaissa","doi":"10.1016/j.is.2026.102699","DOIUrl":"10.1016/j.is.2026.102699","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"139 ","pages":"Article 102699"},"PeriodicalIF":3.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190248","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}

{"title":"Efficient data structures for fast and low-cost first-order logic rule mining","authors":"Ruoyu Wang ,&nbsp;Raymond Wong ,&nbsp;Daniel Sun","doi":"10.1016/j.is.2026.102690","DOIUrl":"10.1016/j.is.2026.102690","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"139 ","pages":"Article 102690"},"PeriodicalIF":3.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049170","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}

{"title":"HLR-SQL: Human-like reasoning for Text-to-SQL with the human in the loop","authors":"Timo Eckmann ,&nbsp;Matthias Urban ,&nbsp;Jan-Micha Bodensohn ,&nbsp;Carsten Binnig","doi":"10.1016/j.is.2025.102670","DOIUrl":"10.1016/j.is.2025.102670","url":null,"abstract":"<div><div>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 <u>H</u>uman-<u>L</u>ike <u>R</u>easoning 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.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"138 ","pages":"Article 102670"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023097","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}

{"title":"Improving the understandability of declarative process discovery results using easyDeclare","authors":"Graziano Blasilli ,&nbsp;Lauren S. Ferro ,&nbsp;Simone Lenti ,&nbsp;Fabrizio Maria Maggi ,&nbsp;Andrea Marrella ,&nbsp;Tiziana Catarci","doi":"10.1016/j.is.2025.102667","DOIUrl":"10.1016/j.is.2025.102667","url":null,"abstract":"<div><div>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 <span>easyDeclare</span>, a novel visual notation to specify declarative process models using the <span>Declare</span> language. <span>easyDeclare</span> was developed with consideration of the well-established Moody’s design principles. We conducted extensive user experiments to demonstrate that <span>easyDeclare</span>, when compared with the original graphical representation of <span>Declare</span>, reduces the cognitive load required to interpret <span>Declare</span> models of increasing complexity, making it a promising alternative to enhancing overall comprehension of declarative process discovery tasks.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"138 ","pages":"Article 102667"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884521","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}

{"title":"ACTER: Activity Customization through Timely and Explainable Recommendations","authors":"Anna Dalla Vecchia,&nbsp;Niccolò Marastoni,&nbsp;Barbara Oliboni,&nbsp;Elisa Quintarelli","doi":"10.1016/j.is.2025.102666","DOIUrl":"10.1016/j.is.2025.102666","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"138 ","pages":"Article 102666"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737870","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}

{"title":"Visualizing repetition in process execution variants from partially ordered event data","authors":"Ariba Siddiqui ,&nbsp;Francesca Zerbato ,&nbsp;Daniel Schuster","doi":"10.1016/j.is.2025.102664","DOIUrl":"10.1016/j.is.2025.102664","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"138 ","pages":"Article 102664"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737932","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}

{"title":"From precision to perception: Human-in-the-loop evaluation of keyword extraction for internet-scale contextual advertising","authors":"Jingwen Cai ,&nbsp;Sara Leckner ,&nbsp;Johanna Björklund","doi":"10.1016/j.is.2025.102665","DOIUrl":"10.1016/j.is.2025.102665","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"138 ","pages":"Article 102665"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737933","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}

{"title":"A Generalized CALM Theorem for Non-Deterministic Computation in Asynchronous Distributed Systems","authors":"Tim Baccaert,&nbsp;Bas Ketsman","doi":"10.1016/j.is.2026.102691","DOIUrl":"10.1016/j.is.2026.102691","url":null,"abstract":"<div><div>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.</div><div>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.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"138 ","pages":"Article 102691"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023092","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}

{"title":"MDU-Net: Multi-resolution learning and differential clustering fusion for multivariate electricity time series forecasting","authors":"Yongming Guan ,&nbsp;Chengdong Zheng ,&nbsp;Yuliang Shi ,&nbsp;Gang Wang ,&nbsp;Linfeng Wu ,&nbsp;Zhiyong Chen ,&nbsp;Hui Li","doi":"10.1016/j.is.2026.102693","DOIUrl":"10.1016/j.is.2026.102693","url":null,"abstract":"<div><div>Artificial intelligence (AI) has demonstrated transformative potential in diverse fields such as healthcare, drug discovery, and natural language processing by enabling advanced pattern recognition and predictive modeling of complex data. Particularly in the power system, where it involves areas such as power load, electricity price, and renewable energy, the application of AI technology to enhance the multivariate electricity time series forecasting tasks is crucial for grid security and economic dispatch. In power systems, multivariate electricity time series forecasting tasks involving power load, electricity prices, and renewable energy are crucial for grid security and economic dispatch. Contemporary forecasting approaches primarily focus on two aspects: modeling multi-scale periodic characteristics within sequences and capturing complex collaborative dependencies among variables. However, existing techniques often fail to simultaneously disentangle multi-scale features and model the dynamically heterogeneous dependencies between variables. To overcome these limitations, this paper proposes MDU-Net, a novel forecasting framework. The framework comprises two core modules: Multi-resolution hierarchical Union learning (MRU) module and Differential Channel Clustering Fusion (DCCF) Module. The MRU module constructs multi-granularity temporal representations through downsampling and achieves effective cross-scale feature fusion by integrating channel-independent operations with seasonal-trend decomposition. The DCCF module adopts first- and second-order derivative approximations to generate soft clustering mask matrices, adaptively capturing asymmetric collaborative dependencies among different variables over time. Experimental results on multiple public datasets (ETT, Electricity) demonstrate that MDU-Net significantly outperforms state-of-the-art baselines in multivariate electricity time series prediction. it achieves 2.7% and 17.1% relative MSE reductions compared to TimeMixer and PatchTST, respectively, with 1.4% and 14.4% lower MAE. Notably, MDU-Net maintains strong generalization capabilities and computational efficiency. The framework also shows promising performance in cross-domain applications such as traffic forecasting.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"138 ","pages":"Article 102693"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023095","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}