CoopIS-DT 2025 Abstracts

Area 1 - Knowledge Graphs, Data, Information, and Knowledge Engineering
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Area 2 - Process Analytics and Technology
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Area 3 - Large Language Models, Generative AI, and Retrieval Augmented Generation in Information Systems
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Area 1 - Knowledge Graphs, Data, Information, and Knowledge Engineering

Full Papers
Paper Nr: 11
Title:

GRAPE: A Guiding RML Authoring Projectional Editor in Action and User’s First Impressions
Authors:

Jakub Duchateau and Christophe Debruyne
Abstract: RDF Mapping Language (RML) facilitates Knowledge Graph construction but presents significant authoring challenges. GRAPE, an open-source projectional editor, aims to simplify RML authoring. GRAPE offers a guided, text-like interface preventing syntax errors while maintaining RDF flexibility. We showcase its core features, including its language-oriented design and specialized notation. A user study with 20 MSc students revealed diverse user preferences: some felt reassured by the guided approach, while others preferred traditional text editors. These findings inform future GRAPE development.

Area 2 - Process Analytics and Technology

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Paper Nr: 6
Title:

One Engine to Rule Them All: Executing Declarative Processes with CEP
Authors:

Leo Poss and Stefan Schönig
Abstract: We present a novel tool that directly executes MP-Declare process models on standard Complex Event Processing (CEP) engines, removing the need for middleware. By tightly integrating Business Process Management (BPM) and CEP, it supports flexible, declarative process execution over high-frequency IoT data. Its core innovation is a multi-level event abstraction framework that translates declarative constraints into optimized, executable CEP queries. This demonstrates how a unified engine paradigm simplifies architecture, significantly reduces latency, and enables the real-time enactment of event-driven processes, effectively bridging the abstraction gap between low-level events and high-level business logic, offering a scalable and responsive solution for process-aware systems.
Paper Nr: 8
Title:

Data Asset Analyzer: A Feature-Wise Evaluation System
Authors:

Vanilson Burégio, Belkacem Chikhaoui, Zakaria Maamar and Amel Benna
Abstract: This paper presents an implementation of the eligibility stopover featuring the data assetization journey framework. A feature-wise eligibility scoring model is introduced allowing to assess datasets’ potential for assetization. The model evaluates data quality through completeness and uniqueness metrics while considering assetization efforts through missingness and preprocessing costs. The system is associated with an interactive Streamlit-based tool that provides real-time visualization of eligibility scores and dynamic data quadrants that is updated based on user-defined thresholds. The system’s flexible interface also allows users to adjust scoring parameters and thresholds, enabling customized assessment based on specific organizational priorities and data characteristics. The evaluation shows that the system effectively identifies datasets with high assetization potential while providing immediate visual feedback on how different parameters affect eligibility assessment.
Paper Nr: 12
Title:

BPFragmentODRL: A Web Tool for Generating ODRL Policies for Business-Process Fragments
Authors:

Abderrahmane Maaradji and Zakaria Maamar
Abstract: As organizations increasingly adopt distributed process execution models and cloud-based architectures, business processes are often decomposed into fragments that execute across different environments and systems. However, managing governance policies for these fragments remains a significant challenge, as existing approaches focus primarily on process-level policies without addressing the specific requirements of individual fragments. This paper presents BPFragmentODRL, an online tool for fragment policy generation in business processes that addresses this critical gap. The tool enables users to upload BPMN process models and automatically generate ODRL-compliant policies for process fragments using both template-based and LLM-based techniques. BPFragmentODRL supports multiple fragmentation strategies and provides policy consistency checking and metrics analysis. By enabling fine-grained governance at the fragment level, the tool facilitates compliance management, reduces policy conflicts, and supports the practical deployment of distributed business processes in modern enterprise environments.

Area 3 - Large Language Models, Generative AI, and Retrieval Augmented Generation in Information Systems

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Paper Nr: 5
Title:

AQG4SD: Automated GraphQL Query Generation for Cloud Service Discovery and Selection
Authors:

Yann Ramusat, Vincent Tran, Sébastien Nguyen and Zakaria Maamar
Abstract: This paper demonstrates AQG4SD (Automated Query Generation for Service Discovery), an interactive tool that automates the discovery and selection of cloud services from user requirements defined in natural language. Leveraging Large Language Models (LLMs) and automated GraphQL query generation, the tool outperforms existing solutions by dynamically binding real-time public public cloud pricing APIs. AQG4SD has the capacity of translating user requirements into actionable GraphQL queries, even when confronted to APIs not natively optimized for complex selection criteria. Preliminary experimental results are promising demonstrating the robustness of AQG4SD along with its suitability for an efficient and automated cloud resource management.