PROTEUSScalable Online Machine Learning for Predictive Analytics and Real-Time Interactive VisualizationBONAVENTURA DEL MONTERESEARCHER @DFKI GMBHPH.D. STUDENT @TU BERLINEUROPRO WORKSHOP, EDBT 2017This project is funded

by the European Union. Horizon 2020

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Value

Velocity

VarietyVeracity

Volume BIG DATA

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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

CONTENTS

1. PROJECT DETAILS2. VALIDATION SCENARIO3. HYBRID PROCESSING ENGINE4. SCALABLE ONLINE MACHINE LEARNING5. REAL-TIME INTERACTIVE VISUAL ANALYTICS6. CONCLUSION

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Project Consortium

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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

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Hot Strip Mill: Big Data scenario

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System Architecture

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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

Hybrid Processing

Gather and clean sensor

dataPCA

Train ML

Model

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D1

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Bridging the Gap: Towards Optimizations across Linear and Relational Algebra": Andreas Kunft, Alexander Alexandrov, Asterios Katsifodimos, Volker Markl. BeyondMR workshop @SIGMOD 2016.

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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

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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

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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.

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Thanks for your attention!Questions?

Contact us: Bonaventura Del Monte

bonaventura dot delmonte at dfki dot de www.dfki.berlin

www.proteus-bigdata.comwww.github.com/proteus-h2020

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Extra Slides

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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

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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