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

Monitoring Sustainable Development Goal (SDG) indicators requires integrating heterogeneous open datasets from sources such as relational databases, NoSQL stores, and APIs. While SDG indicators follow standardized definitions, open data sources are often fragmented, schema-less, and inconsistent, making both integration and computation challenging. In this demonstration, we present SDG-KG , a spatio-temporal Knowledge Graph (KG) framework designed to structure metadata, guide data retrieval, and formalize indicator computation workflows. Our approach leverages graph-based modeling to construct a Metadata Graph, apply conflict resolution techniques when multiple sources provide overlapping data, and dynamically generate query-driven execution plans. Through an interactive interface, users can explore United Nations specifications, inspect data provenance and the generated KG, and visualize the computed indicators.

International audience

International audience

Reasoning on inconsistent and uncertain data is challenging, especially for Knowledge-Graphs (KG) to abide temporal consistency. Our goal is to enhance inference with more general time interval semantics that specify their validity, as regularly found in historical sciences. We propose a new Temporal Markov Logic Networks (TMLN) model which extends the Markov Logic Networks (MLN) model with uncertain temporal facts and rules. Total and partial temporal (in)consistency relations between sets of temporal formulae are examined. We then propose a new Temporal Parametric Semantics (TPS) which allows combining several sub-functions leading to different assessment strategies. Finally, we present the new NeoMaPy tool, to compute the MAP inference on MLNs and TMLNs with several TPS. We compare our performances with state-of-the-art inference tools and exhibit faster and higher quality results.

Markov Logic Networks (MLN) are used for reasoning on uncertain and inconsistent temporal data. We proposed the TMLN (Temporal Markov Logic Network) which extends them with sorts/types, weights on rules and facts, and various temporal consistencies. The NeoMaPy framework integrates it as a knowledge graph based on conflict graphs which offers flexibility for reasoning with parametric Maximum A Posteriori (MAP) inferences, efficiency with an optimistic heuristic and interactive graph visualization for results explanation.

Abstract To maximize the impact of an advertisement campaign on social networks, the real-time bidding (RTB) systems aim at targeting the most influential users of this network. Influence maximization (IM) is a solution that addresses this issue by maximizing the coverage of the network with top-k influencers who maximize the diffusion of information. Associated with online advertising strategies at Web scale, RTB is faced with complex ad placement decisions in real time to deal with a high-speed stream of online users. To tackle this issue, IM strategies should be modified in order to integrate RTB constraints. While most traditional IM methods deal with static sets of top influencers, they hardly address the dynamic influence targeting issue by integrating short time decision, no interchange and stream’s incompleteness. This paper proposes a real-time influence maximization approach which takes influence maximization decisions within a real-time bidding environment. A deep analysis of influence scores of users over several social networks is presented as well a strategy to guarantee the impact of an IM strategy in order to define the budget of an ad campaign. Finally, we offer a thorough experimental process to compare static versus dynamic IM solutionswrt. influence scores.

International audience

Digital music scores are a way to present music notation and lack of semantic information useful for musicology purposes in order to manipulate music concepts. We propose a general approach to extend score encodings with semantic annotations. It relies on an ontology of music notation designed to integrate semantic music elements either extracted or produced by a knowledge process. We illustrate the whole mechanism by extracting RDF facts based on the identification of dissonances in Renaissance counterpoint.

We consider the emerging field of music score libraries, where documents rely on a music notation markup language such as MusicXML or MEI. We propose to model as synchronized time-series the music structure that can be extracted from such documents, together with an algebra that operates in closed form and allows manipulations, restructurings, and combinations of music scores stored in a database. We formally present the model, its algebraic operators, and finally show how our approach can serve as a building block for a query and analytic language on large collections of XML-encoded music scores.

Publish/Subscribe (Pub/Sub) systems have been designed to face the exponential growth of information published on the Web by subscribing to sources of interest which produce flows of items. However users may receive some information several times, or information that does not contain any new content, and conversely miss some information of interest hidden in all information received. Pub/Sub systems are consequently witnessing a real challenge to efficiently filter relevant information. We propose in this paper a scalable approach for filtering news (items) which match the user interests (expressed as subscriptions). Introducing for the first time Term Discrimination Value (TDV) in this context, which allows to measure how a term discrimines an item, we filter out in real-time items whose content has already been notified recently to the user, either in another item (filtering by novelty) or globally in his recent history (filtering by diversity). Our experiments illustrate the impact of our different parameters and confirm the scalability of our approach and the relevance of the results notified.

Content syndication has become a popular way for timely delivery of frequently updated information on the Web. It essentially enhances traditional pull-oriented searching and browsing of web pages with push-oriented protocols. However many Web syndication applications imply a tight coupling between feed producers and consumers and do not help users to find, in all information they received, items with interesting and new content. We present the FiND Pub/Sub system which integrates an in-memory filtering process based on keyword subscriptions. Unlike existing proposals, FiND is designed for real-time notifications on item streams. This demonstration illustrates the main features of the FiND system namely (i) a scalable real-time notification process when the most important terms of the subscription are matched, (ii) a tunable filtering by novelty and diversity to reduce user flooding.

The demonstration is devoted to the desktop-level interactions offered by Cador, a content-based document management system currently under development. Cador provides a rule-based language to query and manipulate large collections of documents distributed in repositories. The language is able to define the content of Virtual File Systems (VFS) as views over the document collections. This feature allows users to combine their familiar interface and desktop-based softwares with the powerful search and transformation tools provided by the underlying system.

The explosion of published information on the Web leads to the emergence of a Web syndication paradigm, which transforms the passive reader into an active information collector. Information consumers subscribe to RSS/Atom feeds and are notified whenever a piece of news (item) is published. The success of this Web syndication now offered on Web sites, blogs, and social media, however raises scalability issues. There is a vital need for efficient real-time filtering methods across feeds, to allow users to follow effectively personally interesting information. We investigate in this paper three indexing techniques for users' subscriptions based on inverted lists or on an ordered trie. We present analytical models for memory requirements and matching time and we conduct a thorough experimental evaluation to exhibit the impact of critical workload parameters on these structures.

We present RoSeS, a running system for large-scale content-based RSS feed filtering and aggregation. The implementation of RoSeS is based on standard database concepts like declarative query languages, views and multi-query optimization. Users create personalized feeds by defining and composing content-based filtering and aggregation queries on collections of RSS feeds. These queries are translated into continuous multi-query execution plans which are optimized using a new cost-based multi-query optimization strategy.

We present the WebContent platform for managing distributed repositories of XML and semantic Web data. The platform allows integrating various data processing building blocks (crawling, translation, semantic annotation, full-text search, structured XML querying, and semantic querying), presented as Web services, into a large-scale efficient platform. Calls to various services are combined inside ActiveXML [8] documents, which are XML documents including service calls. An ActiveXML optimizer is used to: ( i ) efficiently distribute computations among sites; ( ii ) perform XQuery-specific optimizations by leveraging an algebraic XQuery optimizer; and ( iii ) given an XML query, chose among several distributed indices the most appropriate in order to answer the query.