Information Systems最新文献_第7页

Resource allocation in business process executions—A systematic literature study 业务流程执行中的资源配置——系统的文献研究

IF 3 2区计算机科学

Information Systems Pub Date : 2025-03-05 DOI: 10.1016/j.is.2025.102541

Luise Pufahl , Fabian Stiehle , Sven Ihde , Mathias Weske , Ingo Weber

{"title":"Resource allocation in business process executions—A systematic literature study","authors":"Luise Pufahl , Fabian Stiehle , Sven Ihde , Mathias Weske , Ingo Weber","doi":"10.1016/j.is.2025.102541","DOIUrl":"10.1016/j.is.2025.102541","url":null,"abstract":"<div><div>To achieve their goals, organizations execute business processes, which require effective allocation of resources to process activities. This results in the decision-making problem: Which resources should be allocated to which process activities? This problem significantly impacts both process efficiency and effectiveness. Over the past decades, various system-initiated (largely automated) resource allocation approaches have been developed. This study presents a comprehensive overview of this field by analyzing 61 primary studies identified through a rigorous, structured literature review covering publications from 1995 to 2023. We investigate resource allocation goals and cardinalities and describe how process models, execution data, and task attributes, as well as resource attributes, are used to specify the resource allocation problem. Additionally, the type of algorithmic solution and evaluation methods are discussed. This study shows that most approaches support 1-to-1 allocation cardinalities only, specify process-oriented goals, focus on process models, and utilize rule-based methods. Based on the results, we call for future research to define common terminology, support evidence-oriented resource allocation and adaptability, and improve reproducibility and comparability by performing benchmarking studies.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"132 ","pages":"Article 102541"},"PeriodicalIF":3.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Context-aware automated ICD coding: A semantic-driven approach 上下文感知的自动ICD编码：语义驱动的方法

IF 3 2区计算机科学

Information Systems Pub Date : 2025-03-04 DOI: 10.1016/j.is.2025.102539

O.K. Reshma, N. Saleena, K.A. Abdul Nazeer

{"title":"Context-aware automated ICD coding: A semantic-driven approach","authors":"O.K. Reshma, N. Saleena, K.A. Abdul Nazeer","doi":"10.1016/j.is.2025.102539","DOIUrl":"10.1016/j.is.2025.102539","url":null,"abstract":"<div><div>Identifying the exact International Classification of Diseases (ICD) codes describing a patient’ s health condition is essential in classifying patients with similar disease conditions. Numerous studies have devised automated approaches to retrieve the ICD codes from patients’ health records. However, majority of these methodologies have considered ICD codes solely as alphanumeric codes, overlooking their descriptions and thus neglecting the inherent semantics. Also, these methodologies overlook the one-to-many semantic relationships between diagnosis and assigned ICD code descriptions. Subsequently, this constrains these approaches from effectively assigning ICD codes with meaningful context. This work addresses these limitations by capturing the semantic similarity between the diagnosis and ICD code descriptions, while utilising the inherent one-to-many relationships between them, to accurately assign ICD codes. For this, we formulate the ICD coding problem as a Semantic Text Similarity task. The proposed approach uses a siamese stacked Bi-LSTM network to learn context-aware representations of diagnoses and ICD code descriptions. We transform each patient-visit data into sentence pairs by considering the one-to-many relationships between diagnosis and assigned ICD code descriptions. Further, we compute their semantic similarity and classify them as similar or dissimilar. The proposed approach was evaluated using 5-fold cross-validation on MIMIC-III dataset and achieved the highest evaluation metric scores (F1-score 0.66, precision 0.67, recall 0.84) compared with other sequential models. The per-label evaluation demonstrates the performance of the proposed approach for each ICD code. Furthermore, the proposed approach outperformed several existing attention-based models, demonstrating the potential use of semantics in automated ICD coding.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"132 ","pages":"Article 102539"},"PeriodicalIF":3.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579980","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

An interpretable deep fusion framework for event log repair 用于事件日志修复的可解释的深度融合框架

IF 3 2区计算机科学

Information Systems Pub Date : 2025-03-02 DOI: 10.1016/j.is.2025.102548

Yongwang Yuan , Xianwen Fang , Ke Lu , ZhenHu Zhang

{"title":"An interpretable deep fusion framework for event log repair","authors":"Yongwang Yuan , Xianwen Fang , Ke Lu , ZhenHu Zhang","doi":"10.1016/j.is.2025.102548","DOIUrl":"10.1016/j.is.2025.102548","url":null,"abstract":"<div><div>In executing business processes, issues like information system failures or manual recording errors may lead to data loss in event logs, resulting in missing event logs. Utilizing such missing logs could seriously impact the quality of business process analysis results. To address this scenario, current advanced repair methods rely primarily on deep learning technology to provide intelligent solutions for business processes. However, deep learning technology is often considered a \"black-box\" model, lacking sufficient interpretability. No method is currently available to provide particular interpretability, especially in repairing specific missing values within the logs. This paper proposes the deep fusion interpretability framework based on artificial intelligence technology to address this issue. In the task of event log repair, this framework gradually transitions from the overall framework's local to global interpretability. It provides local interpretability from the attribute-level data flow perspective, semi-local interpretability from the event-level behavioral control-flow perspective, and global interpretability from the trace-level perspective. Next, we present various modes of multi-head attention within the framework and visualize the process of attention distribution calculation to explain how the framework repairs missing values through the profound combination of multi-head attention mode and context. Finally, Experimental results in real public event logs show that the DFI framework can effectively repair the missing values in event logs and explain the missing value repair process.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"132 ","pages":"Article 102548"},"PeriodicalIF":3.0,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562706","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

RATS: A resource allocator for optimizing the execution of tumor simulations over HPC infrastructures RATS：一个资源分配器，用于在高性能计算基础设施上优化肿瘤模拟的执行

IF 3 2区计算机科学

Information Systems Pub Date : 2025-02-28 DOI: 10.1016/j.is.2025.102538

Errikos Streviniotis , Nikos Giatrakos , Yannis Kotidis , Thaleia Ntiniakou , Miguel Ponce de Leon

{"title":"RATS: A resource allocator for optimizing the execution of tumor simulations over HPC infrastructures","authors":"Errikos Streviniotis , Nikos Giatrakos , Yannis Kotidis , Thaleia Ntiniakou , Miguel Ponce de Leon","doi":"10.1016/j.is.2025.102538","DOIUrl":"10.1016/j.is.2025.102538","url":null,"abstract":"<div><div>In this work, we introduce RATS (<u>R</u>esource <u>A</u>llocator for <u>T</u>umor <u>S</u>imulations), the first optimizer for the execution of tumor simulations over HPC infrastructures. Given a set of drug therapies under in-silico study, the optimization framework of RATS can: <em>(i)</em> devise the optimal number of cores and prescribe the required number of core hours; and <em>(ii)</em> under core capacity constraints, RATS schedules the execution of simulations minimizing the overall number of core hours, simultaneously prioritizing the execution of expectedly promising in-silico trials higher compared to unpromising ones. RATS is deployed by life scientists at the Barcelona Supercomputing Center to remove the burden of blindly guessing the core hours needing to be reserved from HPC admins to study various tumor treatment methodologies, as well as to rapidly distinguish effective drug combinations, thus, potentially cutting time to market for new cancer therapies. The latter is further elevated by the RATS+ extension we plug into the initial framework. RATS+ employs a Transfer Learning approach to leverage optimization models and decisions from prior in-silico studies, thereby reducing the optimization effort required for new studies in this domain.</div><div>Our experimental evaluation, on real-world data derived from the execution of more than 2500 tumor simulations on the MareNostrum4 supercomputer, confirms the effectiveness of both RATS and RATS+ across the aforementioned performance dimensions.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"132 ","pages":"Article 102538"},"PeriodicalIF":3.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hierarchical transformer-based network for multivariate time series classification 基于分层变压器的多变量时间序列分类网络

IF 3 2区计算机科学

Information Systems Pub Date : 2025-02-24 DOI: 10.1016/j.is.2025.102536

Yingxia Tang , Yanxuan Wei , Teng Li , Xiangwei Zheng , Cun Ji

引用次数: 0

Special issue: BPM 2022 Selected papers in Foundations and Engineering 特刊：BPM 2022基础与工程论文选集

IF 3 2区计算机科学

Information Systems Pub Date : 2025-02-15 DOI: 10.1016/j.is.2025.102535

Claudio Di Ciccio, Remco Dijkman, Adela del Río Ortega, Stefanie Rinderle-Ma, Manfred Reichert

引用次数: 0

Advancing EHR analysis: Predictive medication modeling using LLMs 推进电子病历分析：使用LLMs进行预测药物建模

IF 3 2区计算机科学

Information Systems Pub Date : 2025-02-08 DOI: 10.1016/j.is.2025.102528

Hanan Alghamdi , Abeer Mostafa

{"title":"Advancing EHR analysis: Predictive medication modeling using LLMs","authors":"Hanan Alghamdi , Abeer Mostafa","doi":"10.1016/j.is.2025.102528","DOIUrl":"10.1016/j.is.2025.102528","url":null,"abstract":"<div><div>In modern healthcare systems, the analysis of Electronic Health Records (EHR) is fundamental for uncovering patient health trends and enhancing clinical practices. This study aims to advance EHR analysis by developing predictive models for prescribed medication prediction using the MIMIC-IV dataset. We address data preparation challenges through comprehensive cleaning and feature selection, transforming structured patient health data into coherent sentences suitable for natural language processing (NLP). We fine-tuned several state-of-the-art large language models (LLMs), including Llama2, Llama3, Gemma, GPT-3.5 Turbo, Meditron, Claude 3.5-Sonnet, DeepSeek-R1, Falcon and Mistral, using tailored prompts derived from EHR data. The models were rigorously evaluated based on Cosine similarity, recall, precision, and F1-score, with BERTScore as the evaluation metric to address limitations of traditional n-gram-based metrics. BERTScore utilizes contextualized token embeddings for semantic similarity, providing a more accurate and human-aligned evaluation. Our findings demonstrate that the integration of advanced NLP techniques with detailed EHR data significantly improves medication management predictions. This research highlights the potential of LLMs in clinical settings and underscores the importance of seamless integration with EHR systems to improve patient safety and healthcare delivery.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"131 ","pages":"Article 102528"},"PeriodicalIF":3.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Process-driven design of cloud data platforms 云数据平台的流程驱动设计

IF 3 2区计算机科学

Information Systems Pub Date : 2025-02-04 DOI: 10.1016/j.is.2025.102527

Matteo Francia, Matteo Golfarelli, Manuele Pasini

{"title":"Process-driven design of cloud data platforms","authors":"Matteo Francia, Matteo Golfarelli, Manuele Pasini","doi":"10.1016/j.is.2025.102527","DOIUrl":"10.1016/j.is.2025.102527","url":null,"abstract":"<div><div>Data platforms are state-of-the-art solutions for implementing data-driven applications and analytics. They facilitate the ingestion, storage, management, and exploitation of big data. Data platforms are built on top of complex ecosystems of services answering different data needs and requirements; such ecosystems are offered by different providers (e.g., Amazon AWS and Microsoft Azure). However, when it comes to engineering data platforms, no unifying strategy and methodology is available yet, and the design is mainly left to the expertise of practitioners in the field. Service providers simply expose a long list of interoperable and alternative engines, making it hard to select the optimal subset without a deep knowledge of the ecosystem. A more effective design approach starts with knowledge of the data transformation and exploitation processes that the platform should support. In this paper, we sketch a computer-aided design methodology and then focus on the selection of the optimal services needed to implement such processes. We show that our approach lightens the design of data platforms and enables an unbiased selection and comparison of solutions even through different service ecosystems.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"131 ","pages":"Article 102527"},"PeriodicalIF":3.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143196974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing cross-lingual text classification through linguistic and interpretability-guided attack strategies 通过语言和可解释性引导的攻击策略增强跨语言文本分类

IF 3 2区计算机科学

Information Systems Pub Date : 2025-01-27 DOI: 10.1016/j.is.2025.102526

Abdelmounaim Kerkri , Mohamed Amine Madani , Aya Qeraouch , Kaoutar Zouin

{"title":"Enhancing cross-lingual text classification through linguistic and interpretability-guided attack strategies","authors":"Abdelmounaim Kerkri , Mohamed Amine Madani , Aya Qeraouch , Kaoutar Zouin","doi":"10.1016/j.is.2025.102526","DOIUrl":"10.1016/j.is.2025.102526","url":null,"abstract":"<div><div>While adversarial attacks on natural language processing systems have been extensively studied in English, their impact on morphologically complex languages remains poorly understood. We investigate how text classification systems respond to adversarial attacks across Arabic, English, and French — languages chosen for their distinct linguistic properties. Building on the DeepWordBug framework, we develop multilingual attack strategies that combine random perturbations with targeted modifications guided by model interpretability. We also introduce novel attack methods that exploit language-specific features like orthographic variations and syntactic patterns. Testing these approaches on a diverse dataset of news articles (9,030 Arabic, 14,501 English) and movie reviews (200,000 French), we find that interpretability-guided attacks are particularly effective, achieving misclassification rates of 58%–62% across languages. Language-specific perturbations also proved potent, degrading model performance to F1-scores between 0.38 and 0.63. However, incorporating adversarial examples during training markedly improved model robustness, with F1-scores recovering to above 0.82 across all test conditions. Beyond the immediate findings, this work reveals how adversarial vulnerability manifests differently across languages with varying morphological complexity, offering key insights for building more resilient multilingual NLP systems.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"131 ","pages":"Article 102526"},"PeriodicalIF":3.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143196971","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

Federated conformance checking 联邦一致性检查

IF 3 2区计算机科学

Information Systems Pub Date : 2025-01-25 DOI: 10.1016/j.is.2025.102525

Majid Rafiei, Mahsa Pourbafrani, Wil M.P. van der Aalst

{"title":"Federated conformance checking","authors":"Majid Rafiei, Mahsa Pourbafrani, Wil M.P. van der Aalst","doi":"10.1016/j.is.2025.102525","DOIUrl":"10.1016/j.is.2025.102525","url":null,"abstract":"<div><div>Conformance checking is a crucial aspect of process mining, where the main objective is to compare the actual execution of a process, as recorded in an event log, with a reference process model, e.g., in the form of a Petri net or a BPMN. Conformance checking enables identifying deviations, anomalies, or non-compliance instances. It offers different perspectives on problems in processes, bottlenecks, or process instances that are not compliant with the model. Performing conformance checking in federated (inter-organizational) settings allows organizations to gain insights into the overall process execution and to identify compliance issues across organizational boundaries, which facilitates process improvement efforts among collaborating entities. In this paper, we propose <em>a privacy-aware federated conformance-checking approach</em> that allows for evaluating the correctness of overall cross-organizational process models, identifying miscommunications, and quantifying their costs. For evaluation, we design and simulate a supply chain process with three organizations engaged in purchase-to-pay, order-to-cash, and shipment processes. We generate synthetic event logs for each organization as well as the complete process, and we apply our approach to identify and evaluate the cost of pre-injected miscommunications.</div></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"131 ","pages":"Article 102525"},"PeriodicalIF":3.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143196972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0