Added documentation for trigger demo

demo/README.md

@@ -0,0 +1,28 @@
+# 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](/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](/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](/demo/action.cpp) that trains the incremental random forest by the new data. 
+- See [demo/prep.a](/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](/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](/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`](/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](/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.

demo/demo.aquery

@@ -3,15 +3,15 @@
 f demo/prep.a
 exec
 
-procedure demoi run
-procedure democq run
-procedure democa run
+procedure demoi load
+procedure democq load
+procedure democa load
 
-create trigger t action demoi interval 15000
+create trigger t action demoi interval 5000
 exec
 
 create trigger c on source action democa when democq
 exec
 
-# f demo/test.a
-# exec
+f demo/test.a
+exec

demo/prep.a

@@ -1,13 +1,14 @@
 create table source(x vecdouble, y int64);
 LOAD MODULE FROM "./libirf.so"  FUNCTIONS (
-    newtree(height:int, f:int64, sparse:vecint, forget:double, maxf:int64, noclasses:int64, e:int) -> bool,
+    newtree(height:int, f:int64, sparse:vecint, forget:double, noclasses:int64, e:int) -> bool,
     fit_inc(X:vecvecdouble, y:vecint64) -> bool,
-    predict(X:vecvecdouble) -> vecint  
+    predict(X:vecvecdouble) -> vecint ,
+    test(X:vecvecdouble, y:vecint64) -> double  
 );
 
 create table elec_sparse(v int);    
 
 insert into elec_sparse values (0), (1), (1), (1), (1), (1), (1);
 
-select newtree(30, 7, elec_sparse.v, 0, 4, 2, 1) from elec_sparse
+select newtree(30, 7, elec_sparse.v, 0.3, 2, 1) from elec_sparse
 

demo/setup.sh

@@ -0,0 +1,6 @@
+#!/bin/sh 
+
+make -C ../sdk irf
+mkdir ../procedures 
+cp demo*.aqp ../procedures 
+make 

demo/test.a

@@ -1,3 +1,7 @@
+-- select predict(x) from test; 
+-- select test(x, y) from test; 
+
+
 create table test(x vecdouble, y int64);
 load complex data infile "data/electricity/electricity872.csv" into table test fields terminated by ',' element terminated by ';';
-select predict(x) from test
+