Paper: Scalable Detection of Concept Drifts on Data Streams with Parallel Adaptive Windowing Abstract: Machine learning techniques for data stream analysis suffer from concept drifts such as changed user preferences, varying weather conditions, or economic changes. These concept drifts cause wrong predictions and lead to incorrect business decisions. Concept drift detection methods such as adaptive windowing (Adwin) allow for adapting to concept drifts on the fly. In this paper, we examine Adwin in detail and point out its throughput bottlenecks. We then introduce several parallelization alternatives to address these bottlenecks. Our optimizations lead to a speedup of two orders of magnitude over the original Adwin implementation. Thus, we explore parallel adaptive windowing to provide scalable concept detection for high-velocity data streams with millions of tuples per second.

1. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
1
Scalable Detection of Concept Drifts
on Data Streams with
Parallel Adaptive Windowing
Presentation at 18.04.2018
Philipp M. Grulich, René Saitenmacher, Jonas Traub, Sebastian Breß, Tilmann Rabl, Volker Markl
EDBT, Vienna, 2018

2. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
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Background:
Adaptive Windowing (ADWIN) [Bifet ’07]
• Maintain an adaptive window which is the basis for computing a ML model.
• Grow the window as long as there is no concept drift detected.
• As a result, the model can rely on growing training data.

3. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
3
Background:
Adaptive Windowing (ADWIN) [Bifet ’07]
• Maintain an adaptive window which is the basis for computing a ML model.
• Grow the window as long as there is no concept drift detected.
• As a result, the model can rely on growing training data.

4. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
4
Background:
Adaptive Windowing (ADWIN) [Bifet ’07]

5. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
5
Background:
Adaptive Windowing (ADWIN) [Bifet ’07]

6. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
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Initial Performance Study:
Where do we Spend Execution Time?

7. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
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Initial Performance Study:
Where do we Spend Execution Time?
 Parallelize Cut Detection!

8. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
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Scope of Parallelization
Cut-Check
Decoupling
• Cut Checks do
not block
processing.
INTRA Cut-Check
Parallelization
• Parallelize the
execution of
one iteration.
INTER Cut-Check
Parallelization
• Run multiple
iterations
concurrently.

9. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
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INTRA Cut Check Parallelization
• Halve the latency / execution time of the cut detection.
• Introducing a second thread which moves opposite to the first
thread and performs half of the cut checks.
• Can be extended to more threads.

10. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
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INTRA Cut Check Parallelization
• Decouple cut detection from the insertion of stream tuples.
• Execute cut detection on a snapshot histogram.
• Use multi-version concurrency control (MVCC).
• roll back in case of concept drifts.

11. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
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Experiment Results
1) Optimistic Adwin is two
orders of magnitude faster
than the original Adwin
implementation and 54
times faster than an
optimized sequential
implementation.
2) Half-Cut Adwin is 10
times faster than the
original Adwin
implementation and
almost 2 times faster than
an optimized sequential
implementation.
3) Both have a low
microseconds latency.

12. P. M. Grulich, R. Saitenmacher, J. Traub, S. Breß, T. Rabl, V. Markl - Scalable Detection of
Concept Drifts on Data Streams with Parallel Adaptive Windowing , EDBT 2018
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Open Source Repositories
• Parallel Adwin Open Source Repository
– github.com/TU-Berlin-DIMA/parallel-ADWIN
• Original Adwin Repository
– github.com/abifet/adwin