Complex Event Recognition with Symbolic Register Transducers

This repository contains the code needed to reproduce the experiments found in the paper Complex Event Recognition with Symbolic Register Transducers. The original source code has been obtained from https://github.com/CORE-cer/CORE-experiments. It has been adapted appropriately so that the engines can run relational patterns.

How to run

Requirements

• Python >= 3.8
• Java >= 11

Building the systems

Under the sources folder, you may find the source code used to create the executables. Under the jars folder, you may find the executables used to run the experiments presented in the paper for Wayebm, SASE and FlinkCEP. Esper's license does not allow for re-distribution. Therefore, you need to create an executable jar for Esper yourself, by using the relevant code under the sources folder and linking it to Esper. If you wish to modify the behavior of the engines, you may do so and then create a new fat jar.

Running the experiments

The experiments scripts are located in the scripts folder. You may run each script with python. The results are stored under the results folder. There are 7 scripts:

• stock_reg.py: Runs experiments on the stock market dataset for sequential (relational) patterns.
• stock_kleene.py: Runs experiments on the stock market dataset for (relational) patterns with Kleene operators.
• stock_kleene_nested.py: Runs experiments on the stock market dataset for (relational) patterns with nested Kleene operators.
• stock_other.py: Runs experiments on the stock market dataset for (relational) patterns with various operators.
• smart_homes_reg.py: Runs experiments on the smart homes dataset for sequential (relational) patterns.
• smart_homes_kleene.py: Runs experiments on the smart homes dataset for (relational) patterns with Kleene operators.
• smart_homes_kleene_nested.py: Runs experiments on the smart homes dataset for (relational) patterns with nested Kleene operators.
• taxis_reg.py: Runs experiments on the taxis dataset for sequential (relational) patterns.
• taxis_kleene.py: Runs experiments on the taxis dataset for (relational) patterns with Kleene operators.
• taxis_kleene_nested.py: Runs experiments on the taxis dataset for (relational) patterns with nested Kleene operators.

Before running a script, you need to set the WORKING_FOLDER variable inside the script. It should point to your local folder where you have downloaded the becnhmark suite. You also need to unzip the zipped streams/datasets under each stream folder (e.g., stocks.7z under stockstream).