AQuery/demo/README.md

Triggers Demo

This folder contains a demo workflow for the two types of triggers.

• An interval-based trigger will be set up to execute a stored procedure demoi defined in demo/putdata.cpp that inserts a .csv file from data/electricity to the table source every 5 seconds.
• A Conditional trigger will be triggered by condition democq defined in demo/query.cpp that checks and returns true when more than 200 rows of data are inserted into table source. Once triggered, it will execute a stored procedure democa defined in demo/democa.cpp that trains the incremental random forest by the new data.
• See demo/prep.a for parameters of the random forest.

Run the demo

Preparation

• Preprocess data
  • Put electricity dataset to /data/electricity_orig
  • Run python3 rfdata_preproc.py to generate .csv files to data/electricity/
• Use demo/setup.sh to
  • setup stored procedures for this demo
  • compile random forest user module used in this demo
  • compile queries used in this demo

Running the demo

• Run AQuery prompt python3 prompt.py
• Use Automated AQuery script in demo/demo.aquery to execute the workflow. It does the following things in order:
  • Register user module, create a new random forest by running f demo/prep.a
  • Register stored procedures.
  • Create an Interval-based Trigger that executes payload demoi every 5 seconds
  • Create a Conditional Trigger that executes payload democa whenever condition democq returns a true. While condition democq is tested every time new data is inserted to table source.
  • Loads test data by running demo/test.a
• Use query select predict(x) from test to get predictions of the test data from current random forest.
  • In AQuery prompt, an extra exec command after the query is needed to execute the query.
• Use query select test(x, y) from test will also calculate l2 error.