The complexity of database systems has increased alongside the exponential growth of data, necessitating Information Systems (IS) architects to continuously refine data models and meticulously select storage and management options that align with requirements. While existing solutions focus on data model transformation, none offer guidance in selecting the most suitable model. In this context, we propose DaMoOp , an automated approach for leading data model selection process. DaMoOp starts from a conceptual model and associated use case comprising queries, settings and infrastructure constraints, to generate relevant logical data models. A cost model, considering environmental, financial, and temporal factors, facilitates comparison and selection of the most suitable data model. Our cost model incorporates both data model and queries costs. Additionally, we suggest a data model selection process that enhances the ability to choose the optimal data model(s) for a specific use case, while also adapting to rapidly evolving use cases. We provide a strategic optimization approach designed to identify the most cost-efficient and stable data model as use case scenarios evolve. Moreover, we offer a simulation tool for the entire process, which enables visualizing the impact of use case variations on data model costs, thus empowering IS architects to make informed decisions.

Gait recognition has gained substantial attention as a key research topic within the Computer Vision field in the past years. It consists of the re-identification of individuals based on soft biometrics such as their gait cycles referring to walking patterns. Existing approaches include appearance-based methods using silhouettes, and model-based methods relying on human joint representations. These methods face numerous challenges, such as variations in clothing and viewing angles, which affect their performance and limit their generalizability. In this paper, we introduce DualGait, a novel model-based approach for gait recognition that leverages a Spatio-Temporal Graph Convolutional Networks (STGCN) architecture enhanced with modular attention blocks. In contrast to traditional approaches, our method relies on two human representation models: a graph that captures the spatial coordinates of joints, and its corresponding edge-to-vertex dual, which encodes the lengths of body segments. We evaluate our framework on the CASIA-B benchmark, demonstrating competitive performance in gait recognition and its adaptability to variations in view angles and clothing.

International audience

International audience

Uncertainty is one of the major challenges related to the semantic gap in multimedia data description and retrieval. It is due not only to errors and imprecisions in content classification but also to the extended range of user queries. In this paper, an extension of fuzzy conceptual graphs, suitable for handling uncertainty in visual event description and retrieval, is presented. We deal with two types of graphs according to the sources of uncertainty. An extension of fuzzy spatial and temporal relationships is defined to capture imprecision in video content spatiotemporal descriptions. Moreover, similarity measures and matching algorithms are defined to assess the degree of match between the descriptions of video objects and the event model and then to detect events within video segments.

Very often, enterprises are subject to changes. These changes require adaptations regarding business process, security components, database access, etc. In this paper we present a comprehensive (basic) approach for the controlled evolution of Web service business protocols in a cooperative Web services framework. A set of elementary change operators is proposed to capture the change of protocols and a selective process of change projection is adopted to predict potentially interesting changes to partners in order to maintain compatibility.

Behavior understanding and semantic interpretation of dynamic visual scenes have attracted a lot of attention in computer vision research community. Although the use of surveillance cameras has proliferated, the understanding of activities still remains complex. While users are mostly interested in high level and subjective semantics, only low level visual features can be extracted in a reliable way. This paper presents a novel framework for video guided behavior monitoring, built around the event modeling concept. It enables users to design their personal models of events combining elementary concept and low level features using expressive formalisms. The framework enables then detection of the events within video streams based on low level features extraction and manual annotations analysis, while taking in consideration uncertainty. Examples depicting content-based events modeling and detection from video surveillance are presented to illustrate the approach.