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Programming by Composing Filters

13 pagesPublished: May 4, 2017

Abstract

We present a formal model for event-processing pipelines. Event-processing pipelines appear in a large number of domains, from control of cyber-physical systems (CPS), to large scale data analysis, to Internet-of-things applications. These applications are characterized by stateful transformations of event streams, for example, for the purposes of sensing, computation, and actuation of inner control loops in CPS applications, and for data cleaning, analysis, training, and querying in data analytics applications.
Our formal model provides two abstractions: streams of data, and stateful, probabilistic, filters, which transform input streams to output streams probabilistically. Programs are compositions of filters. The filters are scheduled and run by an explicit, asynchronous, scheduler.
We provide a transition system semantics for such programs based on infinite-state Markov decision processes. We characterize when a program is scheduler-independent, that is, provides the same observable behavior under every scheduler, based on local commutativity.

In: Thomas Eiter and David Sands (editors). LPAR-21. 21st International Conference on Logic for Programming, Artificial Intelligence and Reasoning, vol 46, pages 1--13

Links:
BibTeX entry
@inproceedings{LPAR-21:Programming_by_Composing_Filters,
  author    = {Jeffrey Fischer and Rupak Majumdar},
  title     = {Programming by Composing Filters},
  booktitle = {LPAR-21. 21st International Conference on Logic for Programming, Artificial Intelligence and Reasoning},
  editor    = {Thomas Eiter and David Sands},
  series    = {EPiC Series in Computing},
  volume    = {46},
  pages     = {1--13},
  year      = {2017},
  publisher = {EasyChair},
  bibsource = {EasyChair, http://www.easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/ZP6P},
  doi       = {10.29007/w8s9}}
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