CoopIS 2025 Abstracts

Area 1 - Applications of Human-centric Information Systems in a Digital World

Full Papers

Paper Nr:	144
Title:	From Prediction to Diagnostic Action: A Human-Centric System for Coronary Artery Disease Assessment
Authors:	Mohamed Amine Chaabane, Imen Ben Said, Sirine Ayedi and Amine Bahloul
Abstract:	Coronary Artery Disease (CAD), a leading cause of death worldwide, requires prompt and accurate diagnosis to enable effective treatment and avoid unnecessary or invasive procedures. Traditional statistical methods, while widely used, often fall short in delivering reliable predictive performance, especially in heterogeneous patient populations. To address this gap, this paper explores the use of Machine Learning (ML) to improve CAD risk assessment and support diagnostic decision-making. We propose a stacking ensemble model that combines multiple classifiers and achieves an accuracy of 83%, outperforming individual models. Building on this model, we introduce CARDiA, a human-centered, intelligent clinical decision support system (CDSS) designed to assist cardiologists in evaluating CAD likelihood and determining the most appropriate diagnostic actions. CARDiA integrates a workflow-based recommendation service that supports diagnostic decisions aligned with established clinical guidelines. By combining predictive accuracy with explainable, process-driven guidance, CARDiA offers a robust, user-friendly tool to enhance CAD diagnosis in clinical practice.

Short Papers

Paper Nr:	21
Title:	AI-Powered Cooperative Fleet Management Through Explainable Context-Aware Anomaly Detection
Authors:	Nadeem Iftikhar, Cosmin-Stefan Raita, Aziz Kadem, Matthew Haze Trinh, Yi-Chen Lin, David Buncek, Anders Vestergaard and Gianna Belle
Abstract:	Effective cooperative management of maritime fleets is challenged by technologies that cannot distinguish between actual technical faults and normal operational patterns influenced by weather. This ambiguity leads to frequent false alarms, undermining operator trust and limiting collaborative decision-making. To solve this problem, this paper introduces a framework that creates reliable, explainable knowledge from centrally-processed fleet data. The novelty relies in its two-stage process. First, it fuses sensor data with external weather context and assesses the context's reliability using a Weather Confidence Score—a score derived from engine performance, vessel proximity to land, and an assessment of weather data trustworthiness. Second, a dual-autoencoder ensemble performs a counterfactual analysis to generate an explainable Weather Influence Index. The Weather Index quantifies weather's impact, enabling a granular classification of anomalies as technical or weather-driven, moving beyond simple binary flagging. Evaluation on real-world vessel data shows the framework can reliably differentiate these anomaly types, enabling more trustworthy fleet-wide monitoring.

Area 2 - Architecture and Management of Information Systems

Full Papers

Paper Nr:	29
Title:	A Maturity Model for Blockchain Adoption in the Banking Industry
Authors:	Faruk Hasić, Amalia Morel de Westgaver and Johannes De Smedt
Abstract:	Blockchain is an emerging technology that facilitates the elimination of intermediaries or third parties, thereby reducing costs and enhancing efficiency. Over the past decade, blockchain has been integrated across various industries, yet its wide-scale adoption within the financial industry remains uncertain. This paper seeks to aid financial institutions in adopting blockchain technology by developing a maturity model. This model is intended to assist organisations in assessing their current maturity level and charting a way forward towards a more enhanced adoption of the technology. We construct an initial model based on literature on existing maturity models in other fields of study. Subsequently, we refine the model through the Delphi approach which is a qualitative, systematic, and iterative panel method. We conduct the Delphi method in three rounds with experts in the field, yielding a final maturity model and a corresponding questionnaire which allows financial institutions to conduct a self-assessment of their blockchain adoption capabilities. We discuss the validity of the model and provide a proof of concept through a self-assessment exercise by a participating institution. The panel participants and contributing institutions concurred that the resulting maturity model and the corresponding self-assessment questionnaire are relevant and useful tools for the purpose of assessing their current maturity and informing future actions to enhance their blockchain adoption capabilities.

Paper Nr:	100
Title:	A Method for Automated Deployment Architecture Reconstruction from Heterogeneously Nested Deployment Models
Authors:	Marcel Weller, Uwe Breitenbücher and Steffen Becker
Abstract:	Deploying large-scale software systems often requires combining multiple different deployment technologies by creating heterogeneously nested deployment models, for example, using Kubernetes manifests within a Terraform configuration file. If such nested models need to be manually analyzed regarding the architecture of the final deployment, immense technical expertise in different technologies is required. Moreover, current automated architecture reconstruction approaches do not consider such heterogeneously nested deployment models. Therefore, we present a rule-based method that automatically transforms a given deployment model into a technology-agnostic, graph-based architecture model while considering heterogeneously nested deployment models. We applied the method in a case study to reconstruct the deployment architectures from six deployment models of three reference applications using different combinations of the technologies Ansible, Kubernetes, Terraform, Helm, Bash, and Docker. The case study shows that many architectural aspects can be reconstructed generically, but some parts need custom or semantic analysis transformation rules.

Short Papers

Paper Nr:	25
Title:	Inferring a Hierarchical Input Type for Plug-and-Play SQL
Authors:	Santosh Aryal, Shubham Swami, Curtis Dyreson and Sourav S Bhowmick
Abstract:	Plug-and-play queries simplify the process of writing SQL queries. In plug-and-play SQL a programmer uses a query guard to specify a query’s input type. The input type is the shape of the data needed by a query. The input type can be matched to a database’s schema to determine whether the query can be safely evaluated. Plug-and-play queries are portable, easier to write, and are type safe. But plug-and-play queries depend on the programmer to provide an input type. Since the programmer could err in sketching the input type this paper shows how to infer the input type from the query itself, thereby removing the need for manual construction. We consider two cases for inferring the input type: with and without knowledge of the database’s schema. Knowing the schema can help to prune the input type, making it smaller and more flexible. This paper describes the inference algorithm.

Paper Nr:	102
Title:	Boosting the Entity-Relationship Model for Document-Oriented Databases
Authors:	Andrea Avignone, Silvia Chiusano, Alessandro Fiori and Riccardo Torlone
Abstract:	The growing adoption of NoSQL document-oriented databases, such as MongoDB, has introduced new paradigms for managing data with flexibility and scalability. These characteristics make document models suitable for applications that involve heterogeneous data sources and evolving information needs. Current design methodologies often lack formal support, covering only a subset of Entity-Relationship (ER) constructs or relying on general-purpose notations like UML, which are not optimized for database design. In this work, we propose ERd, a revised version of the ER model to support the conceptual design of document-oriented databases. ERd extends the classical ER framework to capture the hierarchical and flexible nature of document structures, enabling a more accurate and semantically rich representation of data. We define a set of translation rules that map conceptual constructs into typical document patterns like embedding, referencing, and polymorphism.

Area 3 - Human Aspects and Social Interaction in Information Systems

Full Papers

Paper Nr:	71
Title:	Digital Twin Narratives: Framework for Clear Communication
Authors:	Faten El Outa, Hugo Breuillard and Guillaume Dechambenoit
Abstract:	Digital Twin (DT) systems are increasingly applied in en-vironmental contexts to support real-time monitoring, forecasting, anddecision-making. While technically advanced, many DT implementationsfall short in communicating insights in ways that are accessible, inter-pretable, and actionable for diverse audiences. This paper introducesa user-centered framework that enhances the communicative power ofenvironmental DTs through the integration of data storytelling tech-niques. The proposed approach is structured around five components:(i) a four-phase data storytelling process, (ii) a user-system interactionmodel driven by user intent, (iii) progressive levels of knowledge formal-ization, (iv) a workflow that adapts key data storytelling activities to gen-erate DT outputs that are both analytically robust and communication-ready, and (v) a semantic mapping table that links analytical opera-tions to narrative and visual outputs. Together, these components enableDT systems to transform analytical results into meaningful, audience-adapted stories. The framework is illustrated through environmental pre-diction use case, demonstrating its potential to enhance interpretability,increase user engagement, and support more informed decision-making

Area 4 - Human-centric Security and Privacy in Information Systems

Full Papers

Paper Nr:	62
Title:	RDF Query Answering in the Presence of Access Restrictions
Authors:	Maxime Buron, Hritika Kathuria, Ioana Manolescu and George Siachamis
Abstract:	In this work, we explore algorithms for answering conjunctive RDF queries in the presence of RDFS ontologies and access control. We consider an access control setting where by default all users have access to the complete graph, and a restriction can forbid user a user’s access to specific IRIs. Here, restricting for user u the access to an IRI i entails that: no answer to a query by u may contain the IRI i; no triple containing i can be used to compute an answer for a query by i, nor to entail such a triple via reasoning with the ontology. We present a set query answering algorithms for this novel context, and formally prove that five among them are correct, i.e., sound and complete, with respect to both the ontology and the access restrictions in place. We have implemented all our algorithms and present experiments comparing their performance.

Paper Nr:	130
Title:	Fed-DL: Federated Learning with Distributed Ledger for Social Demographic Equality
Authors:	Jaya Pathak, Yash Pandey, Jagat Sesh Challa and Amitesh Singh Rajput
Abstract:	Federated Learning has become a viable solution to train machine learning models on decentralized data without sharing sensitive information. Meanwhile, Blockchain ensures data integrity and transparency. In this work, we utilize the best of both worlds by implementing a distributed blockchain ledger within a federated learning system, named Fed-DL. Due to aggregation of non-IID data within federated setup, the model favors over-represented groups within clients. This introduces inequality in model's predictive performance across different demographics, resulting in bias within the system. The proposed federated learning framework based on smart contract filters irregular or false updates from clients that negatively impact predictive analysis within demographic groups. This framework is also effective in reducing the server traffic by allowing limited updates which are validated through consensus for model aggregation at the server. Fed-DL not only helps in protecting sensitive client's information but also fosters trust and equality among participants through transparency and record (updates) traceability. This approach enhances the overall quality of machine learning models while adhering to data protection laws and prompts fair and ethical practice. Results indicate that the proposed framework performs well in non-IID data scenarios and is also robust against poisoning attacks.

Paper Nr:	133
Title:	Towards User-Centric Authorization for Data Access, Sharing and Control
Authors:	Florian Weingartshofer, Aya Mohamed and Marc Kurz
Abstract:	With the increasing demand and interest in users' data in various domains like energy and Internet of Things (IoT), access to these data should be protected, but also controlled by the user for transparent data access and sharing. Currently, there is no standard for user-centric authorization as in typical authorization and access control models due to challenges related to the different authorization processes and entities involved, which vary for each domain and use case. First, we explain user-centric authorization compared to traditional authorization and user consent along with related work. We propose a user-centric authorization approach to involve users in the authorization process to decide about access to their private data by requesters, share specific data with interested parties, and control even after granting the permission. Actors (e.g., user, requester, and data provider), resources (i.e., request and data), general states, and request structure are defined for the permission request, data access, and revoke processes. Our approach is implemented and applied within the European Distributed Data Infrastructure for Energy (EDDIE) project to access and share energy data. We provide a demo case, including implementation of the model and detailed steps from creating a permission request until getting a decision from the end-customer and receiving the requested energy consumption data. Finally, we discuss characteristics of our approach, such as multi-transparency, permission revocation, access audit, data granularity, and external termination.

Short Papers

Paper Nr:	77
Title:	A Hybrid GAM-Based Model for Predicting Vulnerability Exploitation
Authors:	Noufal Issa, Damas Gruska and Loubna Ali
Abstract:	Vulnerability management requires prioritizing which vulnerabilities to patch, since only a small fraction are ever exploited, and writing, testing, and installing patches can involve considerable resources, requiring companies to prioritize based on some notion of risk. Traditional severity scores, such as the Common Vulnerability Scoring System are often poor predictors of exploitation risk. Data-driven scores, such as the Exploit Prediction Scoring System, provide probabilities of exploitation, but still leave room for improvements. We propose a lightweight hybrid model using a Generalized Additive Model (GAM) that combines numeric features (CVSS base score, EPSS probability, age, reference count) with semantic text features (derived from the vulnerability description via Term Frequency–Inverse Document Frequency and Singular Value Decomposition). The GAM framework yields an interpretable, additive risk score without black-box explanations. On a 2023 training set (with labels from CISA’s KEV and public exploits), our model achieves significantly better precision-recall tradeoff than CVSS or EPSS alone. Tested on 2024 disclosures, our presented model consistently outperforms the baselines at nearly all recall levels.

Area 5 - Inductive Learning, Machine-Learning and Knowledge Discovery

Full Papers

Paper Nr:	17
Title:	NarrativeMind: A Dynamic Neural-Symbolic Decoder for Culturally-Authentic Arabic Story Generation
Authors:	Mossab Ibrahim, Pablo Gervás and Gonzalo Méndez
Abstract:	We introduce NarrativeMind, a previously unexplored cooperative neural-symbolic decoder in Arabic NLP that dynamically injects dialect-specific cultural constraints during generation. Our approach achieves a +2.7 BLEU improvement over AraBERT while reducing MSA bias by 58%, addressing the critical gap where traditional Arabic narrative systems inadequately capture the rhetorical sophistication embedded in morphologically complex literary forms. The hybrid architecture seamlessly integrates classical Arabic structures into BLOOMZ’s decoding through weighted interpolation, preserving essential rhetorical devices. For instance, it maintains السجع (sajʿ) patterns in “الحكمة في الكلمة والبركة في العملة” (“wisdom in words, blessing in currency”) and الجناس (jinās) wordplay as demonstrated in “قال القائل للقاتل” (“the speaker said to the killer”). Unlike rigid frameworks, NarrativeMind adapts fluidly across Modern Standard Arabic and six regional dialects, particularly benefiting under-resourced Maghrebi varieties. Our real-time multi-dialect collaboration employs adaptive constraint weighting, optimizing both BLEU coherence and our novel CulturalScore metric. This metric derives from 2,500 expert-annotated templates spanning classical مقامات (maqāmāt) to contemporary حكايات شعبية (ḥikāyāt shaʿbiyya), ensuring comprehensive cultural representation. MADAR corpus evaluation (n = 12,000) demonstrates substantial improvements: BLEU scores reach 29.8 ± 0.4 versus 27.1 ± 0.3 for the baselines, with dialectal accuracy achieving κ = 0.76 compared to 0.70. Human evaluation involving 15 linguists and 185 native speakers validates 82.5 % cultural authenticity (p < 0.01), confirming effective cross-regional story co-creation while preserving dialectal integrity.

Paper Nr:	134
Title:	Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring
Authors:	Sjoerd van Straten, Alessandro Padella and Marwan Hassani
Abstract:	Predictive Process Monitoring (PPM) enables forecasting future events or outcomes of ongoing business process instances based on event logs. However, deep learning PPM approaches are often limited by the low variability and small size of real-world event logs. To address this, we introduce SiamSA-PPM, a novel self-supervised learning framework that combines Siamese learning with Statistical Augmentation for Predictive Process Monitoring. It employs three novel statistically grounded transformation methods that leverage control-flow semantics and frequent behavioral patterns to generate realistic, semantically valid new trace variants. These augmented views are used within a Siamese learning setup to learn generalizable representations of process prefixes without the need for labeled supervision. Extensive experiments on real-life event logs demonstrate that SiamSA-PPM achieves competitive or superior performance compared to the SOTA in both next activity and final outcome prediction tasks. Our results further show that statistical augmentation significantly outperforms random transformations and improves variability in the data, highlighting SiamSA-PPM as a promising direction for training data enrichment in process prediction.

Short Papers

Paper Nr:	20
Title:	MuC: A Multi-Core Tucker Model with Core Attention
Authors:	Yanhui Zhang, Dong Zhu and Aiping Li
Abstract:	Knowledge graph completion (KGC) aims to infer missing triples from existing data. While tensor decomposition-based models offer strong expressiveness, their large core tensors often cause overfitting. We propose MuC, a multi-core tensor model that factorizes the core tensor into several smaller cores and introduces a core attention mechanism to dynamically select relevant cores based on entity-relation input. To encourage diversity among cores, we design a geometric consistency constraint, and to stabilize multi-path interactions, we generalize embedding norm regularization into channel regularization. Experiments show that MuC improves MRR by 22.83% on FB15k-237 and 21.51% on YAGO3-10 compared to strong baselines. Further analysis of core attention weights and dataset characteristics demonstrates the effectiveness of MuC’s dynamic core selection in enhancing adaptability and expressiveness.

Paper Nr:	28
Title:	Adaptive by Design: Rethinking the MLOC Architecture for Learning Systems
Authors:	Marco Hüller, Roman Küble and Jörg Hähner
Abstract:	Adaptive cyber-physical systems must cope with changing environments while guaranteeing reliable operation. The Multi-Layered Observer/Controller (MLOC) architecture provides a well-established blueprint for such self-organising systems, yet broader adoption is constrained by two key shortcomings: (i) a static, rule-based decision layer that scales poorly with state-space complexity and (ii) a fixed simulator that cannot model unforeseen dynamics. This paper proposes a lightweight redesign of MLOC’s operational and adaptive layers. First, we propose replacing the XCS Classifier System (XCS) in Layer~1 with reinforcement learning agents, which reduces configuration effort and enables continuous control in high-dimensional domains. Second, we suggest a dynamic Layer~2 simulator that is fine-tuned or fully exchanged whenever an anomaly detector signals model drift. The detector differentiates between simulation mismatch and agent uncertainty, triggering simulator updates or focused agent fine-tuning without breaching MLOC’s safety principle of avoiding exploratory actions in the real system. All components are lightweight, making them suitable for platforms with limited resources. Furthermore, depending on the chosen methods, the models may remain interpretable---preserving one of the key advantages of the original XCS. The redesign preserves MLOC’s hierarchical structure and unifies reactive control, long-term learning, and model maintenance, substantially widening its applicability to complex and uncertain environments.

Paper Nr:	30
Title:	CodeASG: An approach for extracting code embeddings from Abstract Syntax Graphs
Authors:	Alexandru-Gabriel Sîrbu and Gabriela Czibula
Abstract:	Embeddings are dense vector representations of data that capture essential features, enabling deep learning models to better understand the structure and semantics of the encoded information and thus enhancing their overall performance. In programming languages, learning models often treat code as a natural language, causing them to learn syntax and semantics implicitly. This paper proposes a new approach to generate source code embeddings based on Abstract Syntax Graphs (ASG), a more compact and less redundant representation than traditional ASTs, particularly at the leaf node level. Thus, a Graph Attention Network model is employed for effectively learning to reconstruct ASGs and internalize structural information. To evaluate the quality of the resulting embeddings, the embedded data is clustered using k-means, DBSCAN, and Spectral Clustering, using cosine similarity as the distance metric. Performance is assessed using silhouette scores, which yield results of 0.063, 0.2, and 0.269, respectively, outperforming those achieved by state-of-the-art models such as CodeBERT and CodeT5 on the same task.

Area 6 - Internet of Things and Digital Twins

Full Papers

Paper Nr:	143
Title:	Impact-Sensitive Conflict Management in Smart IoT-Based Systems Using Attention Networks
Authors:	Christson Awanyo, Nawal Guermouche and Morel Kouhossounon Vianney
Abstract:	Modern IoT environments are increasingly evolving into system-of-systems, where independently managed subsystems interconnect and operate over shared IoT devices and infrastructures. As these heterogeneous systems evolve autonomously, the potential for IoT conflicts rises, particularly when they issue overlapping or competing control requests. This growing complexity underscores the need for a robust, dynamic, and real-time conflict management framework that can adapt to changing contexts and system behaviors. Traditional resolution strategies, such as fixed priorities or first-come-first-served, often fail to consider contextual factors and the effective impact of decisions, resulting in degraded non-functional properties. To address these challenges, we propose an impact-aware, attention-based conflict management framework. Our approach resolves conflicts by jointly considering request importance and their predicted system-level consequences. By leveraging real-time contextual data and historical conflict patterns, the model dynamically selects resolution actions that minimize negative impacts. We demonstrate the effectiveness of this framework through extensive evaluations in a smart transportation scenario, using energy consumption and CO2 emissions as key non-functional metrics.

Short Papers

Paper Nr:	41
Title:	A Hybrid Query Language for Digital Twins
Authors:	Philipp Zech, Manuel Burger, Linus Wald, Philipp Pobitzer, Sascha Hammes and Judith Michael
Abstract:	The full potential of Digital Twins (DTs) is hindered by the challenge of integrating complex engineering models with high-frequency runtime data from disparate sources. Existing approaches lack a unified mechanism to query across the boundary of static models and dynamic data, leading to fragmented and inconsistent DT systems. We introduce a hybrid query language that unifies model and data retrieval, translating queries into SPARQL for model access and SQL for data access within a single declarative statement. Our evaluation demonstrates that this approach overcomes the models-meet-data challenge by enabling scalable, near real-time queries, thereby paving the way for more robust and integrated DT applications.

Paper Nr:	108
Title:	Trial by Twin: Behavior-Predictive Trust in Autonomous Drone Swarms
Authors:	Danish Iqbal, Hind Bangui and Bruno Rossi
Abstract:	To reach the goal of future autonomous mobility of Unmanned Aerial Vehicles (UAVs), a reliable evaluation of runtime trust assurance is essential. This paper proposes a trust-assurance method for autonomous drones using runtime compliance checking through a Digital Twin (DT). The DT encapsulates drone-specific metrics from simulations from the AirSim environment, such as Behavior Functionality Metric, including sensors health, network centrality measures for real-time behavior analysis. Drones collaborate in swarms, exchanging predictive and actual behavior for trust assessment. The trust assessment model is based on a regression stage using Random Forest (RF), Support Vector Regression (SVR), and Convolutional Neural Network (CNN) to estimate swarm coordination rate as a trust indicator. During each iteration, a classification stage assigns trust labels as Trusted or Malicious. We applied the model to an autonomous drone package delivery system under trust-related attacks: Critical Node Attack (CNA), Man-in-the-Middle (MITM), Data Manipulation Attack (DMA), and Sybil Attack (SA). Overall, the experimentation showed how SVR can be adopted to estimate the swarm coordination rates under adversarial conditions, while RF and SVM can be used to classify the trustworthiness of drones during each iteration.

Area 7 - Knowledge Graphs, Data, Information, and Knowledge Engineering

Full Papers

Paper Nr:	60
Title:	Extracting Object-Centric Event Logs from Incident Data Using Large Language Models
Authors:	Ahmed Takiy Eddine Hamdi, Marwa Elleuch, Nassim Laga and Walid Gaaloul
Abstract:	Incident monitoring is critical in industrial settings to prevent disruptions and optimize operations. While traditional equipment logs are often converted into XES-like event logs, these formats typically associate each event with a single case object and overlook valuable information from other sources, particularly, pre- and post-incident process logs. These additional logs frequently describe activities involving multiple related objects (e.g., hardware, software). OCEL (Object-Centric Event Log) standard can be used to represent events involving multiple, interconnected objects, thus offering a more comprehensive view of incident-related processes. However, pre- and post-incident data are often recorded in unstructured textual formats, whereas OCEL requires well-structured data in order to be properly populated. To bridge this gap, we introduce a method to extract events and objects from unstructured pre- and post-incident textual content that leverages Large Language Models (LLMs). Our approach is evaluated on real-world data from the data center domain demonstrating its effectiveness in enriching incident monitoring and providing a structured foundation for advanced incident prediction and analysis.

Paper Nr:	66
Title:	Assessing the Stability of Rankings in Knowledge Graphs Against Perturbations
Authors:	Hassan Abdallah, Béatrice Markhoff, Louise Parkin and Arnaud Soulet
Abstract:	Knowledge graphs (KGs) such as Wikidata serve as valuable resources for structuring and analyzing information across various domains. However, their crowdsourced nature makes them vulnerable to perturbations, including both intentional vandalism and unintentional errors, which can significantly impact rankings derived from these graphs. While previous studies have primarily focused on detecting and preventing entity-level perturbations, this paper investigates the potential impact of such perturbations on the stability of rankings at the structural level, specifically targeting relationships. We formalize the problem of ranking stability under perturbations, and we propose a probabilistic model to assess the likelihood of modifications to knowledge graph relationships changing the ranks of entities. We leverage complex network analysis to evaluate ranking vulnerabilities. Our experimental study demonstrates varying levels of resilience in rankings depending on entity degree distributions and the nature of perturbations.

Paper Nr:	89
Title:	Incremental Synchronization of BPMN Models and Documentations by Leveraging Structural Algorithms and LLMs
Authors:	David Cremer, Benjamin Dalmas, Quentin Nivon and Gwen Salaün
Abstract:	Maintaining consistency between business process models and their textual descriptions is critical for operational clarity, compliance, and communication. However, as process models evolve, updating documentation remains costly and error-prone. Existing methods often require manual rewriting or full text regeneration, discarding valuable domain-specific language. This paper presents an edit-based synchronization approach that incrementally updates textual descriptions to reflect changes in Business Process Model and Notation (BPMN) diagrams while preserving unaffected content. We propose two complementary algorithms: the Longest Common Execution Subsequence (LCES) approach for balanced acyclic models, and a heuristic beam search for more complex structures with loops and unbalanced gateways. The resulting transformation steps are translated into structured prompts guiding a large language model to produce minimal, style-consistent revisions. A prototype system demonstrates high semantic accuracy and stylistic coherence across diverse process evolution scenarios.

Short Papers

Paper Nr:	22
Title:	Embedding-Based Ontology Term Recommendation System for FAIR Data Publishing Workflows
Authors:	Nan Liu, Mohamed-Anis Koubaa, Andreas Schmidt, Karl-Uwe Stucky, Wolfgang Suess and Veit Hagenmeyer
Abstract:	FAIR (Findable, Accessible, Interoperable, and Reusable) data publications are important for enabling open energy research across interdisciplinary domains. The realization of the FAIR principle for data still faces many challenges, such as the diversity of data formats, semantic heterogeneity, lack of formalized ontologies, and error-prone manual annotation of data. These challenges impede the effective sharing and integration of energy data. To address these issues, we propose an automated ontology term recommendation system based on ontology embedding and semantic similarity, aiming to facilitate FAIR data publication in energy research. Our recommendation system utilizes contextual embeddings to automate semantic annotation of heterogeneous energy datasets by linking data elements to predefined energy-specific ontologies and referring to the top-K most relevant ontology concepts. The proposed system is designed to significantly streamline the semantic annotation process for energy researchers, thereby accelerating the process of open energy research. For evaluation, we test our system on the SemTab Challenge 2024 dataset provided by the Ontology Alignment Evaluation Initiative (OAEI). The experimental results show that our system improves the matching accuracy significantly compared to the baseline system.

Paper Nr:	54
Title:	Lazy Prediction in Querying Graph Databases
Authors:	Jacques Chabin, Cristina D. Aguiar, Mirian Halfeld-Ferrari, Martin A. Musicante and Lingchen Wang
Abstract:	This work focuses on querying incomplete data graphs using a hybrid, query-driven approach. The method combines the certainty of known paths with the flexibility of on-the-fly embedding-based link prediction guided by the query structure. A lazy-prediction strategy is used to improve relevance while limiting unnecessary inference. We evaluate our method on two embedding-based models and datasets, and test predictive strategies with different search space reductions.

Paper Nr:	63
Title:	Ontology-Guided Chain-of-Thought Reasoning for Knowledge Graph Construction with Large Language Model
Authors:	Gang Xiao, Wenhui Li, Jiawei Lu and Siyu Chen
Abstract:	To address the challenges of traditional knowledge graph construction (KGC) methods in terms of labor cost, accuracy and automation, this paper proposes a framework AdaOntoKG that combines ontology-driven and adaptive chain-of-thought reasoning (Adaptive CoT). The method first constructs a domain on-tology based on domain boundaries, requirements, and data characteristics, and subsequently expands it automatically using LLMs. Then, in the knowledge extraction phase, AdaOntoKG establishes a feedback mechanism using Zero-Shot-CoT and Few-Shot-CoT to guide the triple extraction process. To ensure semantic consistency and structural validity, ontology constraints are applied for filtering and validation. Experiments are conducted on multiple benchmark datasets. The results show that AdaOntoKG generally outperforms mainstream baseline models in terms of F1 and AUC scores. Moreover, its performance on the ontology conformance metric demonstrates strong structural regularity and semantic consistency. In summary, AdaOntoKG demonstrates significant ad-vantages in accuracy, controllability, and domain adaptability, providing a general and scalable solution for building high-quality knowledge graphs.

Paper Nr:	65
Title:	Automated Model/Schema Interoperability with ML/AI Approaches: A Survey
Authors:	Joshua Tetteh Ocansey, Yngve Lamo, Adrian Rutle, Fazle Rabbi and Bahareh Fatemi
Abstract:	Automated model interoperability plays a pivotal role in managing heterogeneous artefacts across software engineering and data-intensive domains. While conventional approaches often depend on manual or rule-based methods, recent advances in machine learning (ML) and artificial intelligence (AI) present promising avenues for automating interoperability tasks such as matching, mapping, and alignment. However, existing surveys rarely provide a systematic account of how ML/AI techniques address structural and semantic heterogeneity. This paper presents a tertiary study and a critical synthesis of recent developments in AI-driven model interoperability. From a corpus of 82 contributions identified in 19 secondary studies, we selected and classified 19 primary papers that explicitly apply ML/AI methods. Using an extended feature model, we analyze these works along multiple dimensions, including artefact management, execution context, and learning techniques. We also identify key research challenges in explainability, generalization, transformation semantics, and conformance validation, offering a foundation for future research and tool development.

Paper Nr:	109
Title:	Path-Based Explanations for Knowledge Graph-Driven Course Recommendation
Authors:	Nadia Ben Hadj Boubaker, Zahra Kodia and Nadia Yacoubi Ayadi
Abstract:	Recommendation systems (RS) play a key role in e-learning by guiding learners toward relevant educational resources. Yet, the integration of domain knowledge to enhance both accuracy and explainability remains underexplored. This paper presents an explainable e-learning RS grounded in an educational Knowledge Graph (KG). The KG is constructed by extracting and linking key course concepts, and leveraged through embedding techniques to improve recommendation quality. To ensure transparency, we propose a path-based explanation mechanism that identifies and ranks user–course connections using a scoring function combining similarity measures and random walk probabilities. A case study demonstrates that the approach not only improves recommendation accuracy but also generates diverse, interpretable explanations, contributing to more transparent and trustworthy systems.

Area 8 - Process Analytics and Technology

Full Papers

Paper Nr:	47
Title:	eRooMiner: A Data-Driven Approach for Root Cause Detection of Process Data Quality Issues
Authors:	Shokoufeh Ghalibafan, Sareh Sadeghianasl and Moe T. Wynn
Abstract:	Process mining relies on high-quality event logs for accurate analysis and decision-making. However, real-life event logs suffer from various types of data quality issues. Existing solutions are retroactive, detecting and repairing data quality issues after their occurrence in event logs. A more permanent and cost-effective solution would be to prevent these issues proactively, “prevention is better than the cure”. This paper proposes a data-driven approach (eRooMiner) for identifying root causes of typical data quality issues in event logs to facilitate their prevention. The eRooMiner approach builds upon the typical data quality issues identified in the collection of eleven event log imperfection patterns, with a focus on imperfect labels which have the same meaning but different syntax. By learning from the data recorded in event logs, eRooMiner bridges the gap between theoretical and data-driven root cause analysis of event log imperfection patterns. The approach utilises machine learning and AI techniques to estimate the most probable root cause of specific data imperfections. The approach has been implemented and evaluated using real-life event logs and stakeholders. The results show that the proposed approach can correctly detect the potential root cause(s) of imperfect labels in various scenarios.

Paper Nr:	48
Title:	Predicting Case Suffixes with Activity Start and end Times: A Sweep-Line Based Approach
Authors:	Muhammad Awais Ali, Marlon Dumas and Fredrik Milani
Abstract:	Predictive process monitoring techniques support the operational decision-making by predicting future states of ongoing cases of a business process. A subset of these techniques predict the remaining sequence of activities of an ongoing case (case suffix prediction). Existing approaches for case suffix prediction generate sequences of activities with a single timestamp (e.g. the end timestamp). This output is insufficient for resource capacity planning, where we need to reason about the periods of time when resources will be busy performing work. This paper introduces a technique for predicting case suffixes consisting of activities with start and end timestamps. In other words, the proposed technique predicts both the waiting time and the processing time of each activity. Since the waiting time of an activity in a case depends on how busy resources are in other cases, the technique adopts a sweep-line approach, wherein the suffixes of all ongoing cases in the process are predicted in lockstep, rather than predictions being made for each case in isolation. An evaluation on real-life and synthetic datasets compares the accuracy of different instantiations of this approach, demonstrating the advantages of a multi-model approach to case suffix prediction.

Paper Nr:	59
Title:	BPMN Patterns for Process Variability
Authors:	Philipp Hehnle and Manfred Reichert
Abstract:	Frequently, variants of the same business process are run in different organisations. Approaches have been proposed that allow managing process variants and reusing process models, i.e. changes can be applied centrally instead of applying them to each process variant. However, these approaches often extend modelling notations requiring proprietary tools. Therefore, we present modelling patterns for different types of variability in processes as solutions for this recurring problem by solely relying on standard BPMN 2.0, i.e. common BPMN 2.0 modelling tools and process engines can be used. As part of the evaluation, two processes encompassing variability were implemented and executed on two process engines demonstrating the technical soundness of the patterns. In addition, results from expert interviews indicate that the patterns are useful, relevant, and easy to use.

Paper Nr:	91
Title:	A Framework for Assessing Overcompliance and Undercompliance in Business Processes
Authors:	Johannes Loebbecke and Stefanie Rinderle-Ma
Abstract:	Ensuring process compliance is critical for companies and organizations to avoid regulatory penalties, financial losses, and reputational damage. As a result, research has focused on detecting non-compliant instances where processes deviate from rules imposed on their execution and formalizing legal documents to enable automatic compliance verification. However, a crucial but overlooked phenomenon is overcompliance, where processes exceed what is required by regulations or policies, potentially leading to inefficiencies, increased operational costs, or overly rigid processes. While the reasons why companies overcomply with regulations have been extensively analyzed in environmental economics, overcompliance has been largely disregarded in process compliance, where compliance is often categorized as either violated or satisfied, and, at most, the degree of undercompliance is considered. We propose a formal framework for evaluating a degree of compliance, which specifically highlights the differences between perfect compliance, (maximal) undercompliance, and (maximal) overcompliance. Furthermore, we present a technique for estimating the costs associated with over and undercompliance. The approach is prototypically implemented and evaluated using real-world and synthetic event logs.

Paper Nr:	135
Title:	Unsupervised Hierarchical Process Mining with the Process Fragment Miner
Authors:	Joern Tobis, Felix Schumann, Juergen Mangler and Stefanie Rinderle-Ma
Abstract:	Abstracting processes into a hierarchical structure of subprocesses helps to improve the understandability and readability of process models. Mining such hierarchical process models from event logs is an active subfield in process mining research. Existing approaches often require activities to be labeled with a hierarchy notion or context data to identify hierarchies. In this work, we propose the ProcessFragmentMiner (PFM) to fragment an event log into subprocesses that represent the root process. PFM harnesses the dependency matrix generated by the heuristics miner in combination with different fragment ranking mechanisms. This work uses the inductive and split miner to mine the resulting process models for each fragment. PFM is evaluated against a supervised and an unsupervised hierarchical process mining approach from the literature. We find that PFM works best in combination with the presented Bigram ranking method and can match supervised approaches for some data sets. The proposed PFM approach enables hierarchical process mining on any event log without the need for any preprocessing steps.

Paper Nr:	136
Title:	Discriminative Rule Learning for Outcome-Guided Process Model Discovery
Authors:	Ali Norouzifar and Wil van der Aalst
Abstract:	Event logs extracted from information systems offer a rich foundation for understanding and improving business processes. In many real-world applications, it is possible to distinguish between desirable and undesirable process executions, where desirable traces reflect efficient or compliant behavior, and undesirable ones may involve inefficiencies, rule violations, delays, or resource waste. This distinction presents an opportunity to guide process discovery in a more outcome-aware manner. Discovering a single process model without considering outcomes can yield representations poorly suited for conformance checking and performance analysis, as they fail to capture critical behavioral differences. Moreover, prioritizing one behavior over the other may obscure structural distinctions vital for understanding process outcomes. By learning interpretable discriminative rules over control-flow features, we group traces with similar desirability profiles and apply process discovery separately within each group. This results in focused and interpretable models that reveal the drivers of both desirable and undesirable executions. The approach is implemented as a publicly available tool and it is evaluated on multiple real-life event logs, demonstrating its effectiveness in isolating and visualizing critical process patterns.

Paper Nr:	137
Title:	Agentic Generation of Process Models from Regulatory Texts
Authors:	Catherine Sai and Stefanie Rinderle-Ma
Abstract:	Process model generation is still mostly a manual task. A vast amount of process-relevant information is captured in textual sources such as regulatory documents and process descriptions. Hence, (semi-) automatic extraction of process model information from textual sources and translation into process models based on graphical notations such as BPMN are ongoing and, with the advent of generative AI, increasingly performant. However, existing approaches for process model generation from text are often limited to control flow aspects and require precise process descriptions as input. Regulatory documents describing processes pose different challenges than process descriptions and are widespread, highly relevant and of increasing volume in organizations. In this work, we exploit a generative AI architecture to present an approach that can automatically generate BPMN 2.0 process models from regulatory texts such as the GDPR. The approach is evaluated on five use cases with regulatory text and corresponding process models from different domains and with different challenges. This way, we can demonstrate that the proposed approach can automatically generate reference models from regulatory requirement documentations. When compared to existing approaches, the proposed approach results in an improvement of both, syntactic and semantic process model quality.

Paper Nr:	138
Title:	Out of Babylon: Object-Centric Conformance Checking on Graph-Based Abstractions
Authors:	Erik Wrede, Jan Niklas van Detten, Lukas Liss and Sander J. J. Leemans
Abstract:	Process mining employs data-driven techniques to analyse and optimise business processes. An important step in such an analysis is to identify and quantify deviations between the observed and modelled behaviour of a business process using conformance checking techniques. While traditional conformance checking approaches utilise process mod- els that primarily focus on events, recently proposed approaches also consider the involvement of business objects. These object-centric pro- cess models introduce new behavioural deviations due to the involve- ment of objects in events like missing objects in the execution of an event. However, existing object-centric conformance checking techniques do not precisely detect all these deviations and can only be applied to specific object-centric modelling formalisms. In this paper, we introduce a novel formalism-agnostic object-centric conformance checking approach that presents a common denominator for many object-centric modelling formalisms, enabling comparable conformance checking results. We eval- uate our approach on a public object-centric log by demonstrating its ability to detect deviations across multiple modelling formalisms.

Short Papers

Paper Nr:	44
Title:	Orchestrating Cyber-Physical Operations with PRiME: A Passive Resource-Integrated Modeling Extension for BPMN
Authors:	Leo Poss and Stefan Schönig
Abstract:	The convergence of the Internet of Things (IoT) and Business Process Management (BPM) in cyber-physical systems highlights a critical limitation in current standards: the inability to effectively model and manage the operational lifecycle of physical resources. Standard BPMN lacks native constructs to differentiate between consumable materials, reusable tools, and generic data in the form of DataObjects, creating a digital-physical divide that hinders execution support and process transparency. This paper addresses this gap by presenting PRiME (Passive Resource-integrated Modeling Extension), a comprehensive and executable BPMN extension developed following the Design Science Research methodology. PRiME provides explicit constructs to model passive resource requirements, inventories, and physical flows while distinguishing between fungible materials and specific tool instances. The extension is validated through a prototype, demonstrating two key capabilities: providing real-time operational support through automated resource checklists and shortage alerts, and enabling analysis through a design-time visualization of material flows. This concept helps bridge the gap between digital process models and physical resource orchestration, fostering a more holistic BPM that enables the support of manual tasks in cyber-physical environments.

Paper Nr:	112
Title:	Enhancing Suitable Tasks Selection for RPA Through Fuzzy AHP and AI Methods
Authors:	Imen Korâani, Wiem Chebil and Sonia Ayachi Ghannouchi
Abstract:	Currently, increasingly more attention is devoted to the digitization of operations and busi- ness processes in companies to provide more value to their customers. In order to efficiently perform low-value tasks and manage better their time, organizations started to adopt technological advancements, such as Robotic Process Automation (RPA), whose goal is to transform their internal operations, to optimize their operations, and reduce the burden of repetitive, low-value tasks. RPA improves productivity by automating structured processes, but implementation of RPA in the incorrect process would not benefit the organization. To harness these benefits, organizations face the challenge of identifying pro- cess activities which are viable automation candidates for RPA. To sum up, the goal of this work is to propose a richer RPA lifecycle by integrating Multi-Criteria Decision Analysis (MCDA), and Artificial Intelligence (AI). MCDA, and more specifically the fuzzy analytical hierarchy process (Fuzzy AHP), offers a structured method for prioritizing automation candidates based on criteria while handling uncertainty in expert evaluations. AI further extends RPA’s capabilities to more complex and unstructured tasks, enabling intelligent, adaptive automation. By combining these three approaches, we propose a smarter, data-informed framework to guide automation strategies and maximize the impact of RPA within organizations.

Paper Nr:	128
Title:	Accurate and Noise-Tolerant Extraction of Routine Logs in Robotic Process Automation
Authors:	Massimiliano de Leoni, Faizan Ahmed Khan and Simone Agostinelli
Abstract:	Robotic Process Mining focuses on the identification of the routine types performed by human resources through a User Interface. The ultimate goal is to discover routine-type models to enable robotic process automation. The routine-type model discovery requires the provision of a routine log. Unfortunately, the vast majority of existing works do not directly focus on enabling the model discovery, limiting themselves to extracting the set of actions that are part of the routines. They were also not evaluated in scenarios characterized by inconsistent routine execution, referred to hereafter as noise, which reflects the natural variability and occasional errors in human performance This paper puts forward a clustering-based technique that aims to extract routine logs. Experiments were conducted on nine UI logs from the literature with different levels of injected noise. Our technique was compared with existing techniques, most of which are not meant to discover routine logs but were adapted for the purpose. The results were evaluated through standard state-of-the-art metrics, showing that we can extract more accurate routine logs than what the state of the art could, especially in presence of noise.

Area 9 - Semantic Interoperability and Open Standards

Short Papers

Paper Nr:	131
Title:	Performance Improvement for an Intensive SPARQL-Based Application: The ERA Route Compatibility Check Tool
Authors:	Daniel Doña, Oscar Corcho and Edna Ruckhaus
Abstract:	This paper addresses performance challenges in the European Union Agency for Railways (ERA) Route Compatibility Check (RCC) tool, a system that intensively uses SPARQL to query a large-scale knowledge graph. The application determines if a vehicle type is compatible with a railway route's characteristics, but a naïve approach of checking all route tracks leads to poor performance. To resolve this, we implemented and evaluated two primary optimisation strategies: track aggregation and query parallelization. Track aggregation groups tracks with identical technical parameters to drastically reduce the number of evaluations during query execution, while parallel execution maximizes the throughput of the SPARQL endpoint. Our evaluation, comparing the optimised strategy against the naïve baseline, demonstrates a significant, approximately five-fold improvement in execution time for long routes. This work shows that combining data summarization with parallel execution is an effective approach for making SPARQL-intensive applications on large knowledge graphs performant and responsive.

Area 10 - Services and Cloud in Information Systems

Full Papers

Paper Nr:	111
Title:	Prompting Strategies for LLM-Based Cooperative Data Service Discovery
Authors:	Devis Bianchini, Massimiliano Garda, Michele Melchiori and Anisa Rula
Abstract:	In the context of Smart Manufacturing and the Internet of Production, data service discovery plays a central role in enabling cross-organizational collaboration and data-driven innovation. Nevertheless, effective discovering and composition of data services often require deep technical knowledge, limiting the autonomy of domain experts and R\&D managers in designing analytics workflows. This paper presents a cooperative approach to data service discovery that combines Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), leveraging a conceptual model of data services and analytics scenarios. On top of this model, a set of prompting strategies are designed to support different levels of user expertise and interaction goals. These strategies leverage the cooperative nature of the approach, enabling domain users to incrementally build, extend, and refine analytics service pipelines through the interaction with LLMs. We describe how these prompting strategies are tightly integrated with the RAG component to inject contextual knowledge derived from a catalog of atomic data services and analytics scenarios. The system is implemented using open-source technologies and evaluated extensively in a real-world smart factory case study. Our evaluation includes both quantitative metrics (precision, recall, faithfulness, factual correctness) and a qualitative user study, demonstrating the effectiveness of prompting strategies and the feasibility of LLM-supported service discovery in cooperative industrial settings.

Area 11 - Software Engineering for Information Systems

Full Papers

Paper Nr:	49
Title:	Automated Synthesis of Kubernetes Variability from OpenAPI Schemas
Authors:	Brian Flores, José Miguel Horcas, Mercedes Amor and Lidia Fuentes
Abstract:	Kubernetes (K8s) has emerged as the de facto standard for orchestrating containerized applications, offering extensive configurability to meet diverse deployment needs. However, this flexibility introduces significant complexity, leading to steep learning curves and a high propensity for configuration errors due to the manual navigation of vast documentation. While variability modeling techniques has proven successful in software product lines (SPLs) to manage such configuration complexity, its application in infrastructure systems such as K8s remains limited. This paper presents an automated approach for synthesizing a comprehensive K8s variability model directly from its official OpenAPI schemas. We demonstrate that this automatically generated model offers broader coverage of the configuration space than a manually constructed model previously developed from the API documentation. Furthermore, we evaluate the model's effectiveness in configuration validation against 250,000 real-world K8s configurations, comparing it with existing K8s validation tools. Our approach achieves 94.8% configuration validity, offering comprehensive structural and semantic checks that go beyond schema compliance or policy enforcement provided by other tools.

Paper Nr:	132
Title:	Data Integrity-by-Design: Combining Declarative Object-Centric Choreographies and Entity Relationship Models
Authors:	Tilman Zuckmantel, Hugo A. López, Yongluan Zhou, Boris Düdder and Thomas T. Hildebrandt
Abstract:	This work proposes a novel technique to align Object-Centric Process models and data integrity constraints from Entity-Relationship (ER) Diagrams, guaranteeing data-integrity-by-design in the execution of a business process. Recent developments in process modelling and mining notations have highlighted how critical data objects affect the evolution of process models. Yet, few works have considered the integration of data objects in object-centric modelling notations. As a consequence, object-centric process models (OCPM) may violate data-integrity and existential dependency constraints that are necessary for the deployment of models in information systems. We examine how OCPM can integrate access control, cardinality constraints, existential dependency, total participation, and structural integrity. In particular, we extend the semantics of one OCPM language, the Object-Centric Condition Response Choreographies (OC-DCR choreographies), to include explicit identities of actors and object deletion and extend the execution semantics to respect the ER model constraints. We illustrate the idea with a running example of an OC-DCR graph and an ER model for a small healthcare process.

Short Papers

Paper Nr:	34
Title:	A Systematic Literature Review on Software Visualization Tools for Program Comprehension
Authors:	Stefan-Octavian Custura
Abstract:	As software systems are continuously being developed and more and more programmers are working on a project, program comprehension is a topic that should be analyzed thoroughly to help the industry accommodate changes for engineers, regardless of experience or age. This way, we tackled the field of Software Visualization and analysed its characteristics in the recent literature through a Systematic Literature Review, where we investigated what and how software is visualized, and how such tools can be empirically validated. Our study notes a mass use of three different approaches for software visualization, while also discovering representations in both graphical and numerical forms. This study also gathers information and views on where software visualization can expand with the current use of artificial intelligence in the day-to-day processes of industry software engineering. Our research shows that current software visualization tools are mainly based on the same three topics, Code Metrics, Dependencies, and Code Changes, but are starting to expand in different directions, tackling more specific issues as the software development field is quickly growing, offering a development perspective towards new or combined approaches of how can program comprehension be enhanced.

Paper Nr:	78
Title:	Automated Duplicate Bugs Detection: Do We Really Need all Bug Report Sections?
Authors:	Lobna Ghadhab, Ilyes Jenhani, Montassar Ben Messaoud and Mohamed Wiem Mkaouer
Abstract:	Duplicate bugs pose a significant challenge that consumes substantial resources and can complicate the bug triage process, requiring extra work to identify and merge duplicates. Several automated duplicate bug detection methods use Natural Language Processing to handle this problem. Bug reports are often long and contain multiple sections that can show some textual (dis)similarities. This disparity may affect the duplicate bug detection process and hence results in inefficient resource utilization. In this work, we study the impact of bug report sections on the detection of duplicates especially when these sections show some (dis)similarities. Filtering out the most pertinent sections can greatly alleviate computational load and reduce the chances of overlooking potential duplicate bugs. Using less sections would also reduce the cost of the duplicate bug detection system as less tokens may be used. To achieve our objective, we developed and analyzed two types of models. One section-based models are used to analyze the individual impact of bug report sections, whereas cross section-based models are used to analyze their collective impact. These models leverage a siamese network constructed from pretrained DistilRoBERTa [1] and fine-tuned for classification by the Multi-Layer Perceptron (MLP). Our findings reveal that the "title" and "description" sections show the highest relevance in duplicate bug detection, achieving f1-scores of 98.93% and 98.29% respectively. Conversely, the "steps to reproduce" and "actual results" sections tend to cause confusion when distinguishing between duplicate reports, which often results in a high misclassification rate.

Paper Nr:	93
Title:	Creation and Termination Patterns for Managing Processes with Board-Based Collaborative Tools
Authors:	Alfonso Bravo, Adela del-Rio-Ortega, Joaquín Peña and Manuel Resinas
Abstract:	Board-Based Collaborative Work Management Tools (BBTs), such as Trello, Planner, and Asana, are a form of cooperative information system that are widely used to support collaborative process coordination, task management, and knowledge sharing. However, designing effective boards can be challenging, particularly for non-technical users. Prior research has addressed this by identifying structural design patterns and a metamodel for board design, but the focus has primarily been on card movement as the primary action shaping board usage. This paper extends that work by analyzing the role that card and list creation and termination plays in real-world BBTs. Based on this analysis, we contribute (1) a set of creation and termination patterns reflecting common usage behaviors, and (2) an extension to the existing metamodel that accounts for these actions. These contributions support the design of more effective and adaptable boards and provide a richer understanding of how boards are actually used in practice