Using Answer Set Programming for pattern mining

Serial pattern mining consists in extracting the frequent sequential patterns from a unique sequence of itemsets. This paper explores the ability of a declarative language, such as Answer Set Programming (ASP), to solve this issue efficiently. We propose several ASP implementations of the frequent sequential pattern mining task: a non-incremental and an incremental resolution. The results show that the incremental resolution is more efficient than the non-incremental one, but both ASP programs are less efficient than dedicated algorithms. Nonetheless, this approach can be seen as a first step toward a generic framework for sequential pattern mining with constraints.Comment: Intelligence Artificielle Fondamentale (2014

Logical Forms of Chronicles

A chronicle is a temporal model introduced by Dousson et al. for situation recognition. In short, a chronicle consists of a set of events and a set of real-valued temporal constraints on the delays between pairs of events. This work investigates the relationship between chronicles and classical temporal-model formalisms, namely TPTL and MTL. More specifically, we answer the following question: is it possible to find an equivalent formula in such formalisms for any chronicle? This question arises from the observation that a single chronicle captures complex temporal behaviours, without imposing a particular order of the events in time. For our purpose, we introduce the subclass of linear chronicles, which set the order of occurrence of the events to be recognized in a temporal sequence. Our first result is that any chronicle can be expressed as a disjunction of linear chronicles. Our second result is that any linear chronicle has an equivalent TPTL formula. Using existing expressiveness results between TPTL and MTL, we show that some chronicles have no equivalent in MTL. This confirms that the model of chronicle has interesting properties for situation recognition

Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences

The need to analyze information from streams arises in a variety of applications. One of its fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the presence of the pattern in transactions but pay no attention to the series of itemsets and their multiple occurrences. The pattern over a window of itemsets stream and their multiple occurrences, however, provides additional capability to recognize the essential characteristics of the patterns and the inter-relationships among them that are unidentifiable by the existing presence-based studies. In this paper, we study such a new sequential pattern mining problem and propose a corresponding sequential miner with novel strategies to prune the search space efficiently. Experiments on both real and synthetic data show the utility of our approach

Visualisation de données relationnelles

International audienceThis article presents the implementation of a QGis plugin for the visualization and the interactive construction of spacial graphs. Spatial graphs are accurate representations of spatial information through spatial objects linked by relationships (spatial or not). This representation is suited to the modeling and analysis of spatial information by computer processing (data mining, search for shortest paths, etc.). The use of spatial graph suffers from the lack of tools to facilitate the construction and integrated visualization. In this paper, we present a QGis plugin introducing a new type of layer: GraphLayer. These new layers can be integrated into any GIS projects. They offer rich functionality for visualization and interactive editing.Cet article présente l'implémentation d'une extension QGis pour la visualisation et la construction interactive de graphes spatiaux. Les graphes spatiaux sont des représentations de l'information spatiale sous la forme d'objets spatiaux reliés entre eux par des relations (spatiales ou non). Cette représentation est adaptée à la modélisation et à l'analyse d'information spatiale par des traitements informatiques (fouille de données, recherche de plus court chemins, etc). L'utilisation des graphes spatiaux pâtit de l'absence d'outils facilitant la construction et la visualisation intégrée. Dans cet article, on présente un extension QGis introduisant un nouveau type de couche, les GraphLayer. Ces nouvelles couches peuvent être intégrées dans les projets SIG. Elles offrent des fonctionnalités riches de visualisation et d'édition interactive

Semantic(s) of negative sequential patterns

National audienc

Self-adaptive web intrusion detection system

The evolution of the web server contents and the emergence of new kinds of intrusions make necessary the adaptation of the intrusion detection systems (IDS). Nowadays, the adaptation of the IDS requires manual -- tedious and unreactive -- actions from system administrators. In this paper, we present a self-adaptive intrusion detection system which relies on a set of local model-based diagnosers. The redundancy of diagnoses is exploited, online, by a meta-diagnoser to check the consistency of computed partial diagnoses, and to trigger the adaptation of defective diagnoser models (or signatures) in case of inconsistency. This system is applied to the intrusion detection from a stream of HTTP requests. Our results show that our system 1) detects intrusion occurrences sensitively and precisely, 2) accurately self-adapts diagnoser model, thus improving its detection accuracy

Long term analysis of time series of satellite images

International audienceSatellite images allow the acquisition of large-scale ground vegetation. Images are available along several years with a high acquisition rate. Such data are called satellite image time series (SITS). We present a method to analyse an SITS through the characterization of the evolution of a vegetation index (NDVI) at two scales: annual and multi-annual. We evaluate our method on SITS of the Senegal from 2001 to 2008 and we compare our method to a clustering of long time series. The results show that our method better discriminates regions in the median zone of Senegal and locates fine interesting areas

Prédiction du niveau de nappes phréatiques : comparaison d'approches locale, globale et hybride

International audienceCet article présente l'exploration d'une méthode autorégressive de prévision d'une série temporelle pour répondre au défi de la prédiction du niveau de nappes phréatiques. Une méthode autorégressive estime une valeur future d'une série temporelle par régression à partir des valeurs historiques de la série. Plusieurs méthodes de régression peuvent alors être employées. Dans cet article, on présente des expérimentations visant à identifier la meilleure configuration pour prédire de manière précise le niveau de nappes phréatiques. On compare pour cela différents prédicteurs, l'apprentissage de modèle par série ou par groupe de séries, et l'utilisation de données exogènes. Des expérimentations intensives ont été menées et nous permettent de conclure sur le choix de la méthode que nous utiliserons pour répondre au défi