PROTEUS H2020
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PROTEUS is an EU-funded research project that aims to develop an open-source big data solution for real-time predictive analytics and interactive visualization. It will provide a hybrid processing engine for both batch and streaming data, a library called SOLMA for scalable online machine learning, and tools for real-time interactive visual analytics. The system will be validated using a use case from ArcelorMittal on analyzing sensor data from a hot strip mill.
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PROTEUS H2020
- 1. PROTEUS Scalable Online Machine Learning for Predictive Analytics and Real-Time Interactive Visualization BONAVENTURA DEL MONTE RESEARCHER @DFKI GMBH PH.D. STUDENT @TU BERLIN EUROPRO WORKSHOP, EDBT 2017This project is funded by the European Union. Horizon 2020
- 3. 3
- 4. 4 PROTEUS is a EU H2020 funded research project which aims to design, develop, and provide an open-source ready-to-use Big Data solution, able to perform real-time interactive analytics and predictive analysis through massive online machine learning, efficiently dealing with extremely large historical data and data stream
- 5. CONTENTS 1. PROJECT DETAILS 2. VALIDATION SCENARIO 3. HYBRID PROCESSING ENGINE 4. SCALABLE ONLINE MACHINE LEARNING 5. REAL-TIME INTERACTIVE VISUAL ANALYTICS 6. CONCLUSION
- 7. 7 Project details Expected Outcomes Hybrid processing Batch & Stream processing engine Declarative Language for batch & streams analytics Scalable Online machine Learning SOLMA Library Real-time interactive Visual Analytics Web charts library Incremental engine for interactive analytics Business Impact Validation in realistic industrial use case
- 8. 8 Hot Strip Mill: Big Data scenario
- 10. Smoother processing of data stream and historical data in the same Flink job A declarative language for batch and streaming analytics ETL and ML pipelines expressed in an unified language are holistically optimized 10 Hybrid Processing Gather and clean sensor data PCA Train ML Model D3 D1 D2 Bridging the Gap: Towards Optimizations across Linear and Relational Algebra": Andreas Kunft, Alexander Alexandrov, Asterios Katsifodimos, Volker Markl. BeyondMR workshop @SIGMOD 2016.
- 11. 11 Scalable Online Machine Learning ML challenge: Distributed Data Streams Current state of the art of machine learning algorithms for Big Data is dominated by offline learning algorithms that process data-at-rest Plenty of current data sources are streaming (online, data-in-motion): sensors, social networks, clickstream, etc. In online learning, the algorithms see the data only once. The traditional meaning of online is that data is processed sequentially one by one but for many epochs: prequential evaluation
- 12. 12 Real-time Interactive Visual Analytics How to interactively visualize Big Data? Incremental Analytics engine: incremental partial results in ~ O(1) Visualization Layer: SSR-enabled web-based library seamlessly connected to the Incremental Analytics engine https://github.com/proteus-h2020/proteic
- 13. 13 Conclusions PROTEUS is an EU H2020 international research project PROTEUS will contribute to the Big Data ecosystem with: An innovative hybrid engine for processing both data-at-rest and data-in-motion SOLMA: An new library for scalable online machine learning Big Data Visualization guidelines: new ways of presenting and working with Big Data Real-time interactive visualization technology: Incremental engine & web-based library PROTEUS will validate its innovations in a realistic industrial scenario PROTEUS will provide full-scale evaluation and impact assessment including benchmarks, KPIs and anonymized datasets Specific metrics for the ArcelorMittal use case Generic indicators on the advancements in scalable machine learning, hybrid computation and real-time interactive visual analytics.
- 14. 14 Thanks for your attention! Questions? Contact us: Bonaventura Del Monte bonaventura dot delmonte at dfki dot de www.dfki.berlin www.proteus-bigdata.com www.github.com/proteus-h2020
- 15. 15 Extra Slides
- 16. 16 Apache Flink 101 Massive parallel data flow engine with unified batch and stream processing Rich set of operators (including native iteration) Flink Optimizer Inspired by optimizers of parallel database systems Physical optimization follows cost‐based approach Memory Management Flink manages its own memory Never breaks the JVM heap
- 17. 17 Scalable Online Machine Learning PROTEUS contribution: SOLMA User-friendly Extensibility Basic scalable stream sketches that enable to query the stream Iterative algorithms for approximating the outcome of offline computation Ready-to-use (supervised & unsupervised) online ML algorithms in Apache Flink
Editor's Notes
- As you probably got to know in the last couple of years, big data are not just a huge quantity of heterogeneous data whose analysis is rather complex and that are ingested at high rate in your data processing system. Indeed, at the end of the day, what really matters is how much you can capitalize exploiting big data.
- However, if you are going to start a new big data related, you will be facing a zoo of technologies and the final choice which strictly relies on the use case is biased by the knowledge of the IT guys leading the project.
- However, if you need to deal with large historical data as well as data streams and to perform predictive analysis and real-time interactive analytics then you may consider Proteus as it is an open source ready to use big data solution offering such capabilities. AND I WILL SHOW THAT IN THE NEXT SLIDES.
- The presentation goes as follows
- Research partners, pure IT companies and ArcelorMittal
- Our validation scenario deals with the prediction of anomalies in the coils produced through the so-called Hot strip mill process, which comes from our ArcelorMittal partner, a leader in the steelmaking industry. In order to do such task, we need to perform analytics on streaming data and historical data.
- 3 main subsystems: an hybrid processing engine for large historical data and data streams powered by an enhanced version of Apache Flink (distributed dataflow system for batch and stream data in a single engine); a library for scalable online machine learning built on top of our processing engine and then the visualization stack which queries the solma library and the engine in real-time.
- The ML challenge we are facing deals with data stream, we need online machine learning which suits better streaming processing rather than traditional batch ML. Online machine learning algorithms see data item one by one, generally speaking it firsts predicts the class of the item and then it does a single training step on the model. This is called prequential evaluation.
- Our real-time interactive visual analytics stack tries to answer the question: How to interactively visualize big data? The answer is through incremental partial results that update the charts and a SSR-enabled webchart library