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From attila.peter@capgemini.com Mon Sep 11 20:41:26 2017
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From: "Peter, Attila" <attila.peter@capgemini.com>
To: Siegfried Wonneberger <siegfried.wonneberger@ift-informatik.de>
Subject: Fwd: Analysis of the cybersec beer tasting results
Thread-Topic: Analysis of the cybersec beer tasting results
Thread-Index: AdMgD1pQn+pEWIplTDKsKcgD9LJu2wLHjX1A
Date: Mon, 11 Sep 2017 18:41:22 +0000
Message-ID: <2C6EF9D1-752C-4714-B3E3-3146A4AD8F24@capgemini.com>
References: <1AF5367555908F48BCD20EFE8CE7231A46D27D79@DE-CM-MBX25.corp.capgemini.com>
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Sent from my iPhone
Begin forwarded message:
From: "Peter, Attila" <attila.peter@capgemini.com<mailto:attila.peter@capge=
mini.com>>
Date: August 28, 2017 at 7:46:22 PM GMT+2
To: DL DE Infra Cyber Security PC <infracybersecuritypc.de@capgemini.com<ma=
ilto:infracybersecuritypc.de@capgemini.com>>
Subject: Analysis of the cybersec beer tasting results
Cybersectasters et al,
After our last team meeting in Frankfurt, Sascha offered a variety of (non-=
alcoholic) beers for tasting. 10 of us gathered to taste and rate 13 differ=
ent brands and eventually find the tops and flops among them.
After the dust settled, Sascha started some analysis on the data that was s=
howing some interesting characteristics. He also asked me if I could look a=
t the results and over the weekend I gave it a try=85. I fired up a splunk =
instance in AWS and loaded the data. If you don=92t know splunk, it is a da=
ta gathering and analysis platform providing near and real time analytics o=
f machine data of any kind. For more go to www.splunk.com<http://www.splunk=
.com>.
Anyways=85 Sascha was after a method to group tasters based on their beer p=
references. That sounds simple at a first glance but if you consider that w=
e were 10 people for 13 brands of beer than you=92ll be quick to realize th=
at this is not so obvious. The problem of clustering data is nothing new. M=
any different algorithms exist (K-Means, DBSCAN, Birch, etc), all having th=
eir strength and tradeoff. Which one to pick, largely depends on the type a=
nd the spread/density of the data. ( You can check out visualization exampl=
es of K-Means and DBSCAN clustering algorithms on the blog of Naftali Harri=
s : https://www.naftaliharris.com/blog/visualizing-k-means-clustering/ and=
https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/)
We had 13 brands and our rating was based on simple ranking (flop beer 1 po=
int -> top beer 13 points). With that we produced a matrix of tasters x bee=
r brand, having the rank in the intersection:
[cid:image011.jpg@01D32036.4FE707A0]
Just so that you can see the results of the contest :
[cid:image015.jpg@01D32036.4FE707A0]
Krombacher took the top spot with 103 points, while Jever Fun scored at low=
est with a total of 22 points (not so much fun=85).
The columns in the chart display the totals, while the lines represent the =
maximum, minimum and average points given for each brand. The scale for the=
lines is on the right side of the chart.
A few observations based on visuals only:
Going from left to right the max line (yellow one) is pretty flat until we =
hit Clausthaler, then it takes a slight dip on Licher and then again on Vel=
tins and Jever Fun. That means these were the 3 brands that nobody liked to=
o much.
Based on the min_rank line (blue one) we can observe, that Moenchshof has t=
he minimum variance in ratings : max and min values are really close. That=
=92s a beer that nobody dislikes and everybody could get along with.
Krombacher, Clausthaler, Neumarkter Lammsbraeu and Stoertebecker, on the o=
ther hand, got very low scores as well as very high ones. So, are these 4 b=
rands everybodys=92 darling or what ?. Well, the average line (red one) cou=
ld tell a story. Krombacher has got a high average, meaning the high scores=
were in majority. Sure, it took the top spot. Neumarkter Lammsbraeu still =
good with a quite high average rating and rank #5.
Bitburger also exhibits high average and very high max and also not the low=
est minimum. It scored 2nd.
The average drops at Clausthaler to a value that suggests that the tester g=
roup was divided on this: low and high values had roughly the same occurran=
ce, the distance to max and min is pretty much the same.
How about the 4th in this group, Stoertebecker ? That=92s a weird one : the=
average is much closer to the minimum as to the max. It also scored in the=
tail section, which means only a few people liked it and most testers didn=
=92t.
Based on these observation it is a safe bet to tell that the group can be c=
ategorized based on liking Krombacher + Bitburger and disliking Jever Fun. =
But how about Clausthaler and Stoertebecker ? These also seem to be charact=
eristical (remember :Clausthaler divides the group roughly equally and Stoe=
rtebecker is somebody=92s darling + the rest don=92t like it). So these 2 s=
hould be taken into account as well.
But hey, we are living the age of automation and machine learning so we are=
not going to do it by hand. Luckily, Splunk supports a range of machine le=
arning algorithms. Clustering numerical values is rather simple. I won=92t =
put up the splunk searches here (we can do that part on a live session if t=
here is any interest). I=92m going to post only the results of the K-Means =
clustering. I tried various cluster sizes, k=3D3 seems to be an ideal one. =
Based on this the tester team can be clustered as the following :
[cid:image017.jpg@01D32036.4FE707A0]
Wow=85 Sascha landed alone in a cluster group ! Surprise : the top brand fo=
r this group is Stoertebecker. Flop is Flensburger.
Another small group is the last one : only Ingo and I (Attila) were assigne=
d to that one. We like Clausthaler and Neumarkter Lammsbraeu, and apparentl=
y cannot drink Jever Fun.
The largest cluster group (7 people) prefer Bitburger and Krombacher (not v=
ery surprised based on the ranking chart). They also refuse to drink Flensb=
urger, Jever Fun and Veltins.
That=92s pretty cool ! Now we know which people can get along for a beer. W=
e=92ve been also assured that Sascha is an individualist=85
The table above is nice and sufficient, however, we cannot see who likes an=
d hates which brand. I did some further Splunk massage on the data and rend=
ered the results in a sankey diagram:
[cid:image020.jpg@01D32036.4FE707A0]
Here you can see the testers linked to their cluster group. Every tester is=
then linked to 2 beer brands : one is their individual top beer and the ot=
her their absolute no-go beer. To reflect this, the beer brands are eventua=
lly linked to one of the Top Beer or Flop Beer groups.
A final observation : we could not detect absolute extremities within the g=
roup. That is: nobody liked the most any brand that another person disliked=
the most. This is good. It means, we still can go out as a team :D :D :D.
I decided to share this analysis with the entire team in the hope to wake i=
nterest in this field.
Even if not, it won=92t hurt to know that we have really cool technology at=
our fingertips.
We should repeat the experiment in the fall when the entire cybersec team g=
athers in Frankfurt. It=92s gonna be fun and I=92m sure we are all very exc=
ited about the results!
Cheers,
_______________________________________________________________________
[Email_CBE.gif]Attila Peter
Senior Cyber Security Architect | Cloud Infrastructure Services
Capgemini | Frankfurt
Phone: +496995151435 =96 Mobile: +4915111374370
e-mail: attila.peter@capgemini.com<mailto:attila.peter@capgemini.com>
www.de.capgemini.com<http://www.de.capgemini.com/>
Mainzer Landstra=DFe 180, 60327 Frankfurt, Germany
People matter, results count.
_______________________________________________________________________
Connect with Capgemini:
[cid:image006.png@01CCBA6D.0B1D4150]<http://www.de.capgemini.com/insights/b=
logs/>[cid:image007.png@01CCBA6D.0B1D4150]<https://twitter.com/#!/Capgemini=
DE>[cid:image008.png@01CCBA6D.0B1D4150]<https://www.facebook.com/CapgeminiD=
E>[cid:image009.gif@01CCBA6B.A83786A0]<https://plus.google.com/b/1080498089=
62486716484/108049808962486716484/posts>[cid:image013.png@01CCBA6D.0B1D4150=
]<http://www.linkedin.com/company/capgemini>[cid:image014.png@01CCBA6D.0B1D=
4150]<https://www.xing.com/companies/capgemini/about>[cid:image015.png@01CC=
BA6D.0B1D4150]<http://www.slideshare.net/capgemini>[cid:image016.png@01CCBA=
6D.0B1D4150]<http://www.youtube.com/capgeminimedia>
________________________________
Firma: Capgemini Outsourcing Services GmbH
Gesch=E4ftsf=FChrer: John Brahim =95 Patrick Potters =95 Jean-Philippe Bol
Amtsgericht D=FCsseldorf, HRB 58881
This message contains information that may be privileged or confidential an=
d is the property of the Capgemini Group. It is intended only for the perso=
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<br>
Sent from my iPhone</div>
<div><br>
Begin forwarded message:<br>
<br>
</div>
<blockquote type=3D"cite">
<div><b>From:</b> "Peter, Attila" <<a href=3D"mailto:attila.pe=
ter@capgemini.com">attila.peter@capgemini.com</a>><br>
<b>Date:</b> August 28, 2017 at 7:46:22 PM GMT+2<br>
<b>To:</b> DL DE Infra Cyber Security PC <<a href=3D"mailto:infracyberse=
curitypc.de@capgemini.com">infracybersecuritypc.de@capgemini.com</a>><br>
<b>Subject:</b> <b>Analysis of the cybersec beer tasting results</b><br>
<br>
</div>
</blockquote>
<div><span></span></div>
<blockquote type=3D"cite">
<div>
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<p class=3D"MsoNormal">Cybersectasters et al,<o:p></o:p></p>
<p class=3D"MsoNormal"><o:p> </o:p></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">After our last team meeting in Frankfurt, Sascha offered a variety of (no=
n-alcoholic) beers for tasting. 10 of us gathered
to taste and rate 13 different brands and eventually find the tops and flo=
ps among them.<o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">After the dust settled, Sascha started some analysis on the data that was=
showing some interesting characteristics. He also
asked me if I could look at the results and over the weekend I gave it a t=
ry=85. I fired up a splunk instance in AWS and loaded the data. If you don=
=92t know splunk, it is a data gathering and analysis platform providing ne=
ar and real time analytics of machine
data of any kind. For more go to <a href=3D"http://www.splunk.com">www.spl=
unk.com</a>.<o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">Anyways=85 Sascha was after a method to group tasters based on their beer=
preferences. That sounds simple at a first glance but
if you consider that we were 10 people for 13 brands of beer than you=92ll=
be quick to realize that this is not so obvious. The problem of clustering=
data is nothing new. Many different algorithms exist (K-Means, DBSCAN, Bir=
ch, etc), all having their strength
and tradeoff. Which one to pick, largely depends on the type and the sprea=
d/density of the data. ( You can check out visualization examples of K-Mean=
s and DBSCAN clustering algorithms on the blog of Naftali Harris :
<a href=3D"https://www.naftaliharris.com/blog/visualizing-k-means-clusterin=
g/">https://www.naftaliharris.com/blog/visualizing-k-means-clustering/</a> =
and
<a href=3D"https://www.naftaliharris.com/blog/visualizing-dbscan-clustering=
/">https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/</a>)<o=
:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">We had 13 brands and our rating was based on simple ranking (flop beer 1 =
point -> top beer 13 points). With that we produced
a matrix of tasters x beer brand, having the rank in the intersection:<o:p=
></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><img border=3D"0" wid=
th=3D"607" height=3D"175" id=3D"Picture_x0020_10" src=3D"cid:image011.jpg@0=
1D32036.4FE707A0"><span lang=3D"FR" style=3D"font-size:10.0pt;font-family:&=
quot;Arial",sans-serif;color:black"><o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
"><o:p> </o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">Just so that you can see the results of the contest :<o:p></o:p></sp=
an></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><img border=3D"0" wid=
th=3D"1050" height=3D"242" id=3D"Picture_x0020_11" src=3D"cid:image015.jpg@=
01D32036.4FE707A0"><span lang=3D"FR" style=3D"font-size:10.0pt;font-family:=
"Arial",sans-serif;color:black"><o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">Krombacher took the top spot with 103 points, while Jever Fun scored at l=
owest with a total of 22 points (not so much fun=85).<br>
The columns in the chart display the totals, while the lines represent the =
maximum, minimum and average points given for each brand. The scale for the=
lines is on the right side of the chart.<o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">A few observations based on visuals only:<br>
Going from left to right the max line (yellow one) is pretty flat until we =
hit Clausthaler, then it takes a slight dip on Licher and then again on Vel=
tins and Jever Fun. That means these were the 3 brands that nobody liked to=
o much.<br>
Based on the min_rank line (blue one) we can observe, that Moenchshof has t=
he minimum variance in ratings : max and min values are really close. =
That=92s a beer that nobody dislikes and everybody could get along with.<br>
Krombacher, Clausthaler, Neumarkter Lammsbraeu and Stoertebecker, on =
the other hand, got very low scores as well as very high ones. So, are thes=
e 4 brands everybodys=92 darling or what ?. Well, the average line (re=
d one) could tell a story. Krombacher has got
a high average, meaning the high scores were in majority. Sure, it took th=
e top spot. Neumarkter Lammsbraeu still good with a quite high average rati=
ng and rank #5.<br>
Bitburger also exhibits high average and very high max and also not the low=
est minimum. It scored 2<sup>nd</sup>.<br>
The average drops at Clausthaler to a value that suggests that the tester g=
roup was divided on this: low and high values had roughly the same occ=
urrance, the distance to max and min is pretty much the same.<br>
How about the 4th in this group, Stoertebecker ? That=92s a weird one&=
nbsp;: the average is much closer to the minimum as to the max. It also sco=
red in the tail section, which means only a few people liked it and most te=
sters didn=92t.<o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">Based on these observation it is a safe bet to tell that the group can be=
categorized based on liking Krombacher + Bitburger
and disliking Jever Fun. But how about Clausthaler and Stoertebecker =
? These also seem to be characteristical (remember :Clausthaler divide=
s the group roughly equally and Stoertebecker is somebody=92s darling +=
the rest don=92t like it). So these 2 should be taken
into account as well.<o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">But hey, we are living the age of automation and machine learning so we a=
re not going to do it by hand. Luckily, Splunk supports
a range of machine learning algorithms. Clustering numerical values is rat=
her simple. I won=92t put up the splunk searches here (we can do that part =
on a live session if there is any interest). I=92m going to post only the r=
esults of the K-Means clustering. I
tried various cluster sizes, k=3D3 seems to be an ideal one. Based on this=
the tester team can be clustered as the following :<o:p></o:p></span>=
</p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span style=3D"font-s=
ize:10.0pt;font-family:"Arial",sans-serif;color:black"><img borde=
r=3D"0" width=3D"861" height=3D"229" id=3D"Picture_x0020_12" src=3D"cid:ima=
ge017.jpg@01D32036.4FE707A0"></span><span style=3D"font-size:10.0pt;font-fa=
mily:"Arial",sans-serif;color:black">
<span lang=3D"FR"><o:p></o:p></span></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">Wow=85 Sascha landed alone in a cluster group ! Surprise : the =
top brand for this group is Stoertebecker. Flop is Flensburger.<o:p></o:p><=
/span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">Another small group is the last one : only Ingo and I (Attila) were =
assigned to that one. We like Clausthaler and Neumarkter
Lammsbraeu, and apparently cannot drink Jever Fun.<o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">The largest cluster group (7 people) prefer Bitburger and Krombacher (not=
very surprised based on the ranking chart). They
also refuse to drink Flensburger, Jever Fun and Veltins.<o:p></o:p></span>=
</p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">That=92s pretty cool ! Now we know which people can get along for a =
beer. We=92ve been also assured that Sascha is an individualist=85<o:p></o:=
p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">The table above is nice and sufficient, however, we cannot see who likes =
and hates which brand. I did some further Splunk massage
on the data and rendered the results in a sankey diagram:<o:p></o:p></span=
></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><img border=3D"0" wid=
th=3D"1186" height=3D"532" id=3D"Picture_x0020_14" src=3D"cid:image020.jpg@=
01D32036.4FE707A0"><span lang=3D"FR" style=3D"font-size:10.0pt;font-family:=
"Arial",sans-serif;color:black"><o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">Here you can see the testers linked to their cluster group. Every tester =
is then linked to 2 beer brands : one is their individual
top beer and the other their absolute no-go beer. To reflect this, the bee=
r brands are eventually linked to one of the Top Beer or Flop Beer groups.<=
br>
A final observation : we could not detect absolute extremities within =
the group. That is: nobody liked the most any brand that another person dis=
liked the most. This is good. It means, we still can go out as a team =
:D :D :D.<o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">I decided to share this analysis with the entire team in the hope to wake=
interest in this field.<br>
Even if not, it won=92t hurt to know that we have really cool technology at=
our fingertips.<o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">We should repeat the experiment in the fall when the entire cybersec team=
gathers in Frankfurt. It=92s gonna be fun and I=92m sure
we are all very excited about the results!<o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">Cheers,<o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span lang=3D"FR" sty=
le=3D"font-size:10.0pt;font-family:"Arial",sans-serif;color:black=
">_______________________________________________________________________<o=
:p></o:p></span></p>
<p class=3D"MsoNormal"><!--[if gte vml 1]><v:shapetype id=3D"_x0000_t75" co=
ordsize=3D"21600,21600" o:spt=3D"75" o:preferrelative=3D"t" path=3D"m@4@5l@=
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0;margin-top:0;width:22.5pt;height:23.25pt;z-index:-251657216;visibility:vi=
sible;mso-wrap-style:square;mso-width-percent:0;mso-height-percent:0;mso-wr=
ap-distance-left:0;mso-wrap-distance-top:0;mso-wrap-distance-right:5.65pt;m=
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age;mso-height-relative:page'>
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</v:shape><![endif]--><!--[if !vml]--><img width=3D"30" height=3D"31" src=
=3D"cid:image001.gif@01D32021.3BF66750" align=3D"left" hspace=3D"4" style=
=3D"margin-left:-4px;margin-right:4px" alt=3D"Email_CBE.gif" v:shapes=3D"Im=
age_x0020_0"><!--[endif]--><b><span lang=3D"FR" style=3D"font-size:10.0pt;f=
ont-family:"Arial",sans-serif;color:black">Attila
Peter<o:p></o:p></span></b></p>
<p class=3D"MsoNormal"><span style=3D"font-size:8.0pt;font-family:"Ari=
al",sans-serif;color:black">Senior Cyber Security Architect | Cloud In=
frastructure Services<o:p></o:p></span></p>
<p class=3D"MsoNormal"><span lang=3D"FR" style=3D"font-size:8.0pt;font-fami=
ly:"Arial",sans-serif;color:black"><o:p> </o:p></span></p>
<p class=3D"MsoNormal"><span lang=3D"FR" style=3D"font-size:8.0pt;font-fami=
ly:"Arial",sans-serif;color:black">Capgemini | Frankfurt</span><s=
pan lang=3D"DE" style=3D"font-size:8.0pt;font-family:"Arial",sans=
-serif;color:black"><o:p></o:p></span></p>
<p class=3D"MsoNormal"><span lang=3D"DE" style=3D"font-size:8.0pt;font-fami=
ly:"Arial",sans-serif;color:black">Phone: +</span><span lang=
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olor:black">496995151435
</span><span lang=3D"DE" style=3D"font-size:8.0pt;font-family:"Arial&q=
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<o:p></o:p></span></p>
<p class=3D"MsoNormal"><span lang=3D"DE" style=3D"font-size:8.0pt;font-fami=
ly:"Arial",sans-serif;color:black">e-mail:
<a href=3D"mailto:attila.peter@capgemini.com">attila.peter@capgemini.com</a=
> <o:p></o:p></span></p>
<p class=3D"MsoNormal"><span style=3D"font-size:8.0pt;font-family:"Ari=
al",sans-serif;color:black"><a href=3D"http://www.de.capgemini.com/"><=
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n><span lang=3D"DE" style=3D"font-size:8.0pt;font-family:"Arial",=
sans-serif;color:black"><o:p></o:p></span></p>
<p class=3D"MsoNormal"><span lang=3D"DE" style=3D"font-size:8.0pt;font-fami=
ly:"Arial",sans-serif;color:black"><o:p> </o:p></span></p>
<p class=3D"MsoNormal"><span lang=3D"DE" style=3D"font-size:8.0pt;font-fami=
ly:"Arial",sans-serif;color:black">Mainzer Landstra=DFe 180</span=
><span lang=3D"DE" style=3D"font-size:8.0pt;font-family:"Arial",s=
ans-serif;color:black">, 60327 Frankfurt, Germany<o:p></o:p></span></p>
<p class=3D"MsoNormal" style=3D"margin-top:6.0pt"><b><span style=3D"font-si=
ze:10.0pt;font-family:"Verdana",sans-serif;color:#009BCC">People =
matter, results count.<o:p></o:p></span></b></p>
<p class=3D"MsoNormal" style=3D"margin-bottom:6.0pt"><span style=3D"font-si=
ze:10.0pt;font-family:"Arial",sans-serif;color:black">___________=
____________________________________________________________<o:p></o:p></sp=
an></p>
<p class=3D"MsoNormal"><b><span style=3D"font-size:8.0pt;font-family:"=
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<p class=3D"MsoNormal"><o:p> </o:p></p>
</div>
</div>
</blockquote>
<br>
<hr>
<font face=3D"Arial" color=3D"Gray" size=3D"1"><br>
Firma: Capgemini Outsourcing Services GmbH<br>
Gesch=E4ftsf=FChrer: John Brahim =95 Patrick Potters =95 Jean-Philippe Bol<=
br>
Amtsgericht D=FCsseldorf, HRB 58881<br>
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