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Department of History and Welsh History at Aberystwyth University, United Kingdom
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9 Papers

GLIMPSE-Med: Single-Screen Visualization of Multivariate Time Series for a Single Individual

Topics: Biomedical Visualization and Applications; Dynamic or Temporal Data Visualisation; Visual Analytics; Multi-dimensional or Multivariate data visualisation

Authors: Hugo Le Baher 1 ; 2 ; 3 , Jérôme Azé 1 , Sandra Bringay 4 ; 1 , Pascal Poncelet 1 , Arnaud Sallaberry 1 ; 4 and Caroline Dunoyer 3 ; 5

Affiliations: 1 LIRMM, UMR 5506, University of Montpellier, CNRS, Montpellier, France , 2 5 DEGRÉS, Paris, France , 3 Health Data Science Unit, Public Health Service, Centre Hospitalier Universitaire de Montpellier, Montpellier, France , 4 AMIS, Paul-Valéry University of Montpellier, Montpellier, France , 5 Desbrest Institute of Epidemiology and Public Health, UMR UA11, University of Montpellier — Inserm, Montpellier, France

Digital Object Identifier: 10.5220/0013373300003912

Proceedings of IVAPP 2025, ISBN: 978-989-758-728-3

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The widespread digitization of hospital information systems is paving the way for the integration of interactive visualization methods into decision support systems. This progress enhances the ability to anticipate critical risks in monitored patients and alleviates the workload of healthcare providers. However, Electronic Health Records (EHRs) encompass large, heterogeneous, and temporal records, making it a significant challenge to develop tools that enable effective understanding trajectories embedded in these complex data. We introduce GLIMPSE-Med, an interactive timeline-based visualization interface for temporal and heterogeneous events in the EHR, incorporating a score generated by a predictive model. The evaluation of this interface, conducted with healthcare professionals, confirmed that it meets two essential needs: (1) Assess the quality of data collected in an EHR ; (2) Estimate the patient’s condition over time.

Multivariate Time Series Visualization for a Single Individual: A Scoping Review Using PRISMA-ScR

Topics: Biomedical Visualization and Applications; Dynamic or Temporal Data Visualisation; Visual Analytics; Multi-dimensional or Multivariate data visualisation

Authors: Hugo Le Baher 1 ; 2 ; 3 , Jérôme Azé 1 , Sandra Bringay 4 ; 1 , Pascal Poncelet 1 , Arnaud Sallaberry 4 ; 1 and Caroline Dunoyer 2 ; 5

Affiliations: 1 LIRMM, UMR 5506, University of Montpellier, CNRS, Montpellier, France , 2 Health Data Science Unit, Public Health Service, Centre Hospitalier Universitaire de Montpellier, Montpellier, France , 3 5 DEGRÉS, Paris, France , 4 AMIS, Paul-Valéry University of Montpellier, Montpellier, France , 5 Desbrest Institute of Epidemiology and Public Health, UMR UA11, University of Montpellier — Inserm, Montpellier, France

Digital Object Identifier: 10.5220/0013373000003912

Proceedings of IVAPP 2025, ISBN: 978-989-758-728-3

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The digitization of hospital information systems is becoming widespread, enabling the increasing integration of interactive visualization methods into decision support systems. This development facilitates the anticipation of critical risks in monitored patients and helps reduce the workload of healthcare providers. However, Electronic Health Records (EHRs) contain large, heterogeneous, and temporal data. Then, providing tools to understand these complex data is a challenge. Using PubMed and Google Scholar, we conducted a search for articles using keywords related to time, visualization, and data. Out of 3,197 retrieved articles, we identified 111 relevant ones through clustering. Applying exclusion criteria to focus on implemented prototypes, we manually annotated 21 articles for our review. This exploratory literature analysis reveals that while this research area has garnered recent interest, it demonstrates limitations in the proposed solutions. Few approaches employ temporal axis distortion, and no approach in the medical domain visually integrates model predictions. The study highlights preferred functionalities for the visual representation of multivariate temporal data, such as parallel time series and hierarchical views.

Variability-Driven User-Story Generation Using LLM and Triadic Concept Analysis

Topics: Requirements Engineering; Software Product Line Engineering

Authors: Alexandre Bazin 1 , Alain Gutierrez 1 , Marianne Huchard 1 , Pierre Martin 2 ; 3 and Yulin Zhang 4

Affiliations: 1 LIRMM, Univ. Montpellier, CNRS, Montpellier, France , 2 AIDA, Univ. Montpellier, CIRAD, Montpellier, France , 3 CIRAD, UPR AIDA, F-34398 Montpellier, France , 4 EPROAD, Université de Picardie Jules Verne, Amiens, France

Digital Object Identifier: 10.5220/0013360500003928

Proceedings of ENASE 2025, ISBN: 978-989-758-742-9

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A widely used Agile practice for requirements is to produce a set of user stories (also called “agile product backlog”), which roughly includes a list of pairs (role, feature), where the role handles the feature for a certain purpose. In the context of Software Product Lines, the requirements for a family of similar systems is thus a family of user-story sets, one per system, leading to a 3-dimensional dataset composed of sets of triples (system, role, feature). In this paper, we combine Triadic Concept Analysis (TCA) and Large Language Model (LLM) prompting to suggest the user-story set required to develop a new system relying on the variability logic of an existing system family. This process consists in 1) computing 3-dimensional variability expressed as a set of TCA implications, 2) providing the designer with intelligible design options, 3) capturing the designer’s selection of options, 4) proposing a first user-story set corresponding to this selection, 5) consolidating its validity according to the implications identified in step 1, while completing it if necessary, and 6) leveraging LLM to have a more comprehensive website. This process is evaluated with a dataset comprising the user-story sets of 67 similar-purpose websites.

A Framework for Concurrent Design of Metamodels and Diagrams - Towards an Agile Method for the Synthesis of Domain Specific Graphical Modeling Languages

Topics: Tools, Techniques and Methodologies for System Development; Model Driven Architectures and Engineering; Modeling Formalisms, Languages, and Notations (e.g. UML, ER variants)

Authors: François Pfister 1 , Marianne Huchard 2 and Clémentine Nebut 2

Affiliations: 1 Ecole des Mines d’Alès, France , 2 Université Montpellier 2, France

Digital Object Identifier: 10.5220/0004895202980306

Proceedings of ICEIS 2014, ISBN: 978-989-758-028-4

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DSML (Domain Specific Modeling Languages) are an alternative to general purpose modeling languages (e.g. UML or SysML) for describing models with concepts and relations specific to a domain. DSML design is often based on Ecore metamodels, which follow the class-relation paradigm and also require defining a concrete syntax which can be either graphical or textual. In this paper, we focus on graphical concrete syntax, and we introduce an approach and a tool (Diagraph) to assist the design of a graphical DSML. The main principles are: non-intrusive annotations of the metamodel to identify nodes, edges, nesting structures and other graphical information; immediate validation of metamodels by immediate generation of an EMF-GMF instance editor supporting multi-diagramming. We report a comparison experience between Diagraph and Obeo Designer (a commercial proprietary tool), which was conducted as part of a Model Driven Engineering Course.
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Enterprise Information Systems

Model Driven Architectures and Engineering

Tools, Techniques and Methodologies for System Development
Information Systems Analysis and Specification

NoSQL Graph-based OLAP Analysis

Topic: Business Intelligence Applications

Authors: Arnaud Castelltort and Anne Laurent

Affiliation: UMR5506 - Université Montpellier 2, France

Digital Object Identifier: 10.5220/0005072902170224

Proceedings of KDIR 2014/IC3K, ISBN: 978-989-758-048-2

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OLAP is a leading technology for analysing data and decision making. It helps the users to discover relevant information from large databases. Graph OLAP has been studied for several years in the OLAP framework. In existing work, the authors study how to import graph data into OLAP cube models but no work has explored yet the feasability to exploit graph structures to store analytical data. As graph databases are more and more used through NoSQL implementations (e.g., social and biological networks), in this paper we aim at providing an original model for managing cubes into NoSQL graphs. We show how cubes can be represented in graphs and how these structures can then be used for graph OLAP queries to support decision making.
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Artificial Intelligence

Knowledge Discovery and Information Retrieval

Symbolic Systems
Business Intelligence Applications

Knowledge-Based Systems

How to Extract Unit of Measure in Scientific Documents?

Authors: Soumia Lilia Berrahou 1 , Patrice Buche 1 , Juliette Dibie-Barthelemy 2 and Mathieu Roche 3

Affiliations: 1 Univ. Montpellier 2, INRA – UMR IATE and INRIA – GraphIK 2, France , 2 INRA - Mét@risk & AgroParisTech, France , 3 Univ. Montpellier 2, France

Digital Object Identifier: 10.5220/0004666302490256

Proceedings of SSTM 2013/IC3K, ISBN: 978-989-8565-75-4

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A large amount of quantitative data, related to experimental results, is reported in scientific documents in a free form of text. Each quantitative result is characterized by a numerical value often followed by a unit of measure. Extracting automatically quantitative data is a painstaking process because units suffer from different ways of writing within documents. In our paper, we propose to focus on the extraction and identification of the variant units, in order to enrich iteratively the terminological part of an Ontological and Terminological Resource (OTR) and in the end to allow the extraction of quantitative data. Focusing on unit extraction involves two main steps. Since we work on unstructured documents, units are completely drowned in textual information. In the first step, our method aims at handling the crucial time-consuming process of unit location using supervised learning methods. Once the units have been located in the text, the second step of our method consists in extracting and identifying candidate units in order to enrich the OTR. The extracted candidates are compared to units already known in the OTR using a new string distance measure to validate whether or not they are relevant variants. We have made concluding experiments on our two-step method on a set of more than 35000 sentences.

Using Formal Concept Analysis to Extract a Greatest Common Model

Topics: Coupling and Integrating Heterogeneous Data Sources; Software Engineering; Tools, Techniques and Methodologies for System Development; Business Modeling and Business Process Management; Project Management

Authors: Bastien Amar 1 , Abdoulkader Osman Guédi 2 , André Miralles 3 , Marianne Huchard 4 , Thérèse Libourel 1 and Clémentine Nebut 4

Affiliations: 1 Maison de la Télédétection, France , 2 Maison de la Télédétection, Université de Djibouti and Univ. Montpellier 2 et CNRS, France , 3 Tetis IRSTEA, Maison de la télédetection and Univ. Montpellier 2 et CNRS, France , 4 Univ. Montpellier 2 et CNRS, France

Digital Object Identifier: 10.5220/0003996000270037

Proceedings of ICEIS 2012, ISBN: 978-989-8565-10-5

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Data integration and knowledge capitalization combine data and information coming from different data sources designed by different experts having different purposes. In this paper, we propose to assist the underlying model merging activity. For close models made by experts of various specialities, we partially automate the identification of a Greatest Common Model (GCM) which is composed of the common concepts (coreconcepts) of the different models. Our methodology is based on Formal Concept Analysis which is a method of data analysis based on lattice theory. A decision tree allows to semi-automatically classify concepts from the concept lattices and assist the GCM extraction. We apply our approach on the EIS-Pesticide project, an environmental information system which aims at centralizing knowledge and information produced by different specialized teams.
Collapse Button Related Ontology Subjects/Areas/Topics (9)
Applications

Enterprise Information Systems

Project Management

Coupling and Integrating Heterogeneous Data Sources

Information Systems Analysis and Specification

Software Engineering

Databases and Information Systems Integration

Operational Research

Tools, Techniques and Methodologies for System Development

HOW STATISTICAL INFORMATION FROM THE WEB CAN HELP IDENTIFY NAMED ENTITIES

Author: Mathieu Roche

Affiliation: Univ. Montpellier 2, France

Digital Object Identifier: 10.5220/0003473906850689

Proceedings of WTM 2011/WEBIST, ISBN: 978-989-8425-51-5

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This paper presents a Natural Language Processing (NLP) approach to filter Named Entities (NE) from a list of collocation candidates. The NE are defined as the names of ’People’, ’Places’, ’Organizations’, ’Software’, ’Illnesses’, and so forth. The proposed method is based on statistical measures associated with Web resources to identify NE. Our method has three stages: (1) Building artificial prepositional collocations from Noun-Noun candidates; (2) Measuring the ”relevance” of the resulting prepositional collocations using statistical methods (Web Mining); (3) Selecting prepositional collocations. The evaluation of Noun-Noun collocations from French and English corpora confirmed the relevance of our system.

MINING FOR RELEVANT TERMS FROM LOG FILES

Topics: Mining Text and Semi-Structured Data; Pre-Processing and Post-Processing for Data Mining; Information Extraction

Authors: Hassan Saneifar 1 , Stéphane Bonniol 2 , Anne Laurent 3 , Pascal Poncelet 3 and Mathieu Roche 3

Affiliations: 1 Univ. Montpellier 2 - CNRS;Satin IP Technologies, France , 2 Satin IP Technologies, France , 3 Univ. Montpellier 2 - CNRS, France

Digital Object Identifier: 10.5220/0002307200770084

Proceedings of KDIR 2009/IC3K, ISBN: 978-989-674-011-5

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The Information extracted from log files of computing systems can be considered one of the important resources of information systems. In the case of Integrated Circuit design, log files generated by design tools are not exhaustively exploited. The logs of this domain are multi-source, multi-format, and have a heterogeneous and evolving structure. Moreover, they usually do not respect the grammar and the structures of natural language though they are written in English. According to features of such textual data, applying the classical methods of information extraction is not an easy task, more particularly for terminology extraction. We have previously introduced EXTERLOG approach to extract the terminology from such log files. In this paper, we introduce a new developed version of EXTERLOG guided by Web. We score the extracted terms by a Web and context based measure. We favor the more relevant terms of domain and emphasize the precision by filtering terms based on their scores. The experiments show that EXTERLOG is well-adapted terminology extraction approach from log files.
Collapse Button Related Ontology Subjects/Areas/Topics (7)
Artificial Intelligence

Knowledge-Based Systems

Pre-Processing and Post-Processing for Data Mining

Information Extraction

Mining Text and Semi-Structured Data

Symbolic Systems
Knowledge Discovery and Information Retrieval

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