Information Systems最新文献_第3页

A visualization-driven decision support system for selecting feature attribution methods 基于可视化的特征归因方法选择决策支持系统

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-06-01 Epub Date: 2026-01-02 DOI: 10.1016/j.is.2025.102661

Priscylla Silva , Evandro Ortigossa , Dishita Turakhia , Claudio Silva , Luis Gustavo Nonato

{"title":"A visualization-driven decision support system for selecting feature attribution methods","authors":"Priscylla Silva , Evandro Ortigossa , Dishita Turakhia , Claudio Silva , Luis Gustavo Nonato","doi":"10.1016/j.is.2025.102661","DOIUrl":"10.1016/j.is.2025.102661","url":null,"abstract":"<div><div>Feature attribution techniques are crucial for interpreting machine learning models, but practitioners often face difficulties to understand how different methods compare and which one best fits their analytical goals. This difficulty arises from inconsistent results across methods, evaluation metrics that emphasize distinct and sometimes conflicting properties, and subjective preferences that influence how explanation quality is interpreted. In this paper, we introduce Explainalytics, an open-source Python library that transforms this challenging decision-making process into an evidence-based visual analytics workflow. Explainalytics calculates a range of evaluation metrics and presents the results through five coordinated views spanning global to local analysis. Linked filtering, dynamic updates, and brushing allow users to pivot fluidly between global trends and local details, supporting exploratory sense-making rather than rigid pipelines. In a within-subject laboratory study with 10 machine learning practitioners, we compared Explainalytics against a baseline. Explainalytics users experienced significantly lower cognitive workload and higher perceived usability.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"138 ","pages":"Article 102661"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977429","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

A Bayesian framework for robust local intrinsic dimensionality estimation 鲁棒局部固有维数估计的贝叶斯框架

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-06-01 Epub Date: 2026-01-02 DOI: 10.1016/j.is.2025.102668

Zaher Joukhadar , Hanxun Huang , Sarah Monazam Erfani , Ricardo J.G.B. Campello , Michael E. Houle , James Bailey

{"title":"A Bayesian framework for robust local intrinsic dimensionality estimation","authors":"Zaher Joukhadar , Hanxun Huang , Sarah Monazam Erfani , Ricardo J.G.B. Campello , Michael E. Houle , James Bailey","doi":"10.1016/j.is.2025.102668","DOIUrl":"10.1016/j.is.2025.102668","url":null,"abstract":"<div><div>Local Intrinsic Dimensionality (LID) is a measure of data complexity in the vicinity of a query point. In this work, we propose a novel Bayesian framework for LID estimation that improves robustness and accuracy, especially in scenarios with small neighborhood sizes (<span><math><mrow><mi>k</mi><mo>≤</mo><mn>10</mn></mrow></math></span>), where maintaining locality is critical. Our framework allows the incorporation of both informative and non-informative priors, enabling the integration of prior knowledge to enhance the estimation process. Using this framework, we derive new LID estimators and provide insights into transitional ones. Furthermore, we propose aggregation methods using linear and logarithmic pooling to combine multiple LID posteriors. These methods allow for principled integration of LID estimates across different training states of Deep Neural Networks (DNNs), such as epochs, thereby improving estimation stability and performance. We also derive a posterior predictive distribution (PPD) for modeling and synthesizing nearest-neighbor distances. Experiments demonstrate that the LID Bayesian estimation methods achieves a balanced approach between reducing variance and lowering squared bias, often leading to lower mean squared error (MSE) values for small number of nearest neighbors.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"138 ","pages":"Article 102668"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023093","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

A decade of systems for human data interaction 人类数据交互系统的十年

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-06-01 Epub Date: 2026-01-21 DOI: 10.1016/j.is.2026.102689

Eugene Wu , Yiru Chen , Haneen Mohammed , Zezhou Huang

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

Information Systems Pub Date : 2026-06-01 Epub Date: 2025-11-29 DOI: 10.1016/j.is.2025.102651

Ruijia Guo, Zhiyuan Chen

{"title":"IPDRM: A pyramid-based diffusion and contrastive learning framework for sequential recommendation","authors":"Ruijia Guo, Zhiyuan Chen","doi":"10.1016/j.is.2025.102651","DOIUrl":"10.1016/j.is.2025.102651","url":null,"abstract":"<div><div>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 <span><span>https://github.com/CLTCGUO/IPDRM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"138 ","pages":"Article 102651"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685680","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

HILTS: Human-LLM collaboration for effective data labeling HILTS：人类-法学硕士协作有效的数据标签

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-06-01 Epub Date: 2025-12-12 DOI: 10.1016/j.is.2025.102660

Juliana Barbosa, Eduarda Alencar, Grace Fan, Aécio Santos, Juliana Freire

{"title":"HILTS: Human-LLM collaboration for effective data labeling","authors":"Juliana Barbosa, Eduarda Alencar, Grace Fan, Aécio Santos, Juliana Freire","doi":"10.1016/j.is.2025.102660","DOIUrl":"10.1016/j.is.2025.102660","url":null,"abstract":"<div><div>The growing complexity and volume of data highlight the importance of learning-based classifiers across diverse tasks, from medical diagnosis to environmental monitoring. A common and impactful use case is data triage—efficiently identifying rare, relevant instances in large, imbalanced datasets. This is crucial for enabling domain experts to focus on what matters most. However, traditional supervised learning approaches often struggle with scalability due to the high cost and time required for manual labeling.</div><div>We introduce HILTS (Human-In-the-loop Learn To Sample), a framework designed to tackle these limitations. HILTS leverages Large Language Models (LLMs) for automated initial labeling and strategically incorporates human expertise through advanced active learning techniques. It selects diverse and representative samples for pseudo-labeling and identifies highly uncertain or likely incorrect LLM labels for targeted human review. This focused use of human effort maximizes the value of domain expertise while minimizing annotation overhead.</div><div>Our system reduces human labeling effort by up to 80% while outperforming few-shot foundation models such as GPT-4 by over 5% in F1-score in some scenarios—all at a significantly lower cost. HILTS also shows clear improvements over fully automated pseudo-labeling approaches and proves especially effective in handling class imbalance in real-world datasets. Its adaptability and efficiency make it a practical and scalable solution for high-stakes, domain-specific data triage tasks.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"138 ","pages":"Article 102660"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791324","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

Paper title: Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs 论文题目：过程挖掘的事件日志缺陷模式：走向清除事件日志的系统方法

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-04-01 Epub Date: 2025-11-06 DOI: 10.1016/j.is.2025.102645

Robert Andrews, Moe Thandar Wynn

{"title":"Paper title: Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs","authors":"Robert Andrews, Moe Thandar Wynn","doi":"10.1016/j.is.2025.102645","DOIUrl":"10.1016/j.is.2025.102645","url":null,"abstract":"<div><div>Process-oriented data mining (process mining) uses algorithms and data (in the form of event logs) to construct models that aim to provide insights into organisational processes. The quality of the data (both form and content) presented to the modeling algorithms is critical to the success of the process mining exercise. Cleaning event logs to address quality issues prior to conducting a process mining analysis is a necessary, but generally tedious and ad hoc task. In this paper we describe a set of data quality issues, distilled from our experiences in conducting process mining analyses, commonly found in process mining event logs or encountered while preparing event logs from raw data sources. We show that patterns are used in a variety of domains as a means for describing commonly encountered problems and solutions. The main contributions of this article are in showing that a patterns-based approach is applicable to documenting commonly encountered event log quality issues, the formulation of a set of components for describing event log quality issues as patterns, and the description of a collection of 11 event log imperfection patterns distilled from our experiences in preparing event logs. We postulate that a systematic approach to using such a pattern repository to identify and repair event log quality issues benefits both the process of preparing an event log and the quality of the resulting event log. The relevance of the pattern-based approach is illustrated via application of the patterns in a case study and through an evaluation by researchers and practitioners in the field.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"137 ","pages":"Article 102645"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521064","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

KAVA-PM: Knowledge-assisted visual process mining KAVA-PM：知识辅助视觉过程挖掘

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-04-01 Epub Date: 2025-11-01 DOI: 10.1016/j.is.2025.102638

Daniel Schuster , Wolfgang Aigner , Chiara Di Francescomarino , Cagatay Turkay , Francesca Zerbato

{"title":"KAVA-PM: Knowledge-assisted visual process mining","authors":"Daniel Schuster , Wolfgang Aigner , Chiara Di Francescomarino , Cagatay Turkay , Francesca Zerbato","doi":"10.1016/j.is.2025.102638","DOIUrl":"10.1016/j.is.2025.102638","url":null,"abstract":"<div><div>This article aims to foster a collaborative environment between the visual analytics and process mining communities by bringing together analysis methods, techniques, and tools from the process mining and visual analytics domains to devise a new knowledge-assisted, human-in-the-loop approach to process mining. Building on recent advances in methods emphasizing the role of human knowledge in analysis, we introduce <em>knowledge-assisted interactive visual process mining</em> (KAVA-PM) as a framework where analysts’ tacit knowledge and the externalizations of this knowledge play a key role. To achieve this, we extend an established conceptual model of KAVA that combines interactive visualizations and automated methods to support a richer process mining analysis practice that has human experts and their knowledge at its core. The paper outlines the key components of KAVA-PM as a conceptual model and its relations, proposes key analytical patterns, and demonstrates the use and validity of the patterns through usage scenarios. We then present challenges and open problems which we validate through a survey with experts. We anticipate that along with the conceptual model, these challenges will bring the VA and PM communities together along a shared research agenda where the role of humans and their knowledge is better established.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"137 ","pages":"Article 102638"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468411","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

Implementing a declarative query language for high level machine learning application design 实现用于高级机器学习应用程序设计的声明式查询语言

IF 3.4 2区计算机科学

Information Systems Pub Date : 2026-04-01 Epub Date: 2025-11-03 DOI: 10.1016/j.is.2025.102637

Hasan H. Rahman , Hasan M. Jamil

{"title":"Implementing a declarative query language for high level machine learning application design","authors":"Hasan H. Rahman , Hasan M. Jamil","doi":"10.1016/j.is.2025.102637","DOIUrl":"10.1016/j.is.2025.102637","url":null,"abstract":"<div><div>The rising popularity of data science and machine learning (ML) across diverse domains, often driven by users with limited computational expertise, reflects the growing commoditization of ML tools. However, the advanced technical and mathematical knowledge demanded by current ML frameworks poses a formidable barrier for non-experts, preventing them from fully exploiting these powerful platforms.</div><div>In response, we introduce MQL, a novel declarative query language for ML application design, alongside its corresponding query processing engine. We demonstrate that abstracting ML concepts – similarly to SQL – can preserve both processing efficiency and analytical fidelity. Our implementation defines MQL semantics through a semantics-preserving mapping to widely understood ML code fragments. By leveraging task-specific meta-features, heuristic knowledge, and standard assessment methods, our system ranks candidate ML libraries, selects optimal algorithms, and frees users from these choices.</div><div>We introduce mapping algorithms to ensure that each MQL program retains its intended semantics and present experimental evaluations demonstrating that MQL’s algorithmic selections not only match but surpass human-engineered solutions in terms of performance and model accuracy. By offering declarative queries as a high-level alternative to traditional coding, MQL significantly reduces the complexity of data analysis pipeline construction, thereby democratizing machine learning application design.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"137 ","pages":"Article 102637"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571556","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

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

Information Systems Pub Date : 2026-04-01 Epub 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

{"title":"Reproducible experiments on visual exploration framework of geospatial vector big data","authors":"Zebang Liu , Anran Yang , Mengyu Ma , Luo Chen , Jiali Zhou , Ning Jing , Jichong Yin , Pranav Kasela , Raúl Martín-Santamaría","doi":"10.1016/j.is.2025.102640","DOIUrl":"10.1016/j.is.2025.102640","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"137 ","pages":"Article 102640"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571431","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

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

Information Systems Pub Date : 2026-04-01 Epub Date: 2025-11-14 DOI: 10.1016/j.is.2025.102646

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

{"title":"Design patterns for GDPR-aware process modeling in BPMN","authors":"Simone Agostinelli , Francesca De Luzi , Fabrizio Maria Maggi , Andrea Marrella , Alessia Volpi","doi":"10.1016/j.is.2025.102646","DOIUrl":"10.1016/j.is.2025.102646","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"137 ","pages":"Article 102646"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571432","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