Requirements of the SALTY project

This document is the first external deliverable of the SALTY project (Self-Adaptive very Large disTributed sYstems), funded by the ANR under contract ANR-09-SEGI-012. It is the result of task 1.1 of the Work Package (WP) 1 : Requirements and Architecture. Its objective is to identify and collect requirements from use cases that are going to be developed in WP 4 (Use cases and Validation). Based on the study and classification of the use cases, requirements against the envisaged framework are then determined and organized in features. These features will aim at guide and control the advances in all work packages of the project. As a start, features are classified, briefly described and related scenarios in the defined use cases are pinpointed. In the following tasks and deliverables, these features will facilitate design by assigning priorities to them and defining success criteria at a finer grain as the project progresses. This report, as the first external document, has no dependency to any other external documents and serves as a reference to future external documents. As it has been built from the use cases studies that have been synthesized in two internal documents of the project, extracts from the two documents are made available as appendices (cf. appen- dices B and C)

Plate-forme pour l'indexation spatiale multi-niveaux d'un corpus territorialisé

The aim of our work is to provide a more easier way to access documents in territorial copora and, particularly, spatial information contents. We suggest to go further than classical based-statistic information retrieval systems that are not suitable in the case of spatial information extraction. A light linguistic process can rather be used in order to draw the information's main thing. They can be a good starting point to be used thereafter in a more precise interpretation process, using only the geographic properties extracted, in order to propose a multi-level indexing method, each level corresponding to an abstraction level of spatial information.Thus, we propose a multi-levels spatial information retrieval system, indexing unstructured textual documents. This method, that interprets spatial information, allows to improve the efficiency of information retrieval systems each time a spatial query is performed. This interpretation can also retrieve the context in which the spatial information is used by the author. Particularly, text units can be classified in itinerary, local description or area comparison contexts.Notre travail s'insère dans la problématique de l'accès à l'information spatiale présente dans des corpus textuels territoriaux. Nous proposons d'aller au-delà des systèmes de recherche d'information classiques basés sur l'analyse statistique des documents, peu adaptés pour ce cas particulier, via un traitement linguistique ciblé interprétant l'information spatiale. Notre hypothèse est que des traitements relativement peu coûteux suffisent à dégager l'essentiel de l'information. Ils sont un bon point de départ pour une interprétation plus poussée par la suite, utilisant les propriétés géographiques de l'information extraite afin de développer un système d'indexation à plusieurs niveaux d'abstraction.Nous proposons en effet une méthode de recherche d'information spatiale multi-niveaux indexant un corpus textuel brut. Cette méthode qui extrait l'information d'un corpus et l'interprète, permet d'améliorer l'efficacité de systèmes de recherche d'information à chaque fois que l'interrogation comporte une connotation spatiale. L'interprétation permet en outre de retrouver le contexte dans lequel l'information spatiale a été utilisée. En particulier, elle permet d'indexer des unités de texte en leur associant des contextes de type itinéraire, description locale ou comparaison de lieux

Extraction et interprétation d'information géographique dans des données non-structurées

National audienc

Plate-forme pour l'indexation spatiale multi-niveaux d'un corpus territorialisé

Notre travail s insère dans la problématique de l accès à l information spatiale présente dans des corpus textuels territoriaux. Nous proposons d aller au-delà des systèmes de recherche d information classiques basés sur l analyse statistique des documents, peu adaptés pour ce cas particulier, via un traitement linguistique ciblé interprétant l information spatiale. Notre hypothèse est que des traitements relativement peu coûteux suffisent à dégager l essentiel de l information. Ils sont un bon point de départ pour une interprétation plus poussée par la suite, utilisant les propriétés géographiques de l information extraite afin de développer un système d indexation à plusieurs niveaux d abstraction. Nous proposons en effet une méthode de recherche d information spatiale multiniveaux indexant un corpus textuel brut. Cette méthode qui extrait l information d un corpus et l interprète, permet d améliorer l efficacité de systèmes de recherche d information à chaque fois que l interrogation comporte une connotation spatiale. L interprétation permet en outre de retrouver le contexte dans lequel l information spatiale a été utilisée. En particulier, elle permet d indexer des unités de texte en leur associant des contextes de type itinéraire, description locale ou comparaison de lieux.The aim of our work is to provide a more easier way to access documents in territorial corpora and, particularly, spatial information contents. We suggest to go further than classical based-statistic information retrieval systems that are not suitable in the case of spatial information extraction. A light linguistic process can rather be used in order to draw the information s main thing. They can be a good starting point to be used thereafter in a more precise interpretation process, using only the geographic properties extracted, in order to propose a multi-level indexing method, each level corresponding to an abstraction level of spatial information. Thus, we propose a multi-levels spatial information retrieval system, indexing unstructured textual documents. This method, that interprets spatial information, allows to improve the efficiency of information retrieval systems each time a spatial query is performed. This interpretation can also retrieve the context in which the spatial information is used by the author. Particularly, text units can be classified in itinerary, local description or area comparison contexts.PAU-BU Sciences (644452103) / SudocSudocFranceF

Deux approches pour la détection d'itinéraires dans du texte.

National audienc

Structuration d'information spatiale qualitative pour la Recherche d'Information

National audienc

Exploiting Geospatial Markers to Explore and Resocialize Localized Documents

International audienc

Associating spatial patterns to text-units for summarizing geographic information

International audienceRetrieving data based not only on key words is a challenge. We worked on semi-structured data (cultural heritage corpora). Our project aimed at getting the most relevant text-units of documents (sets of sentences, paragraphs, sections, etc.) according to a spatial query. This paper proposes a method to build summarized spatial indexes for text-units based on spatial patterns. This approach adds semantic interpretation to classical indexing methods

Towards an IE and IR System Dealing with Spatial Information in Digital Libraries - Evaluation Case Study

International audienceThis paper deals with spatial Information Extraction (IE) and Retrieval (IR) in Digital Libraries environments. The proposed approach (implemented within PIV(1) prototype) is based on a linguistic and semantic analysis of digital corpora and free text queries. First, we present requirements and a methodology of semantic annotation for automatic indexing and geo-referencing of text documents. Then we report on a case study where the spatial-based IR process is evaluated and compared to classical (statistical-based) IR approaches using first pure spatial queries and then more general ones dealing with both spatial and thematic scopes. The main result in these first experiments shows that combining a spatial approach with a classical (statistical-based) IR one improves in a significant way retrieval accuracy, namely in the case of general queries