readme

README.md

@@ -1,5 +1,5 @@
 ## Introduction
-This repository is used to store all the code and data we use to produce experimental results in paper "Towards Effective Log Clustering"
+This repository contains all the code and data we use to produce experimental results in paper "Towards Effective Log Clustering"
 
 ## Organization of the repository
 - data folder: Contains all data files that are used in the experiments
@@ -10,12 +10,29 @@ This repository is used to store all the code and data we use to produce experim
 - script_figure_3.R: produce Figure 3 as shown in the paper.
 - script_figure_4.R: produce Figure 4 as shown in the paper.
 
+## Organization of *data* folder
+There are three datasets that are used in all experiments including: IIT Bombay (bombay), UB Exam (ub) and PocketData-Google+ (googleplus) datasets. The queries with corresponding labels in each dataset is stored in the file **[data_set]_queries.csv**.
+
+There are three different SQL query similarity metrics that are used in all experiments including: Aligon (aligon), Aouiche (aouiche), Makiyama (Makiyama). 
+
+The pairwise distance matrix between queries in a dataset using a particular similarity metric without regularization step is stored in the file **[dataset]_[similarity_metric].csv**. For instance, the distance matrix for PocketData-Google+ dataset with Makiyama similarity metric without regularization is stored in the file *googleplus_makiyama.csv*.
+
+When regularization is used, the pairwise distance matrix is stored in the file **[dataset]_[similarity_metric]_regularization.csv**. For example, the distance matrix for UB Exam dataset with Aoiuche similarity metric when regularization is applied is stored in the file *ub_aouiche_regularization.csv*.
+
+In order to evaluate different modules in regularization step, we consider 4 different modules: Naming (denote as 1), Expression Standardization (2), FROM-nested Subquery (3) and UNION Pull-out (4). The pairwise distance matrix when each module is applied individually is stored in the file **[dataset]_[similarity_metric]_regularization_[module_number]**. For example, the distance matrix for IIT Bombay dataset with Aligon similariy metric when Naming module in regularization step is used is stored in the file *bombay_aligon_regularization_1.csv*.
+
+**result.csv** contains all the numbers that are needed to produce Figure 3 in the paper.
+
+**modules.csv** contains all the number that are needed to produce Figure 4 in the paper.
+
 ## Reproducing experimental results
 ### Reproduce figure 2
 In order to reproduce distribution of silhouette coefficients when using Aligon similarity without regularization and when regularization is applied as shown in Figure 2 of the paper, users can open the file *script_figure_2.R*. Running this script file will produce the silhouette plots in folder *figure*.
 
 ### Reproduce figure 3
-In order to reproduce the plots for comparison between three similarity metrics (Aligon, Aouiche, Makiyama) on three datasets (IIT Bombay, UB Exam and PocketData-Google+ datasets) with and without regularization as shown in Figure 3 of the paper, users can use the file *script_figure_3.R*. This script requires an input file *result.csv* in *data* folder. The number in *result.csv* can be filled in by running the following commands in R:
+In order to reproduce the plots for comparison between three similarity metrics (Aligon, Aouiche, Makiyama) on three datasets (IIT Bombay, UB Exam and PocketData-Google+ datasets) with and without regularization as shown in Figure 3 of the paper, users can use the file *script_figure_3.R*. 
+
+This script requires an input file *result.csv* in *data* folder. We have filled all the numbers for this file. For reproducibility, the numbers in *result.csv* can be manually filled by running the following commands in R:
 
     # load two files evaluation.R and utils.R
     source(file = "./evaluation.R")
@@ -30,14 +47,18 @@ In order to reproduce the plots for comparison between three similarity metrics
     dataset <- read.csv(file = "./data/bombay_queries.csv", header = TRUE, sep = "\t")
 
     # read distance matrix
-    distMat <- readDistMat("./data/bombay_aligon.csv") 
+    distMat <- readDistMat("./data/bombay_aligon_regularization.csv") 
 
     # print different clustering validation measures
     print(avgSilhoette(distMat, dataset$label))
     print(BetaCV(distMat, dataset$label))
     print(DunnIndex(distMat, dataset$label))
 
-When the input file is ready, running 'script_figure_3.R' file will produce the corresponding figures in folder *figure*.
+When the input file is ready, running *script_figure_3.R* file will produce the corresponding figures in folder *figure*.
 
 ### Reproduce figure 4
-In order to reproduce the plots for comparing the effect of different modules in regularization as shown in Figure 4 of the paper, users can use the file *script_figure_4.R*. This script requires an input file *modules.csv* in *data* folder. Running this script will produce the corresponding figure in folder *figure*.
+In order to reproduce the plots for comparing the effect of different modules in regularization as shown in Figure 4 of the paper, users can use the file *script_figure_4.R*. 
+
+This script requires an input file *modules.csv* in *data* folder. We have filled all the numbers in this file. For reproducibility, the numbers in this file can be manually filled by computing average silhouette coefficients, BetaCV and Dunn Index for each module in regularization.
+
+When the input file is ready, running this *script_figure_4.R* will produce the corresponding figure in folder *figure*.