Stream processing and reactive programming

October 31, 2014 — July 1, 2015

computers are awful
concurrency hell
premature optimization
signal processing
stringology
Figure 1

Lazy bookmark for practical details to processing and transforming possibly-infinite streams of data, from signals to parse trees. Disambiguating “transducers”.

Used in parallel/offline processing of large data sets that do not fit in core, or processing things that happen in realtime such as UI.

I am imagining more general objects than singly-indexed real-valued signals; Tokens, maybe. Classic DSP can be elsewhere. Infrastructure to do stream processing in a distributed fashion is filed under message queues.

In statistics and machine learning, stream processing connects with online learning; incorporating data as it comes in, as in distributed statistics.

1 Functional reactive programming

See FRP.

2 Streaming data analysis

Online, possibly realtime, certainly memory-constrained.

3 To read

4 References

Hu, Pehlevan, and Chklovskii. 2014. “A Hebbian/Anti-Hebbian Network for Online Sparse Dictionary Learning Derived from Symmetric Matrix Factorization.” In 2014 48th Asilomar Conference on Signals, Systems and Computers.
McSherry, Isaacs, Isard, et al. 2013. Differential dataflow. US20130304744 A1.
Murray, McSherry, Isaacs, et al. 2013. “Naiad: A Timely Dataflow System.” In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. SOSP ’13.
Pan, Zhang, Wu, et al. 2014. “Online Community Detection for Large Complex Networks.” PLoS ONE.
Ryabko, and Ryabko. 2010. “Nonparametric Statistical Inference for Ergodic Processes.” IEEE Transactions on Information Theory.
Sorensen, and Gardner. 2010. “Programming with Time: Cyber-Physical Programming with Impromptu.” In ACM Sigplan Notices.