Euranova has 3 fundamental pillars: explore, craft and serve. The explore pillar of Euranova is an independent research centre dedicated to data science, software engineering and AI.
Through the exploration of tomorrow’s engineering and data science to answer today’s problems, our research centre is dedicated to anticipating the challenges that European businesses face. We find solutions to current and future digital challenges with passion, creativity and integrity.
Euranova has 3 fundamental pillars: explore, craft and serve. The explore pillar of Euranova is an independent research centre dedicated to data science, software engineering and AI.
Through the exploration of tomorrow’s engineering and data science to answer today’s problems, our research centre is dedicated to anticipating the challenges that European businesses face. We find solutions to current and future digital challenges with passion, creativity and integrity.
Spark+AI Summit: a summary
A few weeks ago, Sabri Skhiri and Florian Demesmaeker were in London to attend the Spark+AI summit. They came back with a lot to say about the new features of Spark and the presented use cases! In this article, they will give you their opinion about Databricks’ main announcement, the intakes of their favourite talks and training, and what they thought of the new name of the conference. A new name This year, Spark expanded the summit’s scope and renamed it “Spark + AI Summit”. The goal of Databricks, announced by its co-founder Ali Ghodsi, is to incorporate unified aspects of data and AI. Florian Demesmaeker, our R&D engineer, explains: “In some of the keynote talks, the speakers talked about use cases where the job of the data engineer is strongly reduced. The data scientists can easily experiment with data, travelling back and forth in time. This means more focus on AI, rather than on the data engineering part that makes all data accessible to the data scientists”. Main announcement In line with this change of name, Databricks announced the release of a complete data science lifecycle on the cloud. Sabri Skhiri, our R&D Director, explains “It is interesting to see that the change in the event name is actually very visible in the change of Databricks’ strategy. Their tools are now completely dedicated to stream ETL, and there is a huge focus on integrated data management”. Databricks’ new features include Databricks Delta which creates data pipeline and provides data views and exploration features. Secondly, the Databricks Runtime ML is a ready-to-use environment providing a set of pre-loaded ML frameworks where the data scientist can play with data. Finally, the MLflow tool allows to simplify the ML models development at enterprise scale. Our R&D Director precises: “Together, these
Flink Forward 2018: What You Want to Know and What You (Will) Need to Know.
Early September 2018, 8 EURA NOVA engineers travelled to Berlin to attend the Flink Forward Conference, dedicated to Apache Flink users and stream processing communities. They came back with a lot to say about the hot topics in stream processing and the presented use cases! In this article, they will give you their opinion about data Artisans’ main announcement, the intakes of their favourite talks, and what they thought makes Flink Forward different from other conferences. First keynote announcement: During the keynote speech, data Artisans announced that they now bring ACID transactions directly on streaming data with data Artisans Streaming Ledger. Charles Bonneau, our software architect, says: “This feature allows ACID transactions between multiple operators’ event-processing operations and internal states. This means that streaming applications can now update multiple states in one transaction. For example, an application that transfers money from one bank account to another can finally be implemented using Flink with strong consistency guarantees. Both bank accounts will have their balance updated at the same time as if there was a master data-management state”. For Sabri Skhiri, our R&D director, this opens the doors to a brand new range of applications, especially in data-driven real-time services but also in streaming data management. He explains: “They are pushing forward the concept of streaming. Now, you could imagine a master data-management state that can be updated by operational streaming applications in real time. This will allow even more complex and advanced use cases of stream processing!”. Favourite talks: In 2 days, each Euranovian attended about 18 talks and use case presentations, with speakers from tech giants such as IBM, Netflix, Alibaba, and Uber as well as speakers from smaller companies. Charles explains: “The conclusions are reassuring: most of them face the same issues that we see at our
Data Mining and ML Techniques Supporting TBS Concept Deployment
Our paper “Data Mining and Machine Learning Techniques supporting Time-based Separation Concept Deployment”, co-written with Eurocontrol and WaPT, has been accepted by the 37th Digital Avionics Systems Conference (DASC) in London, U.K. The paper presents two methods to allow air traffic controllers to deliver separation minima accurately and safely, on the basis of time intervals instead of distances. Importantly, in strong headwind conditions, the aircraft’s groundspeed during approach decreases, meaning that keeping the distance-based separation method results in lower landing rates. At a time of intensified air traffic, this situation leads to considerable delays at airports with significant costs to operators and travellers. With the new methods presented in the paper, capacity can increase by up to 14% in strong wind conditions, and by up to 8% in moderate wind conditions. The paper has been presented in September at DASC 2018. If you wish to go deeper into the subject, do not hesitate to contact our research department at [email protected]. The abstract The Time-Based Separation (TBS) concept consists in the definition of separation minima for aircraft on the final approach to a runway based on time intervals instead of distances, as applied in Distance-Based Separation (DBS) operations. TBS allows for dynamic distance separation reductions in strong headwind conditions so as to preserve time spacing across all wind conditions. However, TBS application entails the use of a support tool providing separation distance indicators depending on the applicable time separation minimum, the aircraft speed profile which also depends on the headwind conditions. This paper details two methodologies allowing a system to compute those TBS indicators so as to allow Air Traffic Controllers to accurately and safely deliver the TBS minima using a separation delivery support tool. The first approach is based on “analytical” data mining and modelling whereas the second one is
Third Workshop on Real-time & Stream Analytics in Big Data
EURA NOVA Research center is proud and excited to organize the third workshop on Real-time and Stream analytics in Big Data, collocated with the 2018 IEEE conference on Big Data. The workshop will take place in December in Seattle, USA. As the world become more connected, flood of digital data is getting generated, in high volume, and in a high velocity. For industries such as financial markets, telecommunications, Smart Cities, manufacturing, or healthcare, there is an increasing need to process, and analyze, these data streams in real time. These past two years, we have seen arriving another usage of Stream & complex event processing: the data management. New architecture patterns have been proposed to resolve data pipeline and data management within enterprise. After the success of the two first edition, this is an excellent opportunity to engage in discussions with experts and researchers, to refine new opportunities and use cases required by the industry. Authors are invited to contribute to the conference by submitting articles in the (among others) following areas: Scalable real-time decision algorithms, IoT analytics & stream mining, Data pipelines & Data management with Streams and Stream ETL and Real-Time Data Warehouse. Want to submit a paper? Check out the workshop website to find all the information you will need. Your paper will be reviewed by a prestigious panel of international experts from both the academic and the industrial worlds.
Graph BI & Analytics: Current State and Future Challenges
Our paper “Graph BI & Analytics: Current State and Future Challenges” has been accepted for publication at the 20th International Conference on Big Data Analytics and Knowledge Discovery, taking place in Regensburg, Germany. The paper presents the state of the art of graph BI & analytics, with a focus on graph warehousing. We survey the topics of graph modelling, management, querying, and processing in graph warehouses. Then we conclude by discussing future research directions for solving complex graph problems, building native graph components and intelligent techniques to assist end-users in building and analysing the graph. More importantly, the paper calls for the development of intelligent, efficient and industry-grade graph data warehousing systems to support the structure-driven management and analytics of data efficiently. While adopting a template that is similar to the traditional BI systems, the graph BI that is presented here extends current systems with graph analytics capabilities that deliver graph-derived insights. The paper has been presented in September at DaWak 2018, you can now find the full version here. If you wish to go deeper into the subject, don’t hesitate to contact our research department at [email protected]. Abstract. In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning
Second Spring School Big Data Analytics
EURA NOVA Research Center is both proud and happy to lead the Second Spring School Big Data Analytics that will be held in Tunis, from the 20th to the 22nd of March 2018. Sabri Skhiri and Aymen Cherif will talk about their favorite topics: Deep Learning TensorFlow CNN Architecture Unsupervised Learning Complex Event Processing Stream processing & micro-services Check out the complete agenda and register on the event website : https://sites.google.com/view/ssbda2018/welcome The conference is organised by the Ecole Polytechnique de Tunisie.
The Next Activities of our R&D Centre in Marseille
The French branch of EURA NOVA will take part in two great tech events in the following days and weeks. On the 22nd of February, data scientist Thomas Peel will give a talk titled “Machine Learning à l’ère du RGPD” (Machine learning and the General Data Protection Regulation) on the opening day of the Colloquium intelligence artificielle, machine learning, data science to be held at the grand amphitheatre of the Saint-Charles campus in Marseille. Other great speakers from INRIA, Google, Provence Innovation, and Criteo will be featured. The event is free but registration is mandatory. Practical information: What? Colloquium intelligence artificielle, machine learning, data science When? Thursday 22nd of February Where? Grand amphithéâtre, campus Saint-Charles, – 3, place Victor Hugo – case 39 – 13331 MARSEILLE Cedex 03 Registration: : https://framaforms.org/conferences-ia-data-science-machine-learning-i2mlis-1518019875 On the 12th of March, the French branch of EURA NOVA is organising the Marseille Community Event, supported by the Neo4j GraphTour. Two speakers are already announced: R&D project manager Cécile Péreaira will present a text-mining use case with Neo4j in biology, and data scientist Antoine Bonnefoy will sum up the Parisian Neo4j conference, from technology and business viewpoints. After the talks, all attendees will be offered a casual dinner to pursue the discussion. Practical information: What? Marseille Community Event – Neo4j GraphTour When? Monday the 12th of March, from 6:30 PM to 8:30 PM Where? Le Wagon, 167 Rue Paradis, Marseille Registration: : https://www.eventbrite.fr/e/billets-neo4j-graphtour-marseille-community-event-42714338737?utm_campaign=new_event_email&utm_medium=email&utm_source=eb_email&utm_term=viewmyevent_button
Discovering Interesting Patterns in Large Graph Cubes
Due to the increasing importance and volume of highly interconnected data, such as in social or information networks, a plethora of graph mining techniques have been designed to enable the analysis of such data. In this work, we focus on the mining of associations between entity features in networks. We model each entity feature as a dimension to be analyzed. Consequently we build our approach on top of the existing graph cube framework which is an extension of the concept of the data cube to networks. Our task is particularly challenging because it requires the analysis of both the initial multidimensional network and all its subsequent aggregate forms. As soon as we deal with a big data situation it is impossible for an analyst to consider manually all the possible views of the network data. The aim of this work is to design an algorithm for the discovery of interesting patterns in large graph cubes. Thus, instead of examining all the possible aggregations manually, the proposed technique leads the analyst to the interesting associations or patterns in the multidimensional network. Furthermore, we study the application of existing algorithms from the frequent itemset mining literature on graph data and propose a mapping between the two settings. Florian Demesmaeker, Amine Ghrab, Siegfried Nijssen, Sabri Skhiri: Discovering interesting patterns in large graph cubes. 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 2017, pp. 3322-3331. Click here to access the paper.
Second Workshop on Real-Time and Stream Analytics in Big Data
EURA NOVA is thrilled to share the news with you: we are organizing our second workshop collocated with the 2017 IEEE International Conference on Big Data. The workshop will take place in December in Boston, MA, USA. Stream processing and real-time analytics have caught the interest of the industry lately. Many use cases are waiting for relevant and efficient solutions to be developed. Such use cases include event-driven marketing, dynamic network management & optimization, real-time recommendation, context-aware applications and real-time fraud detection. After the success of the first edition, this is an excellent opportunity to bring together the industry and academics to discuss, to explore and to refine new opportunities and use cases in the area. The workshop will benefit both researchers and practitioners interested in the latest research in real-time and stream processing. The workshop will showcase prototypes and products leveraging big data technologies as well as models, efficient algorithms for scalable complex event processors and context detection engines, or new architecture leveraging stream processing. Want to submit a paper? Check out the workshop website to find all the information you will need. Your paper will be reviewed by a prestigious panel of international experts from both the academic and the industrial worlds.
Next Workshop on Graph Business Intelligence
EURA NOVA is organizing their second workshop collocated with an international conference. This time, the workshop will be collocated with the 21th European Conference on Advances in Databases and Information Systems. It will take place in September in Cyprus and will bring together industrial and academic stakeholders to discuss, explore and refine new opportunities and use cases in the area of Graph Business Intelligence. Want to be part of the fun? Check out the workshop website to find all the information you need to know and submit your paper. Our researchers Sabri Skhiri, Salim Jouili and Amine Ghrab cannot wait to read your papers and meet you in Nicosia.
Big Data Architectures at Universitat Politècnica de Catalunya
Today and Wednesday (the 13rd and the 15th of March 2017), our R&D Director will be in Barcelona to give a course about Big Data Architectures. The objective is to learn the basic concepts and details to take into account when designing a Big Data Architecture. The student will learn the impact of technical & functional constraints on the storage and processing choices. Going further the course will show, through industrial use cases, the raise of new architecture patterns. The course includes a practical part with hands-on session on distributed frameworks. Contents : Terminology & Concepts Distributed architecture Big Data Storage Big Data Processing Big Data Architecture Patterns (Hands-on session) Distributed processing with Apache Flink / Spark Data manipulation with Apache Pig For more details, contact Oscar Romero ( [email protected] ) Want to host Sabri Skhiri for a course in your university? Contact [email protected]
ENX University in Tunis
On the 9th and 10th of May 2017, the R&D Director of EURA NOVA Sabri Skhiri will lecture on Big Data and Data Science at the Polytechnic School of Tunisia. The course will be hosted by the SERCOM laboratory. After the launch of EURA NOVA Tunis last September, this course will be a new opportunity for us to bond a little more with Tunisians, especially students. Indeed, EURA NOVA offers programmes in collaboration with universities, such as boot camps, master thesis, research internships and PhDs, and engineering internships. We hope that this lecture will make Polytechnic students want to explore Data Science with us and join the pack! Want to organise a lecture on Big Data and Data Science in your own university? Contact [email protected] and ask for ENX University offer. Here is the detailed programme [in French] Mardi 9 mai 2017: Architecture BIG DATA (partie 1) Matin (8h30-12h30) Terminologie et concepts généraux Architecture distribuée Stockage du Big Data : NoSQL, NewSQL, Systèmes de fichiers distribués Pause déjeuner : 12h30-14h Après-midi : 14h-17h Travaux pratiques : Préparation de données : Script Pig Introduction à Pig Exercice de préparation de données ______________________________________________________ Mercredi 10 mai 2017 : Architecture BIG DATA (partie 2) Matin (8h30-12h30) Traitement du Big Data : Batch et Streaming Patrons d’architecture Big Data Architectures adoptées dans des contextes industriels : Etude de cas Pause déjeuner : 12h30-14h Après-midi : 14h-17h Travaux pratiques sur Apache Spark/Flink Introduction à Flink et commande Scala de base Traitement de données en batch et en stream