Which tools to study, compare and exploit data on water courses and pressures undergone ? The aim of preserving and restoring the good status of water bodies, required by the European Water Framework Directive (2000), underlines the necessity of having operational tools to help in the interpretation of the complex information concerning water courses and their functioning, as well as the assessment of the effectiveness of ongoing action programmes.
In this perspective, the FRESQUEAU project has tackled two specific issues: (1) going further into the understanding of links between the different metrics used to characterize the quality of water courses (2) identifying and modeling the relations between sources of pressures on the environment and physico-chemical and biological quality of water courses. To this aim, a multi-sources database has been built, containing data related to water quality, hydrology, stations of measures, etc., and to the environment of water courses. The heterogeneity of the data and the diversity of the questions identified and complexity have then been tackled using different methods of modelling, data mining and visualization so as to allow a countinuous interaction between computer scientists and hydroecologists.
A database and a set of suitable knowledge extraction methods. The first step of the project enabled to make an inventory of a set of data describing water courses and their environment for the Rhine-Meuse and the Rhone-Mediterranean and Corse districts. The integration of the data in the database was based on the design of a model for the data. Information on the quality of the data were also collected (domain knowledge and statistical or topological measures). An integrated database was developed and is available to the partners. At the same time, we built a datawarehouse with two OLAP cubes, enabling to explore physico-chemical measures on the one hand and biological measures on the other hand, along several thematic, spatial and temporal dimensions. Secondly, a set of operational questions was established, every question being specified by a dataset extracted from the database and by three data mining methods to experiment, spatial statistics, sequential patterns, relational lattices. Finally, we developed some visualization techniques for hydroecologists to be able to exploit the results of the data mining methods.
Major results. The main results of the project concern both computer scientists and hydroecologists. The first result concerns the formalisation of a set of questions, as well as data and procedures enabling to answer them. All the questions have not been explored, nevertheless this formalisation constitutes a strong basis for developing the collaboration started with this project. The second result is the design of a new method for the analysis and synthesis of sequences linking physico-chemical and biological data. Many people in the hydroecological domain have found this approach relevant. Finally, the third important result is the development of a platform for the analysis of the data collected and the visualization of the results.
Scientific production. The scientific production concerns the two research domains concerned by the project, computer science and hydroecology. Most of the publications are signed jointly by the different partners of the project, including the consulting firms, these last being implied in all the tasks of the project.
Factual information. The FRESQUEAU project is a project of applied research coordinated by ENGEES. It also includes Irstea (UMR TETIS), the University of Strasbourg (UMR ICube and LIVE), the University of Montpellier (UMR LIRMM) as well as two consulting firms in hydroecology, Aquascop and Aquabio. The project started in October 2011 and lasted 43 months. It received a grant of 851.833€ by the ANR for a global cost around 2.700.000€.
Peer reviewed int. journals
1. Multidimensional modelling and analysis of large and complex watercourses data: an OLAP-based solution. K. Boulil, F. Le Ber, S. Bimonte, C. Grac, F. Cernesson. Ecological Informatics, vol. 24, nov. 2014, pp. 90–106.
2. Discriminant temporal patterns for linking physico-chemistry and biology in hydro-ecosystem assessment. M. Fabrègue, A. Braud, S. Bringay, C. Grac, D. Levet, F. Le Ber, M. Teisseire, Ecological Informatics, vol. 24, nov. 2014, pp. 210–221.
3. Mining Closed Partially Ordered Patterns, a new optimized algorithm. M. Fabrègue, A. Braud, S. Bringay, F. Le Ber, M. Teisseire. Knowledge-Based Systems, vol. 79, mai 2015, pp. 68–79.
4. Experimental study of uncertainties in the Macrophyte index (IBMR) based on species identification and cover. J. Wiederkehr, C. Grac, M. Fabrègue, B. Fontan, F. Labat, F. Le Ber, M. Trémolières, vol. 50, mar. 2015, pp. 242–250.
5. Performance-friendly rule extraction in large water data-sets with AOC posets and relational concept analysis. X. Dolques, F. Le Ber, M. Huchard, C. Grac, International Journal of General Systems, 2016, 45 (2), pp. 187–210.
6. Relational Concept Analysis for Relational Data Exploration. X. Dolques, F. Le Ber, M. Huchard, C. Nebut. Advances in Knowledge Discovery and Management, 5 (Part II), pp. 57–77, Springer, 2016.
7. A quality-aware spatial data warehouse for querying hydroecological data. L. Berrahou, N. Lalande, E. Serrano, G. Molla, L. Berti-Équille, S. Bimonte, S. Bringay, F. Cernesson, C. Grac, D. Ienco, F. Le Ber, M. Teisseire, Computer Geosciences, 2015, 85, pp. 126–135.
8. Experimental study on the uncertainty of intrasubstrate variability in two French index metrics based on macroinvertebrates. J. Wiederkehr, C. Grac, B. Fontan, F. Labat, F. Le Ber, M. Trémolières. Hydrobiologia, 2015, 50, pp. 242–250.
Chapters in Edited Books
1. Relational Concept Analysis for Relational Data Exploration. Xavier Dolques, Florence Le Ber, Marianne Huchard, Clémentine Nebut. Advances in Knowledge Discovery and Management, 5 (Part II), pp.57-77, 2016.
1. Including spatial relations and scales within sequential pattern extraction. M. Fabrègue, A. Braud, S. Bringay, F. Le Ber, M. Teisseire. DS'2012, Lyon, France.
2. Relational Data Exploration by Relational Concept Analysis. X. Dolques, F. Le Ber, M. Huchard, C. Nebut. ECAI2012 WS FC4AI, Montpellier, France.
3. Feedbacks on data collection, data modeling and data integration of large datasets: application to Rhine-Meuse and Rhone-Mediterranean districts (France). N. Lalande, L. Berrahou, G. Molla, E. Serrano, F. Cernesson, C. Grac, A. Herrmann, F. Le Ber, M. Teisseire, M. Trémolières. SEFS, 2013, Münster, Allemagne.
4. Multi index assessment of streams and associated uncertainties: application to macrophytes. J. Wiederkehr, M. Fabrègue, B. Fontan, C. Grac, F. Labat, F. Le Ber, M. Trémolières. SEFS, 2013, Münster, Allemagne.
5. OrderSpan: Mining Closed Partially Ordered Patterns. M. Fabrègue, A. Braud, S. Bringay, F. Le Ber, M. Teisseire. The Twelfth International Symposium on Intelligent Data Analysis (IDA 2013), Oct 2013, London, United Kingdom. Advances in Intelligent Data Analysis XII, pp. 186-197.
6. AOC-posets: a scalable alternative to Concept Lattices for Relational Concept Analysis. X. Dolques, F. Le Ber, M. Huchard. CLA 2013: 10th International Conference on Concept Lattices and Their Applications, Oct 2013, La Rochelle, France. pp. 129-140.
7. RCA as a data transforming method: a comparison with propositionalisation. X. Dolques, K. Chandra Mondal, A. Braud, M. Huchard, F. Le Ber. ICFCA, juin 2014, Cluj, Roumanie.
8. HydroQual: Visual Analysis of River Water Quality. P. Accorsi, M. Fabrègue, A. Sallaberry, F. Cernesson, N. Lalande, A. Braud, S. Bringay, F. Le Ber, P. Poncelet, M. Teisseire. IEEE Conference on Visual Analytics Science and Technology, nov. 2014, Paris.
Peer reviewed nat. journals
1. Le projet Fresqueau : exploiter les données massives concernant les cours d’eau. F. Le Ber, M. Teisseire, A. Braud, F. Cernesson, C. Grac, P. Poncelet. Revue ISI, vol. 19, no 3, 2014, pp. 169-174.
2. Méthode de factorisation progressive pour accroître l’abstraction d’un modèle de classes. A. Miralles, M. Huchard, X. Dolques, F. Le Ber, T. Libourel, C. Nebut, A. Osman-Guédi. Revue ISI, vol. 20, no 2, 2015, pp. 9-39.
3. Un système décisionnel pour l’analyse de la qualité des eaux de rivières. S. Bimonte, K. Boulil, A. Braud, S. Bringay, F. Cernesson, X. Dolques, M. Fabrègue, C. Grac, N. Lalande, F. Le Ber, M. Teisseire. Revue ISI, vol. 20, no 3, 2015, 143-167.
1. Extraction de motifs spatio-temporels à différentes échelles avec gestion de relations spatiales qualitatives. M. Fabrègue, A. Braud, S. Bringay, F. Le Ber, M. Teisseire. Inforsid 2012, Montpellier.
2. Analyse Relationnelle de Concepts pour l'exploration de données relationnelles. X. Dolques, F. Le Ber, M. Huchard, C. Nebut. EGC'2013, Toulouse.
3. Stream multi-index assessment and associated uncertainties: application to macroinvertebrates and macrophytes. J. Wiederkehr, B. Fontan, C. Grac, F. Labat, F. Le Ber, M. Trémolières. JILO 2012, Clermont-Ferrand, France.
4. Une expérience de constitution d’un système d’information multi-sources pour l’étude de la qualité de l’eau. A. Braud, S. Bringay, F. Cernesson, X. Dolques, M. Fabrègue, C. Grac, N. Lalande, F. Le Ber, M. Teisseire. Atelier SI et Environnement, Inforsid 2014, Lyon.
5. Data processing for controlling data quality on surfapce water quality assessment. E. C. Serrano Balderas, L. Berti-Equille, C. Grac. Atelier SI et Environnement, Inforsid 2014, Lyon.
6. Recherche de motifs partiellement ordonnés clos discriminants pour caractériser l’état des milieux aquatiques. M. Fabrègue, A. Braud, S. Bringay, F. Le Ber, M. Teisseire. Atelier AnaEnv, associé à la conférence RFIA 2014, Rouen, France.
7. Exploration de données temporelles avec des treillis relationnels. C. Nica, X. Dolques, A. Braud, M. Huchard, F. Le Ber. Atelier GAST, associé à la conférence EGC 2015, Luxembourg, Luxembourg.