Wayeb

Wayeb is a Complex Event Processing and Forecasting (CEP/F) engine written in Scala. It is based on symbolic automata and Markov models.

Quick start

Building

Assuming $WAYEB_HOME is the root directory of Wayeb, then go inside:

$ cd $WAYEB_HOME

Let's build a fat jar:

$ sbt assembly

Recognition

In $WAYEB_HOME/data/demo/data.csv you may find a very simple dataset, consisting of 100 events. The event type is either A, B or C. In $WAYEBHOME/patterns/demo/aseqbor_c.sre you may find a simple complex event definition for the above dataset. It detects an event of type A followed by another event of type B or C. If we want to run this pattern over the stream, we must first compile this pattern into an automaton (make sure you have created a results folder under $WAYEBHOME): ``` $ java -jar cef/target/scala-2.12/wayeb-0.2.0-SNAPSHOT.jar compile --patterns:patterns/demo/aseqborc.sre --declarations:patterns/demo/declarations.sre --outputFsm:results/aseqborc.fsm ``` Now, *results/aseqborc.fsm* is the produced serialized finite state machine. Note that we also provided as input a declarations.sre file. This file simply lets the engine know that the three predicates IsEventTypePredicate(A), IsEventTypePredicate(B) and IsEventTypePredicate(C) are mutually exclusive (i.e., an event can have only one type). This helps the compiler create a more compact automaton. We can use this FSM to perform event recognition on this simple dataset: ``` $ java -jar cef/target/scala-2.12/wayeb-0.2.0-SNAPSHOT.jar recognition --fsm:results/aseqbor_c.fsm --stream:data/demo/data.csv --statsFile:results/recstats ```

Forecasting

For forecasting, we first need to use a training dataset in order to learn a probabilistic model for the FSM. For this simple guide, we will use $WAYEB_HOME/data/demo/data.csv both as a training and as a test dataset, solely for convenience. Normally, you should use different datasets.

We first run maximum likelihood estimation:

$ java -jar cef/target/scala-2.12/wayeb-0.2.0-SNAPSHOT.jar mle --fsm:results/a_seq_b_or_c.fsm --stream:data/demo/data.csv --outputMc:results/a_seq_b_or_c.mc

$ java -jar cef/target/scala-2.12/wayeb-0.2.0-SNAPSHOT.jar forecasting --modelType:fmm --fsm:results/a_seq_b_or_c.fsm --mc:results/a_seq_b_or_c.mc --stream:data/demo/data.csv --statsFile:results/forestats --threshold:0.5 --maxSpread:10 --horizon:20 --spreadMethod:classify-nextk

License

Copyright (c) Elias Alevizos

Wayeb comes with ABSOLUTELY NO WARRANTY.

Wayeb follows a dual licensing scheme.

For use by individuals, Wayeb is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. This license is provided exclusively for research purposes. The results of any such research involving Wayeb must be made publicly available.

For commercial/institutional/governmental use or any other use by private or public legal entities, sharing, modifying and distributing Wayeb or any derivatives of it in any form, such as source code, libraries and executables, requires the written permission of its author(s) (Elias Alevizos), possibly accompanied by a request for licensing fees.

Documentation

• Building
• Overview
• Recognition
• Forecasting with full-oder Markov models
• Reading streams from Kafka
• Using Wayeb as a library
• How to cite Wayeb

Citing Wayeb

If you want to cite Wayeb, use the following references:

@inproceedings{DBLP:conf/debs/AlevizosAP17,
  author    = {Elias Alevizos and
               Alexander Artikis and
               George Paliouras},
  title     = {Event Forecasting with Pattern Markov Chains},
  booktitle = {Proceedings of the 11th {ACM} International Conference on Distributed
               and Event-based Systems, {DEBS} 2017, Barcelona, Spain, June 19-23,
               2017},
  pages     = {146--157},
  publisher = {{ACM}},
  year      = {2017},
  url       = {https://doi.org/10.1145/3093742.3093920},
  doi       = {10.1145/3093742.3093920}
} 

@inproceedings{DBLP:conf/lpar/AlevizosAP18,
  author    = {Elias Alevizos and
               Alexander Artikis and
               Georgios Paliouras},
  editor    = {Gilles Barthe and
               Geoff Sutcliffe and
               Margus Veanes},
  title     = {Wayeb: a Tool for Complex Event Forecasting},
  booktitle = {{LPAR-22.} 22nd International Conference on Logic for Programming,
               Artificial Intelligence and Reasoning, Awassa, Ethiopia, 16-21 November
               2018},
  series    = {EPiC Series in Computing},
  volume    = {57},
  pages     = {26--35},
  publisher = {EasyChair},
  year      = {2018},
  url       = {https://easychair.org/publications/paper/VKP1}
}

Contributors

• Elias Alevizos (main developer).
• Emmanouil Ntoulias (distributed version, to be released; stream simulator).
• Maria Petropanagiotaki (relaxed selection policies).
• Evangelos Michelioudakis (sbt structure).