Site Web de l'équipe BD du LIP6 / LIP6 DB Web Site https://bd.team.lip6.fr/ 2026-10-07T20:06:06+00:00 https://bd.team.lip6.fr/ https://bd.team.lip6.fr/_media/wiki/logo.png text/html 2023-11-17T14:47:13+00:00 Anonymous (anonymous@undisclosed.example.com) https://bd.team.lip6.fr/site/recherche/logiciels/antm?rev=1700232433&do=diff ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics Dynamic topic models are effective methods that primarily focus on studying the evolution of topics present in a collection of documents. These models are widely used for understanding trends, exploring public opinion in social networks, or tracking research progress and discoveries in scientific archives. Since topics are defined as clusters of semantically similar documents, it is necessary to observe the changes in the content… text/html 2023-11-17T14:38:31+00:00 Anonymous (anonymous@undisclosed.example.com) https://bd.team.lip6.fr/site/recherche/logiciels/atem?rev=1700231911&do=diff ATEM : A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives ATEM is a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a scientific corpus. ATEM explores a new notion of contextual emergence for the discovery of emerging interdisciplinary research topics based on the dynamics of citation links in … text/html 2023-11-17T14:48:02+00:00 Anonymous (anonymous@undisclosed.example.com) https://bd.team.lip6.fr/site/recherche/logiciels/ctc?rev=1700232482&do=diff CTC: Contextualized Topic Coherence Metrics This research introduces a new family of topic coherence metrics called Contextualized Topic Coherence Metrics (CTC) that benefits from the recent development of Large Language Models (LLM). CTC includes two approaches that are motivated to offer flexibility and accuracy in evaluating neural topic models under different circumstances. Our results show automated CTC outperforms the baseline metrics on large-scale datasets while semi-automated CTC outpe… text/html 2023-11-17T17:37:38+00:00 Anonymous (anonymous@undisclosed.example.com) https://bd.team.lip6.fr/site/recherche/logiciels/epique?rev=1700242658&do=diff EPIQUE: Topic Extraction and Alignment for Large Scientific Document Collections Vers une épistémologie quantitative - Reconstruire l'évolution des sciences à grande échelle L'évolution des connaissances scientifiques est directement liée à l'histoire de l'humanité. Les archives documentaires et bibliographiques comme le « Web Of Science » (WoS) ou PubMed représentent des sources fécondes pour l’analyse et la reconstruction de cette évolution. Le projet de recherche proposé part des travaux de… text/html 2017-04-26T11:05:57+00:00 Anonymous (anonymous@undisclosed.example.com) https://bd.team.lip6.fr/site/recherche/logiciels/experimaestro?rev=1493204757&do=diff experimaestro : Computer science experiment scheduler and manager Experimaestro is an experiment manager based on a server that contains a job scheduler (job dependencies, locking mechanisms) and a framework to describe the experiments with JavaScript or in Java. text/html 2023-11-17T14:36:28+00:00 Anonymous (anonymous@undisclosed.example.com) https://bd.team.lip6.fr/site/recherche/logiciels/jsonschemainference?rev=1700231788&do=diff Schema Inference from Massive JSON Datasets Description JSON data collections are usually massive and schema-less. Inferring a schema describing the structure of these collections is crucial for formulating meaningful queries and for adopting schema-based optimizations. The implementation allows for inferring schema information from very large JSON data collections. text/html 2017-04-26T11:06:22+00:00 Anonymous (anonymous@undisclosed.example.com) https://bd.team.lip6.fr/site/recherche/logiciels/rdfdist?rev=1493204782&do=diff RDFdist : RDF distribution approaches using Spark This wiki page provides information about the experiments RDF distribution approaches using Spark. This information consists of i) the query workload ii) the source code for both data preparation and query evaluation, and iii) the description of two datasets used in the experiments. For the sake of reproducibility, each source code is provided as a script which can be directly executed in the spark shell. text/html 2023-11-17T17:39:07+00:00 Anonymous (anonymous@undisclosed.example.com) https://bd.team.lip6.fr/site/recherche/logiciels/sparqlwithspark?rev=1700242747&do=diff SPARQL query processing with Apache Spark This wiki is a companion to the following publications: * SPARQL Graph Pattern Processing with Apache Spark * SPARQL query processing with Apache Spark * HAQWA: a Hash-based and Query Workload Aware Distributed RDF Store * On the Evaluation of RDF Distribution Algorithms Implemented over Apache Spark It provides access to the resources related to the evaluation section of SPARQL query processing with Apache Spark. See also RDFdist concerning … text/html 2021-09-16T10:50:35+00:00 Anonymous (anonymous@undisclosed.example.com) https://bd.team.lip6.fr/site/recherche/logiciels/start?rev=1631789435&do=diff Logiciels index <https://gitlab.lip6.fr> <https://github.com/> <https://gitlab.com/>