1. Peggy Cellier. DeLLIS : Débogage de programmes par Localisation de fautes avec un Système d'Information Logique. PhD thesis, Thèse de l'université de Rennes 1, 05 décembre 2008. Note: Coencadrée par Mireille Ducassé, Sébastien Ferré et Olivier Ridoux.
   

   @PhdThesis{Cellier2008PhD,
   author = {Peggy Cellier},
   title = {{DeLLIS} : Débogage de programmes par Localisation de fautes avec un Système d'Information Logique},
   school = {Thèse de l'université de Rennes 1},
   note = {coencadrée par Mireille Ducassé, Sébastien Ferré et Olivier Ridoux},
   year = {2008},
   month = {05 décembre},
   
   }
   

1. O. Bedel, S. Ferré, O. Ridoux, and E. Quesseveur. GEOLIS: A Logical Information System for Geographical Data. Revue Internationale de Géomatique, 17(3-4):371-390, 2008. [PDF] Keyword(s): logical information system, geographical data, navigation. Abstract:

   Today, the thematic layer is still the prevailling structure in geomatics for handling geographical information. However, the layer model is rigid: it implies partitionning geographical data in predefined categories and using the same description schema for all elements of a layer. Recently, Logical Information Systems (LIS) introduced a new paradigm for information management and retrieval. Using LIS, we propose a more flexible organisation of vectorial geographical data at a thiner level since it is centered on the geographical feature. LIS do not rely on a hierarchical organisation of information, and enable to tightly combine querying and navigation. In this article, we present the use of LIS to handle geographical data. In particular, we detail a data model for geographical features and the corresponding querying and navigation model. These models have been implemented in the GEOLIS prototype, which has been used to lead experiments on real data.

   
   

   @Article{BFRQ2008,
   author = {O. Bedel and S. Ferré and O. Ridoux and E. Quesseveur},
   title = {{GEOLIS}: A Logical Information System for Geographical Data},
   journal = {Revue Internationale de Géomatique},
   year = {2008},
   volume = {17},
   number = {3-4},
   pages = {371-390},
   keywords = {logical information system, geographical data, navigation},
   abstract = {Today, the thematic layer is still the prevailling structure in geomatics for handling geographical information. However, the layer model is rigid: it implies partitionning geographical data in predefined categories and using the same description schema for all elements of a layer. Recently, Logical Information Systems (LIS) introduced a new paradigm for information management and retrieval. Using LIS, we propose a more flexible organisation of vectorial geographical data at a thiner level since it is centered on the geographical feature. LIS do not rely on a hierarchical organisation of information, and enable to tightly combine querying and navigation. In this article, we present the use of LIS to handle geographical data. In particular, we detail a data model for geographical features and the corresponding querying and navigation model. These models have been implemented in the GEOLIS prototype, which has been used to lead experiments on real data.},
   pdf = {http://www.irisa.fr/LIS/ferre/papers/rig2008.pdf},
   
   }
   




2. P. Cellier, S. Ferré, O. Ridoux, and M. Ducassé. A Parameterized Algorithm to Explore Formal Contexts with a Taxonomy. Int. J. Foundations of Computer Science (IJFCS), 19(2):319-343, 2008. Keyword(s): algorithm, concept lattice, taxonomy. Abstract:

   Formal Concept Analysis (FCA) is a natural framework to learn from examples. Indeed, learning from examples results in sets of frequent concepts whose extent contains mostly these examples. In terms of association rules, the above learning strategy can be seen as searching the premises of rules where the consequence is set. In its most classical setting, FCA considers attributes as a non-ordered set. When attributes of the context are partially ordered to form a taxonomy, Conceptual Scaling allows the taxonomy to be taken into account by producing a context completed with all attributes deduced from the taxonomy. The drawback, however, is that concept intents contain redundant information. In this article, we propose a parameterized algorithm, to learn rules in the presence of a taxonomy. It works on a non-completed context. The taxonomy is taken into account during the computation so as to remove all redundancies from intents. Simply changing one of its operations, this parameterized algorithm can compute various kinds of concept-based rules. We present instantiations of the parameterized algorithm to learn rules as well as to compute the set of frequent concepts.

   
   

   @article{CFRD2008,
   author = {P. Cellier and S. Ferré and O. Ridoux and M. Ducassé},
   title = {A Parameterized Algorithm to Explore Formal Contexts with a Taxonomy},
   journal = {Int. J. Foundations of Computer Science (IJFCS)},
   year = {2008},
   publisher = {World Scientific},
   editors = {S. Ben Yahia and E. Mephu Nguifo},
   volume = {19},
   number = {2},
   pages = {319--343},
   keywords = {algorithm, concept lattice, taxonomy},
   abstract = {Formal Concept Analysis (FCA) is a natural framework to learn from examples. Indeed, learning from examples results in sets of frequent concepts whose extent contains mostly these examples. In terms of association rules, the above learning strategy can be seen as searching the premises of rules where the consequence is set. In its most classical setting, FCA considers attributes as a non-ordered set. When attributes of the context are partially ordered to form a taxonomy, Conceptual Scaling allows the taxonomy to be taken into account by producing a context completed with all attributes deduced from the taxonomy. The drawback, however, is that concept intents contain redundant information. In this article, we propose a parameterized algorithm, to learn rules in the presence of a taxonomy. It works on a non-completed context. The taxonomy is taken into account during the computation so as to remove all redundancies from intents. Simply changing one of its operations, this parameterized algorithm can compute various kinds of concept-based rules. We present instantiations of the parameterized algorithm to learn rules as well as to compute the set of frequent concepts.},
   
   }
   

1. P. Allard and S. Ferré. Dynamic Taxonomies for the Semantic Web. In G.M. Sacco, editor, DEXA Int. Work. Dynamic Taxonomies and Faceted Search (FIND), pages 382-386, 2008. IEEE Computer Society. [PDF] Keyword(s): semantic web, ontologies, logical information system, dynamic taxonomies. Abstract:

   The semantic web aims at enabling the web to understand and answer the requests from people and machines. It relies on several standards for representing and reasoning about web contents. Among them, the Web Ontology Language (OWL) is used to define ontologies, i.e. knowledge bases, and is formalized with description logics. In this paper, we demonstrate how dynamic taxonomies and their benefits can be transposed to browse OWL~DL ontologies. We only assume the ontology has an assertional part, i.e. defines objects and not only concepts. The existence of relations between objects in OWL leads us to define new navigation modes for crossing these relations. A prototype, Odalisque, has been developed on top of well-known tools for the semantic web.

   
   

   @InProceedings{AlaFer2008,
   author = {P. Allard and S. Ferré},
   title = {Dynamic Taxonomies for the Semantic Web},
   booktitle = {{DEXA} Int. Work. Dynamic Taxonomies and Faceted Search ({FIND})},
   year = {2008},
   pages = {382--386},
   publisher = {IEEE Computer Society},
   editor = {G.M. Sacco} keywords = {semantic web, ontologies,logical information system, dynamic taxonomies},
   pdf = {http://www.irisa.fr/LIS/ferre/papers/find2008-Allard.pdf},
   abstract = {The semantic web aims at enabling the web to understand and answer the requests from people and machines. It relies on several standards for representing and reasoning about web contents. Among them, the Web Ontology Language (OWL) is used to define ontologies, i.e. knowledge bases, and is formalized with description logics. In this paper, we demonstrate how dynamic taxonomies and their benefits can be transposed to browse OWL~DL ontologies. We only assume the ontology has an assertional part, i.e. defines objects and not only concepts. The existence of relations between objects in OWL leads us to define new navigation modes for crossing these relations. A prototype, Odalisque, has been developed on top of well-known tools for the semantic web.},
   
   }
   




2. O. Bedel, S. Ferré, and O. Ridoux. Handling Spatial Relations in Logical Concept Analysis To Explore Geographical Data. In R. Medina and S. Obiedkov, editors, Int. Conf. Formal Concept Analysis, LNAI 4933, pages 241-257, 2008. Springer. [PDF] Keyword(s): spatial relations, concept analysis, logic, geographical data, data retrieval. Abstract:

   Because of the expansion of geo-positioning tools and the democratization of geographical information, the amount of geo-localized data that is available around the world keeps increasing. So, the ability to efficiently retrieve informations in function of their geographical facet is an important issue. In addition to individual properties such as position and shape, spatial relations between objects are an important criteria for selecting and reaching objects of interest: e.g., given a set of touristic points, selecting those having a nearby hotel or reaching the nearby hotels. In this paper, we propose Logical Concept Analysis (LCA) and its handling of relations for representing and reasoning on various kinds of spatial relations: e.g., Euclidean distance, topological relations. Furthermore, we present an original way of navigating in geolocalized data, and compare the benefits of our approach with traditional Geographical Information Systems (GIS).

   
   

   @InProceedings{BedFerRid2008,
   author = {O. Bedel and S. Ferré and O. Ridoux},
   title = {Handling Spatial Relations in Logical Concept Analysis To Explore Geographical Data},
   booktitle = {Int. Conf. Formal Concept Analysis},
   pages = {241--257},
   year = {2008},
   editor = {R. Medina and S. Obiedkov},
   series = {LNAI 4933},
   publisher = {Springer},
   keywords = {spatial relations, concept analysis, logic, geographical data, data retrieval},
   pdf = {http://www.irisa.fr/LIS/ferre/papers/icfca2008-bedel.pdf},
   abstract = {Because of the expansion of geo-positioning tools and the democratization of geographical information, the amount of geo-localized data that is available around the world keeps increasing. So, the ability to efficiently retrieve informations in function of their geographical facet is an important issue. In addition to individual properties such as position and shape, spatial relations between objects are an important criteria for selecting and reaching objects of interest: e.g., given a set of touristic points, selecting those having a nearby hotel or reaching the nearby hotels. In this paper, we propose Logical Concept Analysis (LCA) and its handling of relations for representing and reasoning on various kinds of spatial relations: e.g., Euclidean distance, topological relations. Furthermore, we present an original way of navigating in geolocalized data, and compare the benefits of our approach with traditional Geographical Information Systems (GIS).},
   
   }
   




3. P. Cellier, M. Ducassé, S. Ferré, and O. Ridoux. Formal Concept analysis enhances Fault Localization in Software. In R. Medina and S. Obiedkov, editors, Int. Conf. Formal Concept Analysis, LNAI 4933, pages 273-288, 2008. Springer. [PDF] Keyword(s): fault localization, formal concept analysis. Abstract:

   Recent work in fault localization crosschecks traces of correct and failing execution traces. The implicit underlying technique is to search for association rules which indicate that executing a particular source line will cause the whole execution to fail. This technique, however, has limitations. In this article, we first propose to consider more expressive association rules where several lines imply failure. We then propose to use Formal Concept Analysis (FCA) to analyze the resulting numerous rules in order to improve the readability of the information contained in the rules. The main contribution of this article is to show that applying two data mining techniques, association rules and FCA, produces better results than existing fault localization techniques.

   
   

   @inproceedings{CDFR2008a,
   title = {Formal Concept analysis enhances Fault Localization in Software},
   author = {P. Cellier and M. Ducassé and S. Ferré and O. Ridoux},
   booktitle = {Int. Conf. Formal Concept Analysis},
   series = {LNAI 4933} publisher = {Springer},
   year = {2008},
   editor = {R. Medina and S. Obiedkov},
   pages = {273--288},
   keywords = {fault localization, formal concept analysis},
   abstract = {Recent work in fault localization crosschecks traces of correct and failing execution traces. The implicit underlying technique is to search for association rules which indicate that executing a particular source line will cause the whole execution to fail. This technique, however, has limitations. In this article, we first propose to consider more expressive association rules where several lines imply failure. We then propose to use Formal Concept Analysis (FCA) to analyze the resulting numerous rules in order to improve the readability of the information contained in the rules. The main contribution of this article is to show that applying two data mining techniques, association rules and FCA, produces better results than existing fault localization techniques.},
   pdf = {http://www.irisa.fr/LIS/ferre/papers/icfca2008-cellier.pdf},
   
   }
   




4. M. Ducassé and S. Ferré. Fair(er) and (almost) serene committee meetings with Logical and Formal Concept Analysis. In P. Eklund and O. Haemmerlé, editors, Int. Conf. Conceptual Structures, LNAI 5113, 2008. Springer-Verlag. [PDF] Keyword(s): formal concept analysis, logical concept analysis, social sciences. Abstract:

   In academia, many decisions are taken in committee, for example to hire people or to allocate resources. Genuine people often leave such meetings quite frustrated. Indeed, it is intrinsically hard to make multi-criteria decisions, selection criteria are hard to express and the global picture is too large for participants to embrace it fully. In this article, we describe a recruiting process where logical concept analysis and formal concept analysis are used to address the above problems. We do not pretend to totally eliminate the arbitrary side of the decision. We claim, however, that, thanks to concept analysis, genuine people have the possibility to 1) be fair with the candidates, 2) make a decision adapted to the circumstances, 3) smoothly express the rationales of decisions, 4) be consistent in their judgements during the whole meeting, 5) vote (or be arbitrary) only when all possibilities for consensus have been exhausted, and 6) make sure that the result, in general a total order, is consistent with the partial orders resulting from the multiple criteria.

   Annotation:

   taux acceptation 19/70 = 27\%

   
   

   @InProceedings{DucFer2008,
   author={M. Ducassé and S. Ferré},
   title={Fair(er) and (almost) serene committee meetings with Logical and Formal Concept Analysis},
   pages={ },
   bookTitle={Int. Conf. Conceptual Structures},
   year={2008},
   editor={P. Eklund and O. Haemmerlé},
   publisher={Springer-Verlag},
   series={LNAI 5113},
   pdf={http://www.irisa.fr/LIS/ferre/papers/iccs2008.pdf},
   abstract={ In academia, many decisions are taken in committee, for example to hire people or to allocate resources. Genuine people often leave such meetings quite frustrated. Indeed, it is intrinsically hard to make multi-criteria decisions, selection criteria are hard to express and the global picture is too large for participants to embrace it fully. In this article, we describe a recruiting process where logical concept analysis and formal concept analysis are used to address the above problems. We do not pretend to totally eliminate the arbitrary side of the decision. We claim, however, that, thanks to concept analysis, genuine people have the possibility to 1) be fair with the candidates, 2) make a decision adapted to the circumstances, 3) smoothly express the rationales of decisions, 4) be consistent in their judgements during the whole meeting, 5) vote (or be arbitrary) only when all possibilities for consensus have been exhausted, and 6) make sure that the result, in general a total order, is consistent with the partial orders resulting from the multiple criteria.},
   keywords={formal concept analysis, logical concept analysis, social sciences},
   annote={taux acceptation 19/70 = 27\%},
   
   }
   




5. S. Ferré. Agile Browsing of a Document Collection with Dynamic Taxonomies. In G.M. Sacco, editor, DEXA Int. Work. Dynamic Taxonomies and Faceted Search (FIND), pages 377-381, 2008. IEEE Computer Society. [PDF] Keyword(s): browsing, navigation, logical information system, dynamic taxonomies. Abstract:

   Dynamic taxonomies and faceted search are increasingly used to organize and browse document collections. The main function of dynamic taxonomies is to start with the full collection, and zoom-in to a small enough subset of items for direct inspection. In this paper, we present other navigation modes than zoom-in for less directed and more exploratory browsing of a document collection. The presented navigation modes are zoom-out, shift, pivot, and querying by examples. These modes correspond to query transformations, and make use of boolean operators. Therefore, the current focus is always clearly specified by a query.

   
   

   @InProceedings{Fer2008,
   author = {S. Ferré},
   title = {Agile Browsing of a Document Collection with Dynamic Taxonomies},
   booktitle = {{DEXA} Int. Work. Dynamic Taxonomies and Faceted Search ({FIND})},
   year = {2008},
   pages = {377--381},
   publisher = {IEEE Computer Society},
   editor = {G.M. Sacco} keywords = {browsing, navigation, logical information system, dynamic taxonomies},
   pdf = {http://www.irisa.fr/LIS/ferre/papers/find2008.pdf},
   abstract = {Dynamic taxonomies and faceted search are increasingly used to organize and browse document collections. The main function of dynamic taxonomies is to start with the full collection, and zoom-in to a small enough subset of items for direct inspection. In this paper, we present other navigation modes than zoom-in for less directed and more exploratory browsing of a document collection. The presented navigation modes are zoom-out, shift, pivot, and querying by examples. These modes correspond to query transformations, and make use of boolean operators. Therefore, the current focus is always clearly specified by a query.},
   
   }
   

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