diff --git a/plot_baselines.py b/plot_baselines.py
new file mode 100644
index 0000000000000000000000000000000000000000..c895be85719c6794ccf2d9166f9b23a17a078d5e
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+++ b/plot_baselines.py
@@ -0,0 +1,145 @@
+#!/usr/bin/python
+
+import matplotlib as mpl
+mpl.use('Agg')
+import os, errno, time, sys, pylab
+from scipy import *
+from scipy import optimize
+from scipy import special
+from scipy import stats
+#import numpy as np
+import matplotlib.cm as cm
+import matplotlib.ticker as ticker
+from datetime import datetime
+import logging, csv
+from os.path import basename, exists
+
+plotdir = os.path.abspath('.') + '/results'
+if not os.path.exists(plotdir):
+    os.makedirs(plotdir)
+    print "Created plotdir: %s" % (plotdir)
+logdir = os.path.abspath('.') + '/log'
+
+def stdev(v):
+    """
+    Return the Bessel corrected standard deviation of a series of measurements
+    """
+    return sqrt(len(v) * (mean(square(v)) - mean(v)**2) / (len(v) - 1))
+
+def mysql_querytimes():
+    """
+    """
+    # The s15 mysql query timing results taken from
+    # https://confluence.lsstcorp.org/display/DM/S15+Large+Scale+Tests
+    # Values on website are reported in seconds
+    counts = [47.75, 40.99, 48.33]
+    shortrunning = [0.09, 0.33]
+    fullscans = [247.61, 244.24, 1088.09, 1077.38, 898.61]
+    joins = [1381.44, 1291.38]
+    neighbor = [676.02]
+    mysqls15 = asarray(counts + shortrunning + fullscans + joins + neighbor)
+    #tms = [ts * 1000 for ts in mysqls15]
+    return mysqls15
+
+def monetdb_querytimes(qlog):
+    """
+    """
+    # From the qlog name, we can deduce the query number
+    # and run type
+    qtime = []
+    with open(qlog, 'rb') as csvfile:
+        qreader = csv.reader(csvfile, delimiter=';')
+        for row in qreader:
+            run = int(row[0])
+            qtime.append(float(row[1])) # unit is ms
+    #for i in range(len(qtime)):
+    #    print "%s: qtime = %s" % (i, qtime[i])
+    return mean(qtime), stdev(qtime)
+
+def plot_results():
+    """
+    """
+
+    logdir = 'sql/s15/log'
+    plotdir = '.'
+    plotfiles = []
+
+    node = "stones10"
+    dbversion = "dec2016sp1"
+
+    # We have hot and cold runs
+    runtype = ['hot', 'cold']
+    #runtype = ['hot']
+    # We divide the queries in five groups
+    counts = ['q01', 'q02', 'q03']
+    shortrunning = ['q04', 'q05']
+    fullscans = ['q06', 'q07', 'q08', 'q09', 'q10']
+    joins = ['q11', 'q12']
+    neighbor = ['q13']
+    queries = asarray(counts + shortrunning + fullscans + joins + neighbor)
+    mysqls15 = mysql_querytimes()
+
+    monetdbs15 = {}
+    for temp in runtype:
+        monetdbs15[temp] = {}
+        monetdbs15[temp]['qt_avg'] = []
+        monetdbs15[temp]['qt_std'] = []
+        for qnr in queries:
+            qlog = logdir + "/%s.%s.%s.%s.log" % (qnr, temp, node, dbversion)
+            #print "qlog = %s" % (qlog)
+            if not exists(qlog):
+                sys.exit("Run timings. Log file %s does not exist (yet)." % (qlog))
+            qt_avg, qt_std = monetdb_querytimes(qlog)
+            print "%s qnr: %s: qt_avg = %s; qt_std = %s" % (temp, qnr, qt_avg, qt_std)
+            monetdbs15[temp]['qt_avg'].append(qt_avg)
+            monetdbs15[temp]['qt_std'].append(qt_std)
+    mhot_t = [t * 0.001 for t in monetdbs15['hot']['qt_avg']]
+    mhot_s = [s * 0.001 for s in monetdbs15['hot']['qt_std']]
+    mcold_t = [t * 0.001 for t in monetdbs15['cold']['qt_avg']]
+    mcold_s = [s * 0.001 for s in monetdbs15['cold']['qt_std']]
+
+    #print "monetdbs15 = %s" % (monetdbs15)
+    fig = pylab.figure(figsize=(28,8))
+    ax = fig.add_subplot(111)
+
+    width = 0.2
+    ekw = dict(ecolor='k', lw=2, capsize=5, capthick=2)
+    r1 = ax.bar(arange(len(mysqls15)), mysqls15, width, color='gray' \
+               ,error_kw=ekw,label='MySQL')
+    r2 = ax.bar(arange(len(mcold_t)) + width, mcold_t, width, yerr=mcold_s, color='dodgerblue' \
+               ,error_kw=ekw,label='MonetDB cold')
+    r3 = ax.bar(arange(len(mhot_t)) + 2 * width, mhot_t, width, yerr=mhot_s, color='red' \
+               ,error_kw=ekw,label='MonetDB hot')
+    ax.set_ylabel('Time [s]', fontsize='25')
+    ax.set_xticks(arange(len(mysqls15)) + width)
+    ax.set_xticklabels([q.upper() for q in queries])
+    #ax.set_ylim(ymin=10, ymax=35000)
+    ax.set_ylim(ymin=0.001, ymax=10000)
+    ax.set_yscale("log")
+    #ax.legend(loc='upper right')
+    ax.legend(loc='upper left', fontsize='25')
+
+    for i in range(len(ax.get_xticklabels())):
+        #ax.get_xticklabels()[i].set_size('xx-large')
+        ax.get_xticklabels()[i].set_size('25')
+    for i in range(len(ax.get_yticklabels())):
+        #ax.get_yticklabels()[i].set_size('xx-large')
+        ax.get_yticklabels()[i].set_size('25')
+    pylab.grid(True)
+
+    fname = 'baseline_lsst_queries.eps'
+
+    plotfile = plotdir + '/' + fname
+    plotfiles.append(plotfile)
+    pylab.savefig(plotfile, dpi=400, bbox_inches='tight')
+    print plotfile
+
+    return plotfile
+
+def main():
+    #query = sys.argv[1]
+    plot_results()
+
+if __name__ == '__main__':
+    main()
+