diff --git a/code/eval/charts/charts.ipynb b/code/eval/charts/charts.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2a23df0b366927eca071c57ff68f29b0eba3de2d
--- /dev/null
+++ b/code/eval/charts/charts.ipynb
@@ -0,0 +1,20233 @@
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+ "title": {
+ "x": 0.05
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+ "xaxis": {
+ "automargin": true,
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+ "text": "Total Time per Query Key Over Time for pg"
+ },
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 1
+ ],
+ "tickformat": "%d.%m. %H:%M:%S",
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+ },
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+ "anchor": "x",
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+ "text": "Total Time (s)"
+ }
+ }
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+ "metadata": {},
+ "output_type": "display_data"
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+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.dates as mdates\n",
+ "import plotly.express as px\n",
+ "\n",
+ "filepaths = [\n",
+ " r'C:\\Studium_MIN\\05_Masterarbeit\\thesis\\ma_code\\code\\eval\\experiments\\240212_first_try\\query_logs\\memgraph\\2024-02-11_11-58-08_query_logs.csv',\n",
+ " r'C:\\Studium_MIN\\05_Masterarbeit\\thesis\\ma_code\\code\\eval\\experiments\\240212_first_try\\query_logs\\neo4j\\2024-02-11_11-58-08_query_logs.csv',\n",
+ " r'C:\\Studium_MIN\\05_Masterarbeit\\thesis\\ma_code\\code\\eval\\experiments\\240212_first_try\\query_logs\\ongdb\\2024-02-11_11-58-08_query_logs.csv',\n",
+ " r'C:\\Studium_MIN\\05_Masterarbeit\\thesis\\ma_code\\code\\eval\\experiments\\240212_first_try\\query_logs\\pg\\2024-02-11_11-58-08_query_execution_logs.csv'\n",
+ "]\n",
+ "\n",
+ "for filepath in filepaths:\n",
+ "\n",
+ " df = pd.read_csv(filepath)\n",
+ "\n",
+ " # Ensure 'Start Time' is converted to a recognizable datetime format\n",
+ " df['Start Time'] = pd.to_datetime(df['Start Time'], unit='s')\n",
+ "\n",
+ " # # Extracting the database name from the filepath for the title\n",
+ " db_name = filepath.split('\\\\')[-2] # Assumes the database name is the second to last part of the path\n",
+ "\n",
+ " # Use Plotly Express to plot\n",
+ " fig = px.line(df, x='Start Time', y='Total Time (s)', color='Query Key',\n",
+ " title=f'Total Time per Query Key Over Time for {db_name}')\n",
+ "\n",
+ " # Customize the tick format for the x-axis to show date and time\n",
+ " # You can adjust the format as per your needs. Here are some examples:\n",
+ " # '%Y-%m-%d %H:%M:%S' for \"Year-Month-Day Hour:Minute:Second\"\n",
+ " # '%H:%M:%S' for \"Hour:Minute:Second\" if you want to focus on time of day\n",
+ " # '%Y-%m-%d' for \"Year-Month-Day\" if the date is more important\n",
+ " fig.update_xaxes(tickformat='%d.%m. %H:%M:%S', title_text='Time')\n",
+ "\n",
+ "\n",
+ " # ax.set_title(f'Total Time per Query Key Over Time for {db_name}')\n",
+ " \n",
+ " # Update y-axis label\n",
+ " fig.update_yaxes(title_text='Total Time (s)')\n",
+ "\n",
+ " # Show the plot\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ "<Figure size 720x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 720x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 720x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "ename": "KeyError",
+ "evalue": "'Total Time (s)'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32mc:\\Python38\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3360\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3362\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mc:\\Python38\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mc:\\Python38\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'Total Time (s)'",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32mC:\\Users\\SVEN-O~1\\AppData\\Local\\Temp/ipykernel_7836/3302337684.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Query Key'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[0msubset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Query Key'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Start Time'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Total Time (s)'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;31m# Extracting the database name from the filepath for the title\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mc:\\Python38\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3456\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3457\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3458\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3459\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3460\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mc:\\Python38\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3361\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3362\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3365\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'Total Time (s)'"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 720x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.dates as mdates\n",
+ "import plotly.express as px\n",
+ "\n",
+ "filepaths = [\n",
+ " r'C:\\Studium_MIN\\05_Masterarbeit\\thesis\\ma_code\\code\\eval\\experiments\\240212_first_try\\query_logs\\memgraph\\2024-02-11_11-58-08_query_logs.csv',\n",
+ " r'C:\\Studium_MIN\\05_Masterarbeit\\thesis\\ma_code\\code\\eval\\experiments\\240212_first_try\\query_logs\\neo4j\\2024-02-11_11-58-08_query_logs.csv',\n",
+ " r'C:\\Studium_MIN\\05_Masterarbeit\\thesis\\ma_code\\code\\eval\\experiments\\240212_first_try\\query_logs\\ongdb\\2024-02-11_11-58-08_query_logs.csv',\n",
+ " r'C:\\Studium_MIN\\05_Masterarbeit\\thesis\\ma_code\\code\\eval\\experiments\\240212_first_try\\query_logs\\pg\\2024-02-11_11-58-08_query_execution_logs.csv'\n",
+ "]\n",
+ "\n",
+ "for filepath in filepaths:\n",
+ "\n",
+ " # Assuming df is your DataFrame\n",
+ "# Replace 'filepath' with the actual path of your data file\n",
+ "df = pd.read_csv(filepath)\n",
+ "df['Start Time'] = pd.to_datetime(df['Start Time'], unit='s')\n",
+ "\n",
+ "fig = px.line(df, x='Start Time', y='Total Time (s)', color='Query Key',\n",
+ " title='Total Time per Query Key Over Time')\n",
+ "fig.update_xaxes(\n",
+ " dtick=\"M1\",\n",
+ " tickformat=\"%b\\n%Y\",\n",
+ " title_text='Time'\n",
+ ")\n",
+ "fig.update_yaxes(title_text='Total Time (s)')\n",
+ "fig.show()\n",
+ "\n",
+ " # # Load data directly from the current file\n",
+ " # df = pd.read_csv(filepath)\n",
+ "\n",
+ " # # Convert timestamps to datetime objects for plotting\n",
+ " # df['Start Time'] = pd.to_datetime(df['Start Time'], unit='s')\n",
+ "\n",
+ " # # Plotting\n",
+ " # fig, ax = plt.subplots(figsize=(10, 6))\n",
+ "\n",
+ " # for key in df['Query Key'].unique():\n",
+ " # subset = df[df['Query Key'] == key]\n",
+ " # ax.plot(subset['Start Time'], subset['Total Time (s)'], label=key)\n",
+ "\n",
+ " # # Extracting the database name from the filepath for the title\n",
+ " # db_name = filepath.split('\\\\')[-2] # Assumes the database name is the second to last part of the path\n",
+ "\n",
+ " # # Formatting the plot\n",
+ " # ax.set_title(f'Total Time per Query Key Over Time for {db_name}')\n",
+ " # ax.set_xlabel('Time')\n",
+ " # ax.set_ylabel('Total Time (s)')\n",
+ " # ax.legend()\n",
+ " # ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))\n",
+ " # plt.xticks(rotation=45)\n",
+ " # plt.tight_layout()\n",
+ "\n",
+ " # # Show the plot\n",
+ " # plt.show()\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/code/eval/experiments/240212_first_try/query_logs/memgraph/query_log.xlsx b/code/eval/experiments/240212_first_try/query_logs/memgraph/query_log.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..f5c6490e7b0d62a50fee06f7a19a6643678ca3bc
Binary files /dev/null and b/code/eval/experiments/240212_first_try/query_logs/memgraph/query_log.xlsx differ
diff --git a/code/eval/experiments/240212_first_try/query_logs/pg/2024-02-11_11-58-08_query_execution_logs.csv b/code/eval/experiments/240212_first_try/query_logs/pg/2024-02-11_11-58-08_query_execution_logs.csv
index 0cafc488517194b82bbfc686454b502c02f27d8d..8620aae277c948c951df38dd549a4ac405b8626e 100644
--- a/code/eval/experiments/240212_first_try/query_logs/pg/2024-02-11_11-58-08_query_execution_logs.csv
+++ b/code/eval/experiments/240212_first_try/query_logs/pg/2024-02-11_11-58-08_query_execution_logs.csv
@@ -1,4 +1,4 @@
-Query Key,Start Time,Execution Time (seconds),Rows Fetched
+Query Key,Start Time,Total Time (s),Rows Fetched
ancestors,1707652688.6375496,0.006899833679199219,0
descendant,1707652688.6460233,0.0014214515686035156,0
path,1707652688.649565,0.0037491321563720703,0
diff --git a/code/eval/queries/sql/cdm_sql_queries.py b/code/eval/queries/sql/cdm_sql_queries.py
index daa8d4749e9f263883eecfe7442b003d1fb51dbb..4648fe5e044ecf1272319e2e2a85f0513248b2d1 100644
--- a/code/eval/queries/sql/cdm_sql_queries.py
+++ b/code/eval/queries/sql/cdm_sql_queries.py
@@ -143,7 +143,7 @@ ORDER BY
writer = csv.writer(csv_log_file)
csv_log_file.seek(0, 2) # Move to the end of the file
if csv_log_file.tell() == 0: # If file is empty, write a header
- writer.writerow(['Query Key', 'Start Time', 'Execution Time (seconds)', 'Rows Fetched'])
+ writer.writerow(['Query Key', 'Start Time', 'Total Time (s)', 'Rows Fetched'])
for key, query in queries.items():
start_time = time.time()
cur.execute(query)