Our engineer Amine Ghrab presented his PhD public defense on the BI on Graph Project

Last Thursday, our engineer Amine Ghrab presented the BI on Graph project during his PhD public defense. Amine did an amazing job at the edge between Industry & Academia. Amine’s thesis was done in collaboration with the CODE/WIT Lab of the Université Libre de Bruxelles and the Universitat Politècnica de Catalunya, with the support of Prof. Oscar Romero & Prof. Esteban Zimanyi!

In his PhD thesis, Amine defined how BI environments can be enriched with Graph Data structures. Over the past decade, business and social environments have become increasingly complex and interconnected. As a result, graphs have emerged as a widespread abstraction tool at the core of the information infrastructure that supports these environments. In particular, the integration of graphs into data warehouse systems has appeared as a way to extend current information systems with graphs management and analysis capabilitiesGoing forward, Amine redefined the concepts of multidimensional cube on graph and showed how it can open new doors for data analysts. Finally, he showed how a graph data warehouse architecture can be defined.

Congratulation for your achievements!

You can find below a list of related publications:

Internship & Master Thesis Offer – 2021

Our master thesis and internships offers for the coming year, supervised by our software engineering department or by our research & development department, will be available in the course of November, and will cover the following research topics:

 

Regarding data privacy: 

  • Legal entity relations with knowledge graph
  • Legal NLP
  • Privacy by design
  • Topic modeling
  • Text summarisation

 

Regarding data automation

  • GAN for multimodal representation
  • AutoML
  • Optimization methods
  • Computer vision
  • Graph Embeddings

 

Regarding data pipelines

  • Reinforcement learning
  • Optimisation methods
  • Stream Processing
  • CEP
  • Network compression

 

Regarding data quality

  • Denoising technique
  • GAN for missing data
  • Semi-Supervised learning
  • Data cleaning
  • Attention Model for Structural dep.

 

Each project is an opportunity to feel both empowered and responsible for your professional development and to address tomorrow’s challenges in ICT, coached by the Eura Nova crew. The detailed offers will be available mid-november. In the meantime, do not hesitate to contact us at career@euranova.eu for any question regarding internships and master thesis!

As an example, the documents listed below present our 2020 master thesis and internships:

TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes

Graphs are a fundamental structure that provides an intuitive abstraction for modelling and analyzing complex and highly interconnected data. Given the potential complexity of such data, some approaches proposed extending decision-support systems with multidimensional analysis capabilities over graphs. In this paper, we introduce TopoGraph, an end-to-end framework for building and analyzing graph cubes. TopoGraph extends the existing graph cube models by defining new types of dimensions and measures and organizing them within a multidimensional space that guarantees multidimensional integrity constraints. This results in defining three new types of graph cubes: property graph cubes, topological graph cubes, and graph-structured cubes. Afterwards, we define the algebraic OLAP operations for such novel cubes. We implement and experimentally validate TopoGraph with different types of real-world datasets.

 

The paper will be published soon in Information Systems Frontiers, and is already available online on Springer. Currently, it is unfortunately available only to subscribers, but do not hesitate to reach out to us for more information!

 

Amine Ghrab, Oscar Romero, Sabri Skhiri, Esteban Zimányi, TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes, published in Information Systems Frontiers (2020).

 

 

Schloss Dagstuhl: Where Computer Science Meets

Which direction stream and complex event processing is going to take? Last week, the world’s best-known international researchers met in Schloss Dagstuhl, Germany,  to present and discuss their research. Among the members were present Avigdor Gal, Professor at the Israel Institute of Technology, Alessandro Margara, Assistant Professor at the Polytechnic University of Milan, or Till Rohrmann, engineering lead at Veverica.

Invited to talk about the requirements and needs from the industry, our R&D director Sabri Skhiri explains: “The seminar brought together world-class computer scientists and practitioners working on complex event recognition, distributed systems, databases, stream reasoning and artificial intelligence. Our objective was to disseminate the recent foundational results in each of these isolated fields among all participants, to identify the open problems that need to be resolved, and to establish new research collaborations among these fields”.

What were the big trends and intakes gathered by those brilliant minds? Let’s find out with Sabri!

 

 

The Big Trends

This seminar is a bit particular as it does not show any trends but rather gives a picture of all the communities working on CER in a way or another. I was fascinated by the diversity of researchers. I  did not expect to see such a rich variety of fields: knowledge representation, spatial reasoning, logic-based reasoning, data management, learning-based approaches, event-driven processing, process mining, database theory, stream mining,… According to me, the composite event recognition models that are the best at recognising complex events would include:

  1. Data flow model
  2. Ontology-based and reasoning model
  3. Symbolic reasoning model
  4. Automata-based model

We also identified common challenges across these models and communities. The three priority topics areas we identified are:

  1. Expressivity: composability & hierarchies
  2. Evaluation strategy, parallelization and distribution
  3. Uncertainty management

 

Favourite Talk

Kurt Rothermel from TU Stuttgart – Time-sensitive Complex Event Processing

My first reaction to load shedding was: “It is useless since customers do not want to lose any event, that is why so much effort is spent today on exactly once semantics…“. However, there is a trend today in stream processing, which is the trade-off between cost, latency, and correctness. Tyler Akidau described this challenge as a choice between one of three propositions: fast and correct, cheap and correct, or fast and cheap.  Tyler was talking about streaming but that rule applies in the same way in a CEP context. The load shedding strategy directly falls in the third proposition. In this perspective, the work of Kurt is highly relevant.

 

Favourite Tutorial

Jacopo Urbani & Fredrik Heintz – Stream Reasoning

Concretely, stream reasoning is incremental reasoning over rapidly changing information. The tutorial opened new perspectives on stream processing for me. It tried to answer a very interesting question: how can you provide reasoning about context from streams of data? I definitely come from the database and event-based systems communities and I did not know at all that stream reasoning was so mature. This community has been evolving from having a continuous version of SPARKQL to a complete distributed stream reasoning semantics. It is interesting to see that the work we have done in the LEAD algebra and semantics is deeply inspired by this community. However, we have never used any reasoning logic on top of LEAD. But after a few hours of the tutorial, I realise that (1) reasoning can be used for query rewriting and optimisation (2) it is worth evaluating at least BigSR,  the LARS implementation on Flink.

 

Avigdor Gal & Ruben Mayer – Distributed and Event-Based Systems

Avidgor is a kind of pop star for the stream processing and distributed systems community, or at least for me! The papers he published about a probabilistic CEP engine with late arrival and event uncertainty were visionary.

The speakers started by explaining the basics of stream processing then went deeper into the event recognition language and architecture. They detailed pub/sub applied to event recognition and explained the data flow model, which consists of a single unified data processing model where the stream and batch paradigms are the same.  This last part was based on Tyler Akidau’s paper.

A second part of the talk focused on elasticity on streams. Stream fission puts operators among different categories:

  • Firstly, key-based operators, that is a group by operation (as in SQL)
  • Secondly, window-based operators enable to split processing that needs to have multiple event types correlated with different keys within the same operator
  • Finally, pane-based operators enable a split-merge strategy where you distribute and merge the result.

Interestingly, Avigdor presented his work about late-arrival processing from a probabilistic viewpoint and not from the watermark perspective. Usually, modern stream processing frameworks use watermarks in order to take into account events that arrive later. Avigdor presented a probabilistic approach to this issue.

 

What are late-arrival events?

Imagine we want to count the number of cars entering a road segment every three minutes: we have a “tumbling window” every 3 minutes. If an event (ie a car) arrives at 2’55 second in the window but is stuck somewhere in the network for 6 sec, it is called a late-arrival event. The processing time (the time at which the CEP processes the event) is delayed compared to the event time (the time on which the event really occurs).

Note that for CEP, there is clearly a trade-off between timeliness and accuracy, because the slack time will increase the delay to deliver your result but will increase your accuracy. There is always a tradeoff between cost, latency and correctness, and usually, you can only pick two among the three.

Fun fact: If you need to explain what is event time & processing time to your mother (yeah, don’t underestimate the power of this kind of discussion at Christmas dinner), the best way is to take the Star Wars analogy. From an event time perspective (which is the time at which the story really happened) you should follow episode 1, 2, 3,4, 5, 6, 7,8, 9. But if you take the processing time (the time on which we received the episode), it is 4, 5, 6, 1, 2, 3, 7, 8, 9.  Isn’t it great ?!

 

Final Thoughts

CER has been explored from many viewpoints. However, never in the research history was there a meeting gathering representatives of these communities. This was the objective of this seminar. Having all these people in a castle in the middle of nowhere was a blast! I had very passionate discussions during meals but also during the night at the library with the most brilliant brains on stream and CEP. On the other hand, I still had some fun discussions about comparing Star Trek DIscovery and Picard! Finally, the most important things I will remember after this seminar… are the endless ping pong games with Till Rohrmann and Alessandro Margara :-).

Throwback To 2019

At EURA NOVA, we believe technology is a catalyst for change. To embrace it, we strive to stay at the edge of knowledge. Investing in research allows us to continuously become more proficient, to maintain our know-how at the cutting edge of IT, to share its benefits with our customers, and to incubate the products of tomorrow. As we look back on the year 2019, we are both proud and happy of the work achieved!

 

Published papers:

We are happy to say that our R&D department has published five peer-reviewed scientific papers last year.

 

  • LEAD: A Formal Specification For Event Processing

 

In June, our R&D engineer Anas presented his work on complex event processing at the 13Th ACM international Conference on distributed and event-based systems, which was taking place in Germany.

Anas Al Bassit, Skhiri Sabri, LEAD: A Formal Specification For Event Processing, in 13Th ACM international Conference on distributed and event-based systems 2019

 

  • Coherence Regularization for Neural Topic Models

 

In July, our R&D engineer Kate presented her paper on neural topic models at the 16th International Symposium on Neural Networks taking place in Moscow.

Katsiaryna Krasnashchok, Aymen Cherif, Coherence Regularization for Neural Topic Models. in 16th International Symposium on Neural Networks 2019 (ISNN 2019)

 

  • STRASS: A Light and Effective Method for Extractive Summarization

 

In August, our PhD student Léo was in Italy to present his paper at the 2019 ACL Student Research Workshop.

Léo Bouscarrat, Antoine Bonnefoy, Thomas Peel, Cécile Pereira, STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings, in 2019 ACL Student Research Workshop, Florence, Italy.

 

  • GraphOpt: Framework for Automatic Parameters Tuning of Graph Processing Frameworks

 

In December, the paper written by our former intern and now full-time colleague Muaz was presented in Los Angeles at the third IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications.

Muaz Twaty, Amine Ghrab, Skhiri Sabri: GraphOpt: a Framework for Automatic Parameters Tuning of Graph Processing Frameworks. 2019 IEEE International Conference on Big Data (Big Data) Workshops, Los Angeles, CA, USA.

 

  • A Performance Prediction Model for Spark Applications

 

In June 2020, our paper written as part of the ECCO research project we have been leading at EURA NOVA will be presented at the Big Data congress 2020 taking place in Hawaii.

Florian Demesmaeker, Amine Ghrab, Usama Javaid, Ahmed Amir Kanoun, A Performance Prediction Model for Spark Applications, in the proceedings of Big Data congress 2020.

 

IEEE Big Data Workshop

Last December, Eura Nova’s research centre held the fourth workshop on real-time and stream analytics in big data at the 2019 IEEE Conference on Big Data in Los Angeles. The workshop brought together leading players including Confluent, Apache Pulsar, the University of Virginia and Télécom Paris Tech as well as 8 renowned speakers from 6 different countries. We received more than 30 applications and we are proud to have hosted such interesting presentations of papers in stream mining, IoT, and industry 4.0. Special thanks to our keynote guests, Matteo Merli (Apache Pulsar) and John Roesler (Confluent), and all the attendees and speakers!

 

JERICHO, research driving innovations

The mission of the JERICHO research track is to make the latest technologies available to our client, to offer them a competitive edge to play along megacorporations.  After two years of intense work, seven published papers, presentations in international conferences spanning Russia, the United States, Germany, Australia, or Belgium, our Jericho project has come to an end.

And the adventure continues! We are really excited to continue our work on innovative solutions for the next data challenges with our new research track ASGARD.

Our R&D director Sabri Skhiri says: “The costs of data solutions and the lack of data scientists will increase in the next 3 to 5 years and solutions to reduce them will benefit from a large market. In this sense, ASGARD is precisely in the strategy of Eura Nova. ASGARD aims to reduce these costs by automating the most expensive tasks. As the world becomes increasingly digital and reinvents itself, innovation and research are essential in the market.”

 

Academic collaboration

This year, we welcomed nine interns across our three offices. A big kudo to our intern Muaz who successfully finished his master thesis in collaboration with EURA NOVA! The goal of his thesis was to optimise the configuration of distributed graph frameworks. He now joined EURA NOVA to work as a full-time employee.

 

Talks & seminars

This year, the research team had the pleasure to be invited at several international conferences:

  • In February, our research director Sabri Skhiri gave a seminar on modern Stateful Stream Processing at EPT. Our R&D engineer Syrine Ferjaoui also went to Morocco to give a workshop about data architecture at the Annual International Conference on Arab Women In Computing.
  • In March, Sabri was at the World AI Show in Dubaï to talk about successfully deploying AI projects in production. He was also invited to Barcelona Tech to give a Big Data Architecture & Design  seminar.
  • In June, our data privacy officer Nazanin Gifani gave a masterclass on Fairness and Transparency in AI at the DI Summit in Brussels.
  • In September, our R&D project manager Shivom Aggarwal talked at the Arab Future Cities Summit 2019 about deploying AI at industrial scale for smart cities.
  • In October, our software engineer Christophe Philemotte was in San Francisco to talk at the Kafka Summit about crossing the streams thanks to Kafka and Flink.
  • In November, Sabri was invited as a keynote speaker at the 17th International Conference on Service-Oriented Computing to share his experience about the convergence between micro-service, stateful stream processing and function as a service.

 

Summer schools & conferences

This year, Euranovians attended more than 15 prestigious international conferences and summits across the world to remain up to date and grow our network. We investigated the state of the art in streaming, data science, DevOps, computer vision or cloud engineering at conferences such as Flink Forward, Spark AI Summit, Kubecon, IEEE Big Data, DataWorks Summit, Kafka Summit, NeurIPS, RedHat, Elixir LDN or CVPR.

Euranovians brought back what they learned for the rest of the team and the big data community. Find our public summaries, identified trends and review of conferences here:

 

Fourth Workshop on Real-Time and Stream Analytics in Big Data: key takeaways

Last December, Eura Nova’s research center held the fourth workshop on real-time and stream analytics in big data at the 2019 IEEE Conference on Big Data in Los Angeles. The workshop brought together leading players including Confluent, Apache Pulsar, the University of Virginia and Télécom Paris Tech as well as 8 renowned speakers from 6 different countries. We received more than 30 applications and we are proud to have hosted such interesting presentations of papers in stream mining, IoT, and industry 4.0.

The workshop was a real success with many interesting questions and comments. If you could not attend, our R&D engineer Syrine Ferjaoui brought back important elements from the presentations for you.

 

First keynote speaker:

First of all, the workshop started with the keynote of Matteo Merli, PMC member at Apache Pulsar. His talk “Messaging and Streaming” explained how Pulsar can be a unified infrastructure that supports messaging and streaming.

Matteo introduced messaging as events that are being created and streaming as analysing events that just happened. These are two different processing concepts but they need a single infrastructure. He then explained the architecture view of Pulsar, which has separate layers between the brokers and the bookies (BookKeeper instances that handle persistent storage of messages). This means that brokers and bookies can be added independently, traffic can be shifted very quickly across brokers, and new bookies will ramp up on traffic quickly. This segmented distribution makes the architecture of Pulsar more flexible and dynamic.

Pulsar has other interesting features such as durability, low latency, high throughput, high availability, unified messaging model, high scalability, native computing, … The roadmap includes working on Pulsar storage API to allow direct access to data stored in Pulsar and to retrieve and process data more efficiently. They are also working on higher-level messaging features.”

 

Second keynote speaker:

The second keynote was given by John Roesler, a Kafka committer at Confluent. He talked about Kafka Streams and the evolution of streaming paradigms.

To design software, we, developers, used to separate the application logic from the database. To scale the database capacity, we then started to use a search index to do ETL jobs and query the database in a fast and optimal way. However, this created bugs in the software, added data consistency issues, and created more complexity in the system. Later, we started to use HDFS for a more flexible design. While enabling replication and distributed storage, this solution added more latency and supported batch processing only. It did not meet the needs of real-time processing use cases.

At this point, streaming helped a lot. The next step was to add a streaming platform that reads from sources, does some computation, and sinks the result somewhere else. The KafkaStreams design is a set of multiple lambda stateful functions, which makes it a good fit for a microservices architecture.  With Kafka Streams’ new updates, the app logic is linked to a relational database with ACID guarantees.

Finally, John Roesler considers that “software is a fractal”, a never-ending pattern: a software architecture is complex and even when we zoom into a single component, it is still complex. But for the Kafka Streams’ design, when we zoom out, it looks like a set of services interacting and connected to each other and this simplifies the aforementioned designs.

John concluded by mentioning open problems that can be dealt with in stream processing, including semantics, observability, operability, and maintainability.

 

Workshop Invited Speakers:

After the keynotes, 8 selected papers were presented, covering mainly these 6 topics: (1) Stream Processing for IoT, (2) Serverless and HPC (High Performance Computing), (3) Collaborative Streaming, (4) Stream Mining, (5) Image Mining and (6) Real-time Machine Learning. Some papers are not yet available, as they will be published in the proceeding of the IEEE Big Data Conference. In the meantime, do not hesitate to contact our R&D department at research@euranova.eu to discuss how you can leverage stream processing in your projects.

Sören and Wilhelm are engineers in the Software Engineering Group from Kiel University. They propose a stream processing architecture which allows for aggregating sensors in hierarchical groups, supports multiple hierarchies in parallel, provides reconfiguration at runtime, and preserves the scalability and reliability qualities of streaming.

Andre Luckow, head of Blockchain and Emerging Technologies at BMW Group, and Shantenu Jha, associate professor at Rutgers University, presented StreamInsight, which provides insight into the performance of streaming applications and infrastructure, their selection, configuration, and scaling behaviour.

The paper is written by Tobias Grubenmann, researcher at The University of Hong Kong, in collaboration with Daniele Dell’Aglio and Abraham Bernstein, researchers at the University of Zurich. They present the Collaborative Stream Processing (CSP), a model where the costs, which are set exogenously by providers, are shared between multiple consumers, the collaborators. For this, they identify the important requirements for CSP to establish trust between the collaborators and propose a CSP algorithm adhering to these requirements.

  • Kennard-Stone Balance Algorithm for Time-series Big Data Stream Mining (Tengyue Li, Simon Fong, and Raymond Wong)

Tengyue Li and Simon Fong (researcher and associate professor at the University of Macau, China) and Raymond Wong (associate professor at UNSW Sydney) worked on the Kennard-Stone Balance algorithm used as a new data conversion method. Training a prediction model effectively using big data streams poses certain challenges in machine learning. In this paper, the authors apply the Kennard-Stone algorithm on time-series to extract a meaningful representation of big data streams, which improves the performance of a machine learning model.

 

  • Assessing the Effects of TV Ad Events on Digital Search: On the Selection of Outcome Measures (Shawndra Hill, Anthony Colas, H. Andrew Schwartz, and Gordon Burtch)

Shawndra Hill (Microsoft), Anthony Colas (University of Florida), H. Andrew Schwartz (State University of New York at Stony Brook) and Gordon Burtch (University of Minnesota) explained their work on the interactions between TV content and online behaviours such as response to digital advertising. They developed AdMiner, a tool that can track online activity around a brand and provide actionable insights into ad campaigns.

 

Austin Harris, Jose Stovall, and Mina Sartipi (researchers and CUIP director at the University of Tennessee at Chattanooga) have helped to create Chattanooga’s smart corridor, used to test new technologies and generate data-driven outcomes. In their talk, they presented the corridor, used as a test bed for research in smart city developments in a real-world environment. The wireless communication infrastructure and network of sensors in combination with data analytics provide a means of monitoring and controlling city resources and infrastructure in real time.

 

Sebastian Trinks and Carsten Felde (TU Bergakademie Freiberg) presented how image mining can help avoiding errors and low quality of printed prototypes in real time. This can result in saving resources and increasing efficiency when developing new products.

 

This year, IEEE Big Data held the Real-time Machine Learning Competition on Data Streams. As the competition is focused on streaming, its online platform required a specific infrastructure that meets data stream mining requirements. Dihia Boulegane is a Ph.D. student at Télécom ParisTech working in collaboration with Orange Labs on machine learning for IoT networks monitoring. She was in charge of implementing the streaming engine of the dedicated platform of the competition. Dihia explained its components, the technologies used, and the challenges met to build the platform. At the end, the platform was able to provide multiple streams to multiple users, to receive multiple streams, to process them and to provide the leader board and live results.

 

Special thanks to our keynote guests, Matteo Merli and John Roesler, and all the attendees and speakers! We are looking forward to an even more successful workshop in the coming edition of the IEEE Big Data Conference. Stay tuned for paper submission dates!

 

A Performance Prediction Model for Spark Applications

Apache Spark is a popular open-source distributed-processing framework that enables efficient processing of massive amounts of data. It has a large number of parameters that need to be tuned to get the best performance. However, tuning these parameters manually is a complex and time-consuming task. Therefore, a robust performance model to predict applications execution time could greatly help in accelerating the deployment and optimization of big data applications relying on Spark. In this paper, we ran extensive experiments on a selected set of Spark applications that cover the most common workloads to generate a representative dataset of execution time. In addition, we extracted application and data features to build a machine learning-based performance model to predict Spark applications execution time. The experiments show that boosting algorithms achieved better results compared to other algorithms.

Florian Demesmaeker, Amine Ghrab, Usama Javaid, Ahmed Amir Kanoun, A Performance Prediction Model for Spark Applications, in the proceedings of Big Data congress 2020.

Click here to access the paper in its preprint form.

IEEE Big Data 2019 – A Summary

At the beginning of the month, our R&D director Sabri Skhiri and our R&D engineer Syrine Ferjaoui travelled to Los Angeles to attend IEEE Big Data Conference. It is one of the most influential academic gatherings in distributed machine learning. This year, it featured 879 authors, shortlisted from 2009 applicants. They came from 28 countries and presented 210 papers. Back in Belgium, Sabri and Syrine give you their opinion on the event itself and the important elements from the keynotes, the tutorials, the workshops and the interesting papers.

 

The Big Trends

Sabri says: “The main trends were deep learning, NLP, privacy-preserving approaches, GAN, graph mining and stream mining. In my view, the level of the papers was quite good. Authors are becoming ever more skilled in data science, maths and algorithms. This goes to show that to be a good data scientist, you need an extensive set of advanced skills. Interestingly, there was almost nothing about distributed computing! This is a big move compared to the previous editions. The only presentations that had something to do with distributed systems were about optimisation strategies, an area similar to what our ECCO team researches. The Big Data Conference focuses on data science; it does not really look into its scalability.  Distributed computing topics tend to be dealt with at conferences like DEBS, VLDB, USENIX, SIGMOD, etc. As a result, this conference is an amazing place to see hundreds of data science use cases with, most of the time, an interesting contribution.”

 

The Keynotes

 

The keynotes were focused on data science as well. We even heard the term “Big Data Science”.

Keynote 1: Responsible Data Science by Lise Getoor – Professor at UC Santa Cruz

Syrine says: “The first keynote was my favourite. Lise started by comparing machine learning to a black box. The goal was to unpack the box and invite people to use data science and to use it wisely. To autonomise ethical decision-making, we should move away from maximising AI systems autonomy and move toward human-centric systems. To do this, we should make sure that human-centric systems have three qualities: (1) be knowledge-based, (2) be data-driven, and (3) support human values. Achieving responsible data science requires both machine-learning and ethics.”

 

Keynote 2: DataCommons “Google for Data” by Ramanathan Guha – Google

Guha presented DataCommons, a project started by Google to combine data from different open sources. Syrine explains: “Google’s DataCommons project allows users to pretend that the Web is one website, enabling developers to pretend all this data is in one database. The long-term vision of Google is to aggregate all data from publicly available sources (Medicare, Wikidata, sequence data, Landsat, CDC, Census…) into a single Open Knowledge Graph. The goal is to ​reduce or eliminate the ​​data download-clean-store​ process. Instead, users can access and use already cleaned data in the cloud. ​Data can be public or private (internet & intranet). This will avoid repeated data wrangling  and ease the burden of data storage, indexing, etc.”

 

The Tutorials

This year, IEEE Big Data held nine tutorials. Our R&D director explains: “At this type of events, tutorials are always a good way to learn a complete state of the art in a couple of hours. I particularly appreciated the tutorial on “Taming Unstructured Big data: Automated Information Extraction for Massive Textby the team of the famous Jiawei Han (he is a kind of pop star in data mining and the father of Graph Cube). I found out that many papers about named entity relations were published in the past two years. The idea is to be able to extract supervised, semi-supervised, and unsupervised relations between entities: for instance, discovering that “Trump” is “President of” “USA”. They also propose new approaches to integrate knowledge bases such as DBPedia or YAGO to infer new unknown relations from a corpus. This is just amazing!”

 

Syrine adds: “The tutorial on NewSQL principles, systems, and current trends was interesting as it explained why we should consider using NoSQL/NewSQL to deal with data interconnections and very high scalability. After attending this tutorial, I was motivated to order this book about Principles of Distributed Database Systems. For fans of deep learning, the tutorial “Deep Learning on Big Data with Multi-Node GPU Jobs” covers a lot about large-scale GPU-based deep-learning systems. If you missed the conference, all resources can be found on this ​link​.”

 

The Workshops

The EURA NOVA research centre organised the fourth workshop on Real-time and Stream Analytics in Big Data, at the 2019 IEEE conference on Big Data. We were really happy to welcome Matteo Merli from Apache Pulsar and John Roesler from Confluent as keynotes speakers. Thank you to them and to all the attendees and speakers! They had a great time, with captivating talks and a lot of interesting questions and comments. The summary of the event will soon be available on our website. The slides of the keynotes are available here:

 

 

Favourite Papers

A personal selection of interesting papers:

The paper tackles a really interesting problematic faced by a lot of data scientists. Introducing active learning is a cool idea and so is the way they used a mathematical trick to make their approach feasible.

Su Won Bae, from Mobilewalla, presented how they can define a complete customer acquisition model by mixing their data with their customer data (in this case, a worldwide leader in food delivery). Sabri says: “The quality of data science models highly depends on the data they can train on. I am convinced we will go in the same direction as Mobilewalla in the future to have richer models. However, mixing data must be done with care as it may raise some privacy issues;  our purpose has to have legal ground.”

The speaker presented MorphMine, a method for unsupervised morpheme segmentation.  It can generate morpheme candidates that are filtered out using entropy to select the best morphemes from a corpus. Then, these morphemes can be used to highly improve the word embedding model and the downstream machine learning tasks.

 

 

GraphOpt: Framework for Automatic Parameters Tuning of Graph Processing Frameworks

Finding the optimal configuration of a black-box system is a difficult problem that requires a lot of time and human labor. Big data processing frameworks are among the increasingly popular systems whose tuning is a complex and time consuming. The challenge of automatically finding the optimal parameters of big data frameworks attracted a lot of research in recent years. Some of the studies focused on optimizing specific frameworks such as distributed stream processing, or finding the best cloud configurations, while others proposed general services for optimizing any black-box system. In this paper, we introduce a new use case in the domain of automatic parameter tuning: optimizing the parameters of distributed graph processing frameworks. This task is notably difficult given the particular challenges of distributed graph processing that include the graph partitioning and the iterative nature of graph algorithms.

To address this challenge, we designed and implemented GraphOpt: an efficient and scalable black-box optimization framework that automatically tunes distributed graph processing frameworks. GraphOpt implements state-of-the-art optimization algorithms and introduces a new hill-climbing-based search algorithm. These algorithms are used to optimize the performance of two major graph processing frameworks: Giraph and GraphX. Extensive experiments were run on GraphOpt using multiple graph benchmarks to evaluate its performance and show that it provides up to 47.8% improvement compared to random search and an average improvement of up to 5.7%.

Muaz Twaty, Amine Ghrab, Skhiri Sabri: GraphOpt: a Framework for Automatic Parameters Tuning of Graph Processing Frameworks. 2019 IEEE International Conference on Big Data (Big Data) Workshops, Los Angeles, CA, USA.

The paper was published at the third IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2019).

You can access it here in its preprint version.

Do not hesitate to contact our R&D department at research@euranova.eu to discuss how you can leverage graph processing in your projects.

Master Thesis 2020

This document introduces you to master thesis supervised by our research & development department. Each project offers you the chance to be actively involved in the development of solutions to address tomorrow’s challenges in ICT and implementing them today!

 

If you are interested in one of our offers, please send us your application to career@euranova.eu, including your CV and motivation regarding your top three master thesis subject (described in the document).

If you are interested in working on a topic that is not in our range of offers, we would be delighted to hear your proposition and invite you get in touch.

Master thesis subjects and application guidelines are available here: Master Thesis Offers.