The Pattern Next Door: Towards Spatio-sequential Pattern Discovery

Alatrista-Salas, Hugo; Bringay, Sandra; Flouvat, Frédéric; Selmaoui-Folcher, Nazha; Teisseire, Maguelonne

doi:10.1007/978-3-642-30220-6_14

Cited by 14 publications

(9 citation statements)

References 10 publications

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“…Lately, these works have been extended to integrate both dimensions. We can distinguish two frameworks: mining trajectories of moving objects [31] and mining evolutions of properties/attributes related to spatial objects [64,67,15,3,49,5]. In our work, we focus on the second framework.…”

Section: Spatio-temporal Pattern Miningmentioning

confidence: 99%

Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic Graph

et al. 2020

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Directed Acyclic Graphs (DAGs) are used in many domains ranging from computer science to bioinformatics, including industry and geoscience. They enable to model complex evolutions where spatial objects (e.g., soil erosion) may move, (dis)appear, merge or split. We study a new graph-based representation, called attributed DAG (a-DAG). It enables to capture interactions between objects as well as information on objects (e.g., characteristics or events). In this paper, we focus on pattern mining in such data. Our patterns, called weighted paths, offer a good trade-off between expressiveness and complexity. Frequency and compactness constraints are used to filter out uninteresting patterns. These constraints lead to an exact condensed representation (without loss of information) in the single-graph setting. A depth-first search strategy and an optimized data structure are proposed to achieve the efficiency of weighted path discovery. It does a progressive extension of patterns based on database projections. Relevance, scalability and genericity are illustrated by means of qualitative and quantitative results when mining various real and synthetic datasets. In particular, we show how such an approach can be used to monitor soil erosion using remote sensing and Geographical Information System (GIS) data.

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Section: Spatio-temporal Pattern Miningmentioning

confidence: 99%

Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic Graph

et al. 2020

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“…Les séquences et plus généralement les graphes, ont été souvent utilisés et étendus au spatio-temporel pour représenter la propagation de phénomènes dans l'espace et dans le temps (Tsoukatos, Gunopulos, 2001 ;2005 ;Huang et al, 2008 ;Mabit et al, 2011 ;Alatrista-Salas et al, 2011 ;2012). Dans (J.…”

Section: Trajectoires Pour Le Suivi D'objets Mobilesunclassified

“…Pour extraire ces motifs, (Alatrista-Salas et al, 2012) ont proposé un algorithme appelé DFS-S2PMiner qui suit une stratégie de recherche en profondeur (depth-first search).…”

Section: Indice De Participation Spatio-temporelunclassified

Motifs spatio-temporels. Enjeux et applications à l'environnement

Selmaoui-Folcher¹,

Flouvat²,

Alatrista-Salas³

et al. 2013

Revue d'intelligence artificielle

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International audienceTechnological advances in terms of data acquisition enable to better monitor dynamic phenomena in various domains including environment. The collected data are more and more complex (spatial, temporal, heterogeneous and multi-scale). The exploitation of this data requires new methods of data analysis and knowledge discovery. In this context, approaches for discovering spatio-temporal patterns are particularly relevant. This paper proposes to make a detailed review of these works. We focus on two examples of patterns : colocation and spatiosequential patterns. These patterns have been used to study real applications in the ﬁeld of environment.Les avancées technologiques en termes d’acquisition de données permettent de mieux surveiller les phénomènes évolutifs dans divers domaines dont l’environnement. Les données collectées sont de plus en plus complexes (spatiales, temporelles, hétérogènes et multi-échelles). L’exploitation de ces données par leurs propriétaires (experts du domaine) nécessite de concevoir de nouvelles méthodes d’analyse de données et de découverte de connaissances. Dans ce contexte, les approches de découverte de motifs spatio-temporels se révèlent particulièrement pertinentes. Ce papier propose de faire une revue détaillée de ces travaux. Nous nous focalisons sur deux exemples de motifs : les colocalisations et les motifs spatio-séquentiels. Ces motifs ont été utilisés pour étudier deux applications réelles dans le domaine de l’environnement

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“…To extract this kind of information, we define a new type of spatiotemporal pattern called Spatio-Sequential Patterns or simply S2P, which allows us to discover the temporal evolution of events appearing not only in a studied area but also in its near surrounding. This new type of patterns have been defined in previous work [3]. An example of pattern in the dengue context is: frequently over the past 10 years, if it rains in an area and if there are standing water and high temperatures in the neighborhood, then there is an increasing number of mosquitoes in adjacent areas, followed by an increase of dengue cases.…”

Section: Introductionmentioning

confidence: 97%

“…In contrast to previous work [3], we propose in this paper, two generic algorithms to extract the S2P. The first one uses the a priori strategy and the second one is based on pattern-growth approach with efficient successive projections over the database.…”

Section: Introductionmentioning

confidence: 99%

Spatio-sequential patterns mining: Beyond the boundaries

Alatrista-Salas

Bringay

Flouvat³

et al. 2016

IDA

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International audienceData mining methods extract knowledge from huge amounts of data. Recently with the explosion of mobile technologies, a new type of data appeared. The resulting databases can be described as spatiotemporal data in which spatial information (e.g., the location of an event) and temporal information (e.g., the date of the event) are included. In this article, we focus on spatiotemporal patterns extraction from this kind of databases. These patterns can be considered as sequences representing changes of events localized in areas and its near surrounding over time. Two algorithms are proposed to tackle this problem: the first one uses \emph{a priori} strategy and the second one is based on pattern-growth approach. We have applied our generic method on two different real datasets related to: 1) pollution of rivers in France; and 2) monitoring of dengue epidemics in New Caledonia. Additionally, experiments on synthetic data have been conducted to measure the performance of the proposed algorithms

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The Pattern Next Door: Towards Spatio-sequential Pattern Discovery

Cited by 14 publications

References 10 publications

Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic Graph

Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic Graph

Motifs spatio-temporels. Enjeux et applications à l'environnement

Spatio-sequential patterns mining: Beyond the boundaries

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