Wayeb: a Tool for Complex Event Forecasting

10 pages•Published: October 23, 2018

Elias Alevizos, Alexander Artikis and Georgios Paliouras

Abstract

Complex Event Processing (CEP) systems have appeared in abundance during the last two decades. Their purpose is to detect in real–time interesting patterns upon a stream of events and to inform an analyst for the occurrence of such patterns in a timely manner. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CEP engine. We present Wayeb, a tool that attempts to address the issue of Complex Event Forecasting. Wayeb employs symbolic automata as a computational model for pattern detection and Markov chains for deriving a probabilistic description of a symbolic automaton.

Keyphrases: data streaming, formal languages and automata theory, pattern matching, random walks and markov chains

In: Gilles Barthe, Geoff Sutcliffe and Margus Veanes (editors). LPAR-22. 22nd International Conference on Logic for Programming, Artificial Intelligence and Reasoning, vol 57, pages 26-35.

Links:	https://easychair.org/publications/paper/VKP1
	https://doi.org/10.29007/2s9t

BibTeX entry

@inproceedings{LPAR-22:Wayeb_Tool_Complex_Event,
  author    = {Elias Alevizos and Alexander Artikis and Georgios Paliouras},
  title     = {Wayeb: a Tool for Complex Event Forecasting},
  booktitle = {LPAR-22. 22nd International Conference on Logic for Programming, Artificial Intelligence and Reasoning},
  editor    = {Gilles Barthe and Geoff Sutcliffe and Margus Veanes},
  series    = {EPiC Series in Computing},
  volume    = {57},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/VKP1},
  doi       = {10.29007/2s9t},
  pages     = {26-35},
  year      = {2018}}

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