New France-Brazil research partnership: Inria and LNCC sign Memorandum of Understanding, 2 July 2020.

Inria and LNCC, the Brazilian National Scientific Computing Laboratory, signed a Memorandum of Understanding to strengthen their collaboration  in High Performance Computing, Big Data and Artificial Intelligence. It is headed by Frédéric Valentin (LNCC, Inria International Chair) and Patrick Valduriez.

 

Permanent link to this article: https://team.inria.fr/zenith/new-france-brazil-research-partnership-inria-and-lncc-sign-memorandum-of-understanding-2-july-2020/

Seminar by Khadidja Meguelati “Clustering Massivement Distribué via Mélange de Processus de Dirichlet” 9 March 2020

Séminaire Zenith  : 9 mars 2020, 14h
Campus St Priest, BAT5, 03.124
Clustering Massivement Distribué via Mélange de Processus de Dirichlet
Khadidja Meguelati
Zenith, Inria & LIRMM
La classification non supervisée (ou clustering) a pour objectif d’identifier des classes pertinentes dans les données. elle est largement utilisée dans de nombreuses applications telles que le marketing, la reconnaissance de patterns, l’analyse de données et le traitement d’images. Déterminer le nombre optimal de clusters dans un ensemble de données est un défi fondamental qui a ouvert de nombreuses directions de recherche. De multiples méthodes sont alors proposées pour résoudre ce problème.
Le Mélange de Processus de Dirichlet (DPM) est utilisé pour le clustering car il permet de définir automatiquement le nombre de classes, mais les temps de calculs qu’il implique sont généralement trop importants, nuisant à son adoption et rendant inefficaces ses versions centralisées.
Nous visons le problème de la parallélisation du mélange de processus de Dirichlet pour améliorer ces performances en exploitant des environnements massivement distribués. En effet, d’après la littérature, l’algorithme de DPM distribué fait appel à de nombreux problèmes tels que : l’équilibre de charge entre les nœuds de calcul, les coûts de communication, et le plein bénéfice de propriétés du DPM.
Nous proposons deux nouvelles approches pour le clustering parallèle via DPM. Tout d’abord, nous proposons DC-DPM (Clustering Distribué via mélange de processus de Dirichlet), une version parallélisée, qui permet le clustering de millions de points de données, ce qui représente un vrai défi. Nos expérimentations, tant sur des données synthétiques que réelles, illustrent la performance de notre approche. Comparativement, l’algorithme centralisé ne passe pas à l’échelle. Son temps de réponse est de plus de 7 heures sur des données de 100K points, quand notre approche prend moins de 30 secondes.
Dans un deuxième temps, nous nous intéressons au problème de dimensionalité de données qui devient un défi important avec les obstacles numériques et théoriques dans ce cas. Nous proposons HD4C (Clustering de Dirichlet Distribué pour des Données de Haute Dimension), une solution de clustering Parallèle qui s’adresse à la dimensionnalité par deux moyens. Premièrement, elle s’adapte à des données massives en exploitant les architectures distribuées. Deuxièmement, elle effectue le clustering de données de haute dimension telles que les séries temporelles (en fonction du temps), les données hyperspectrales (en fonction de la longueur d’onde), etc. Nous avons réalisé des expériences exhaustives  sur des jeux de données synthétiques et réels pour confirmer l’efficacité de notre solution.

Permanent link to this article: https://team.inria.fr/zenith/seminar-by-khadidja-meguelati-clustering-massivement-distribue-via-melange-de-processus-de-dirichlet-9-march-2020/

Seminar by Patrick Valduriez “Innovation : startup strategies” 19 March 2020 ** postponed

**Postponed to June

Zenith seminar: 19 march 2020, 10h30
Campus Saint Priest, BAT5, 01.124

Innovation : startup strategies
Patrick Valduriez
Inria and LIRMM, Univ. Montpellier, France

Technological innovation as driven by startups is hard to formalize (and manage) as the context may be unknown or quickly changing. To be successful, the innovation process involves not only inventions (new methods) but also context, e.g. user behavior, and timing, e.g. market readiness. In this talk, I illustrate various innovation strategies based on startup success stories, in particular LeanXcale, which delivers a new generation HTAP DBMS product. I also give hints to promote innovation within startups.

Permanent link to this article: https://team.inria.fr/zenith/seminar-by-patrick-valduriez-innovation-startup-strategies-19-march-2020/

The book “Principles of Distributed Database Systems – Fourth Edition” is now online.

The book Principles of Distributed Database Systems – Fourth Edition (700 pages, Springer), co-authored with Prof. Tamer Özsu (University of Waterloo), is now online, with major revision of previous chapters and addition on new material on big data, NoSQL, NewSQL, polystores, web data integration and blockchain.

The paper version is also available at various online stores (Amazon, …).

Permanent link to this article: https://team.inria.fr/zenith/the-book-principles-of-distributed-database-systems-fourth-edition-is-now-online/

Zenith winner at the Global Pytorch Summer Hackaton 2019

Antoine Liutkus and Fabian Stoter won the second place at the Global Pytorch Summer Hackaton 2019 organized by FaceBook with the open-unmix software. See the demo here.

Permanent link to this article: https://team.inria.fr/zenith/zenith-second-at-the-global-pytorch-summer-hackaton-2019/

Séminaire en ligne Franco-Africain par Patrick Valduriez “Blockchain 2.0: opportunités et risques”, 13 nov. 2019

Séminaire en ligne Franco-Africain du LIRIMA

Diffusé par l’agence universitaire de la Francophonie (AUF) et Inria

Salle Métivier, Inria Rennes, 13 nov 2019 à 16h

Blockchain 2.0: opportunités et risques
Patrick Valduriez

Inria and LIRMM, Université de Montpellier

Permanent link to this article: https://team.inria.fr/zenith/seminaire-en-ligne-franco-africain-par-patrick-valduriez-blockchain-2-0-opportunites-et-risques-13-nov-2019/

Inaugural lecture by Esther Pacitti: “Data Processing: an evolutionary and multidisciplinary perspective”, CEFET/RJ, Rio de Janeiro on 12 August 2019

Inaugural lecture by Esther Pacitti
Graduate Program in Computer Science , CEFET/RJ, Rio de Janeiro
12 August 2019, 10:00– Auditorium 5,  Maracanã campus

Data Processing: an evolutionary and multidisciplinary perspective
E. Pacitti
Inria and LIRMM, Montpellier, France

The inaugural lecture will address the context of the growth of the amount and variety of data (images, audio, matrixes, text, etc.), produced in various areas (social networks, agronomy, botany, medicine and others), which has also increased the technological and research challenges in the processing  of this large volume of data, termed by the term Big Data.

In the lecture, Professor Esther Pacitti will present a vision of the evolution of data processing  methods from relational databases, distributed databases, and big data to data science. It will also expose some specific applications in Agronomy, Botany and Seismology, and share research experiences in France and Brazil.

Permanent link to this article: https://team.inria.fr/zenith/inaugural-lecture-by-esther-pacitti-data-processing-an-evolutionary-and-multidisciplinary-perspective-cefet-rj-rio-de-janeiro-on-12-august-2019/

New book on Data-Intensive Workflow Management, May 2019

Release of the new book:
Data-Intensive Workflow Management: For Clouds and Data-Intensive and Scalable Computing Environments.
Synthesis Lectures on Data Management

by Daniel de Oliveira (Universidade Federal Fluminense, Brazil), Ji Liu & Esther Pacitti (University of Montpellier, Inria & CNRS, France)
May 2019, 179 pages, Morgan&Claypool Publishers.
(https://doi.org/10.2200/S00915ED1V01Y201904DTM060)

Permanent link to this article: https://team.inria.fr/zenith/new-book-on-data-intensive-workflow-management-may-2019/

Seminar by Patrick Valduriez at Inria Lille “The Case for Hybrid Transaction Analytical Processing”, 17 May 2019

Seminar by Patrick Valduriez (Inria) at Inria, Lille
17 May, 10:30 – Amphi B – Inria – Bat B

The Case for Hybrid Transaction Analytical Processing
P. Valduriez
Inria and LIRMM, Montpellier, France

Abstract. Hybrid Transaction Analytical Processing (HTAP) is poised to revolutionize data management. By providing online analytics over operational data, HTAP systems open up new opportunities in many application domains where real-time decision is critical. Important use cases are proximity marketing, real-time pricing, risk monitoring, real-time fraud detection, etc. HTAP also simplifies data management, by removing the traditional separation between operational database and data warehouse/ data lake (no more ETLs!). However, a hard problem is scaling out transactions in mixed operational and analytical workloads over big data, possibly coming from different data stores (HDFS, SQL, NoSQL, …).
In this talk, I will introduce HTAP systems and illustrate with LeanXcale, a new generation HTAP DBMS that provides ultra-scalable transactions, big data analytics, SQL/JSON support and polystore capabilities

 

Permanent link to this article: https://team.inria.fr/zenith/seminar-by-patrick-valduriez-at-inria-lille-the-case-for-hybrid-transaction-analytical-processing-17-may-2019/

Zenith seminar: Hervé Bredin “Neural speaker diarization” 13 May 2019

Zenith seminar : 13/05/2019, 10h30

Campus Saint Priest, BAT5-02.124

Neural speaker diarization

Hervé Bredin (CNRS, LIMSI)

Speaker diarization is the task of determining “who speaks when” in an audio stream. It is an enabling technology for multiple downstream applications such as meeting transcription or indexing of ever-growing audio-visual archives.

Speaker diarization workflows usually consist of four consecutive tasks: speech activity detection, speaker change detection, speech turn clustering, and re-segmentation.

Recent advances in deep learning led to major improvements in multiple domains such as computer vision or natural language processing, and speaker diarization is no exception to the rule. In this talk, I will discuss our recent progress towards end-to-end neural speaker diarization (including speech and overlap detection with recurrent neural networks, and triplet loss for speaker embedding).

# References

“Tristounet: Triplet Loss for Speaker Turn Embedding.” Bredin 2017. ICASSP.

“Speaker Change Detection in Broadcast TV Using Bidirectional Long Short- Term Memory Networks.”

Yin 2017. Interspeech.

“Neural Speech Turn Segmentation and Affinity Propagation for Speaker Diarization.”

Yin 2018. Interspeech.

# Code

pyannote.audio: Neural building blocks for speaker diarization: speech activity detection, speaker change detection, speaker embedding github.com/pyannote/pyannote-audio

Permanent link to this article: https://team.inria.fr/zenith/zenith-seminar-herve-bredin-neural-speaker-diarization-13-may-2019/