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Stream reasoning

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This is the presentation that I did for PoliMI Data Scientists on Stream Reasoning, an approach to blend Artificial Intelligence and Stream Processing.

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Stream reasoning

  1. 1. STREAM REASONING AN APPROACH TO BLEND 
 AI AND STREAM PROCESSING Emanuele Della Valle
 Politecnico di Milano
 http://emanueledellavalle.org
 @manudellavalle Milano - 17.10.2019
  2. 2. DATA ENGINEERING AND DATA SCIENCE TECHS CAN TAME VOLUME Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  3. 3. DATA ENGINEERING AND DATA SCIENCE TECHS CAN TAME VELOCITY Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  4. 4. DATA ENGINEERING AND DATA SCIENCE TECHS CANNOT TAME VOLUME AND VELOCITY SIMULTANEOUSLY ZB EB PB TB GB MB KB months days hours min. sec. ms. Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  5. 5. DATA ENGINEERING AND DATA SCIENCE TECHS CAN TAME VARIETY USING AI Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  6. 6. DATA ENGINEERING AND DATA SCIENCE TECHS VARIETY MAKES PROBLEMS HARDER ZB EB PB TB GB MB KB months days hours min. sec. ms. VARIETY STILL THERE ARE USERS WHOSE DECISIONS 
 NEED TO TAME ALL Vs Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  7. 7. STILL THERE ARE USERS WHOSE DECISIONS NEED TO TAME ALL Vs OFF-SHORE OIL OPERATIONS ‣ When sensors on a drilling pipe in an oil-rig indicate that it is about to get stuck, how long — according to historical records — can I keep drilling? ‣ 400,000 sensors from 10s of differente producers ‣ 10,000 observations per second, many out-of-operational-ranges
  8. 8. STILL THERE ARE USERS WHOSE DECISIONS NEED TO TAME ALL Vs REQUIREMENT ANALYSIS A system able to answer those queries must be able to ▸ handle massive datasets x ▸ process data streams on the fly x ▸ cope with heterogeneous datasets x ▸ cope with incomplete data x x ▸ cope with noisy data x ▸ provide reactive answers x Volume Velocity Variety Veracity Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  9. 9. STILL THERE ARE USERS WHOSE DECISIONS NEED TO TAME ALL Vs (PARTIAL) SOLUTIONS: STREAM PROCESSING ▸ A paradigmatic change! window input streams streams of answerRegistered Continuous Query Dynamic System Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  10. 10. STILL THERE ARE USERS WHOSE DECISIONS NEED TO TAME ALL Vs STREAM PROCESSING VS. REQUIREMENTS Requirement SP massive datasets data streams heterogeneous dataset incomplete data noisy data reactive answers Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  11. 11. STILL THERE ARE USERS WHOSE DECISIONS NEED TO TAME ALL Vs AI VS. REQUIREMENTS Requirement SP AI massive datasets data streams heterogeneous dataset incomplete data noisy data reactive answers Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  12. 12. Is it possible to make sense in real time 
 of multiple, heterogeneous, gigantic and 
 inevitably noisy and incomplete data streams 
 in order to support the decision processes of extremely large numbers of concurrent users? E. Della Valle, S. Ceri, F. van Harmelen & H. Stuckenschmidt, 2010 STILL THERE ARE USERS WHOSE DECISIONS NEED TO TAME ALL Vs STREAM REASONING RESEARCH QUESTION Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  13. 13. ( , 13), ( , 12), ( , 8) , ( , 8) DEDUCTIVE STREAM REASONING STREAM PROCESSING time 1 minute wide window Which are the top-4 most frequent colours in the last minute? Is there a 
 followed by a 
 in the last minute yes, many Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  14. 14. DEDUCTIVE STREAM REASONING STREAM PROCESSING + SYMBOLIC AI time 1 minute wide window An ontology of colours Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  15. 15. ( , 13),( , 8) , ( , 8) DEDUCTIVE STREAM REASONING DEDUCTIVE STREAM REASONING time 1 minute wide window Which are the top-2 most frequent cool colours in the last minute? Is there a primary cool colour followed by a secondary warm one
 yes, followed by . An ontology of colours Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  16. 16. INDUCTIVE STREAM REASONING THE CONCEPT DRIFT PROBLEM Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  17. 17. INDUCTIVE STREAM REASONING AN EARLY ATTEMPT OF INDUCTIVE STREAM REASONING D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, Y. Huang, V. Tresp,
 A. Rettinger, H. Wermser: Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics. 
 IEEE Intelligent Systems 25(6): 32-41 (2010) • How can we determining the optimal size of the window? Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  18. 18. INDUCTIVE STREAM REASONING ADAPTIVE SLIDING WINDOW (ADWIN) Bifet, A. and Gavaldà, R., 2009, August. Adaptive learning from evolving data streams. In International Symposium on Intelligent Data Analysis (pp. 249-260). Springer Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  19. 19. INDUCTIVE STREAM REASONING CONCEPT DRIFT AND STREAMING MACHINE LEARNING • Hoeffding Adaptive Tree • Adaptive Random Forest • Temporally Augmented Classifier A. Bifet, R. Gavaldà, G. Holmes, B. Pfahringer: Machine Learning for Data Streams: with Practical Examples in MOA. The MIT Press (March 2, 2018) Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle https://moa.cms.waikato.ac.nz/
  20. 20. STREAM REASONING DEDUCTIVE + INDUCTIVE STREAM REASONING time 1 minute wide window A better ontology 
 of colours continuously 
 learned from the data Which are the most frequent sentiments in the last minute? Is there a impulsive, irritating colour followed by an happy one
 The better is the ontology of the colours we are using the more expressive are the queries we can register Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  21. 21. STREAM REASONING 1000+ SCIENTIFIC PAPERS IN 12 YEAR ▸ It is possible extend the Semantic Web stack in order 
 to represent heterogeneous data streams, continuous queries, and continuous reasoning tasks ▸ It is possible to optimise continuous querying and continuous reasoning so to provide reactive answers ▸ Streaming Machine Learning is starting to show that it is possible to continuously learn models Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  22. 22. STREAM REASONING STREAM REASONING VS. REQUIREMENTS Requirement Stream Reasoning massive datasets data streams heterogeneous dataset incomplete data noisy data reactive answers not specifically treated so far treated but not resolved universally addressed by all studies Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  23. 23. STREAM REASONING MASTER THESIS? ▸ Semantics of stream processing languages ▸ Scaling deductive Stream Reasoning ▸ Stream Reasoning on Kafka ▸ Stream Reasoning on Spark ▸ Advancing Inductive Stream Reasoning ▸ Streaming Graph Machine Learning ▸ Streaming Anomaly Detection ▸ Build the first working inductive and 
 deductive Stream Reasoners ▸ Applications Emanuele Della Valle - http://emanueledellavalle.org - @manudellavalle
  24. 24. STREAM REASONING EVENTUALLY :-P
  25. 25. STREAM REASONING THANK YOU! ANY QUESTION? Emanuele Della Valle
 Politecnico di Milano
 http://emanueledellavalle.org
 @manudellavalle Milano - 17.10.2019

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