Final paper

1
Systems
Engineering
Ecosystem for
Modeling,
Analysis, and
Visualization
Sam Berndt
Jan Van Bruggen
Abstract
Data visualization is a rapidly growing
industry as it allows users to quickly
understand and see patterns in otherwise
meaningless data. The Systems Engineering
Ecosystem for Modeling, Analysis, and
Visualization (SEEMAV) takes advantage
of these benefits to provide systems
engineers and stakeholders a dashboard to
view detailed analyses across many areas.
Through SEEMAV, a user is able to create
API endpoints to scrape documents for
relevant information and then tie this data
into a visualization on the dashboard to
answer highly targeted questions. Currently,
SEEMAV requires systems engineers to
write their own parsing script in order to
hook into one of the visualizations. In the
future, I recommend a highly parameterized
algorithm so that the user is able to input
exact requirements as to how to extract the
information required from the uploaded
documents. I also recommend that we
implement a library of visualization cards to
streamline the process of adding highly
sought after data visualizations.
Text
Systems engineers1
are the ones who take
concepts and turn them into creations. To do
this, it is required that they have a complete
understanding of the system. However, it
also involves making sure everyone else has
a clear understanding. What this currently
entails is the manual processing of
information, followed up by the generation
of systems diagrams using tools such as
Tom Sawyer2
or MagicDraw3
to give a
visual aid to the rest of the team.
The Systems Engineering Ecosystem for
Modeling, Analysis, and Visualization
(SEEAMV) is a web-based tool and
dashboard which allows systems engineers
and stakeholders the ability to quickly view
data visualizations which were previously
generated by systems engineers to answer
highly targeted questions. SEEMAV is built
on the JPL Analytics Cloud, Docker4
and
AWS5
using RESTful Web API’s6
and
Python7
for data analysis and D38
,
Swagger9
, React10
and Javascript11
for front
end design and visualizations.
SEEMAV is still being developed, with
many new features to enhance its
capabilities still to come. Currently,
SEEMAV is only made for requirement
documents and requires its code to be
updated each time a new document is to be
scraped, or a new visualization is to be
added to the dashboard. A user has the
ability to go to their dashboard and view
previously setup visualizations which are
live scraped via a RESTful Web API each
time they load the screen. The process that
happens is, a user goes to the dashboard, a
previously setup visualization queries an
API endpoint for data, which then calls a
Python function specifically written for that
endpoint which then returns a JSON object
containing the requested data. The data is
then processed in such a way to answer a
targeted question and is then tied into a
visualization.

2
In the future, I highly recommend
developing SEEMAV’s data-to-visualization
pipeline to be put 100% on the end user. By
giving a user the scraping tools, the ability
to make and enter in their own scraping
algorithm, and a user interface to tie it all
together would greatly increase the
productivity of both systems engineers and
end users. With this, a library of
visualization cards with the ability to upload
code for a custom visualization would
further remove a systems engineer from the
process, as well as give end users the
opportunity to choose a to-standard
visualization to represent their data. The
visualizations available also could be
expanded to add in a 3D model viewer,
physics simulation cards, and the like.
Adding ports to gather and represent data
from other programs such as Tom Sawyer
and MagicDraw would also be a worthy
improvement, as it wouldn’t require the
revisiting of old documents to have the same
data put into SEEMAV.
Web technologies are becoming a disrupter
in the systems engineering community.
Having the ability to put a lot of the work
once needed to be done by hand and giving
it to a computer which has the ability to
share it with the appropriate people on
demand will soon have a substantial effect
on how systems engineers do their work. It
can lead to reduced time to completion of
visualizations, increased communication
between teams through shared big data
analysis, and eventually enforce a standard
of how things are done and data is shared.
SEEMAV has become an excellent example
of how web technologies can help shape
systems engineering. What once was a data
analysis program that needed all of its
dependencies installed on a computer before
it could even run is now a simple web URL
away. Because these API’s are all
connected, it makes it easy to compare the
information they return. This led to
discovery of flaws in some documents, and
patterns in others which led to a deeper
understanding that may not have come
without the use of SEEMAV. SEEMAV
also brought out the need to make the
process of data scraping automated to make
the process that much faster. Through this,
SEEMAV has helped pave the way for
future programs and capabilities to hit the
ground running and help change the
paradigm of systems engineering.
Methods
My area of SEEMAV is mostly tied into the
parsing of data and creation of API
endpoints. Written entirely in Python and
with thousands of lines of code, I developed
12 unique API calls relating 22 requirement
documents together through the use of 21
requirement parsers and 11 traceability
relationship parsers.
In a particular use case, a user could go a
web URL on our domain such as
“/requirement/<document_title>”, which
will then call a function to gather
requirements based off of the document title.
The following function will then pull the file
off of Amazon’s S3 server and convert it to
a Bytes.IO type, which Python is able to
iterate through. Due to the general format of
most requirements documents, where each
requirement takes up multiple paragraphs,
the document is split into tuples of certain
paragraph lengths based off of the document
format between two given strings. A regular
expression is given in each algorithm to
decipher when a requirement ID has been hit
on the first index of the tuple. On a positive
result, the algorithm will append said tuple
onto a list. In a MS Word requirement
parser, each item in the tuple will then be
processed to ensure data integrity and put
into a list of dictionaries with each having
four categories: ID, title, text, and rationale.
This list of dictionaries is then returned to

3
the parent function which is then returned as
JSON using a flask method.
As one can see, there is a noticeable pattern
for scraping requirement documents; the
same can also be said about traceability
documents.
This realization led me to laying the
foundation for a generic scraping algorithm
which has the potential to allow end users of
SEEMAV to upload their own documents,
use a simple web UI to describe the
particular information they are trying to get
out of documents, and from there have a
callable API automatically generated. Given
an appropriate amount of development time,
this would hasten the process for both
systems engineers and end users by a large
factor by eliminating the need to have a
systems engineer write a new algorithm for
each document. The way the custom API
works is there could be two buttons: upload
new document, and create new API. Upload
new document would simply upload a
document to the S3 server dedicated to the
users account. Create new API would open a
web interface asking the user to insert
requirement scraping parameters. These
parameters would then be put into an API
call, which would store them as a JSON
object on a file on the server. To access the
API, a user could simply go to the web URL
associated to the API, such as
“/custom/myapi.” This would then scrape
the JSON document on the S3 server for the
API called “myapi”, and call the generic
scraping algorithm with the user defined
parameters saved under that name which
then returns a JSON object with the
requested information inside.
Languages and Tools
JPL Analytics Cloud
Docker4
AWS5
RESTful Web API’s6
Python7
D38
Swagger9
React10
Javascript11
Figures
Figure 1
A endpoint call to the generic excel
document scraper
Figure 2
The generic excel document scraper
Figure 3

4
A function called in the generic excel
scraper which takes a list of tuples of
Booleans to determine if a cell matches
certain criteria before appending it to the
returnable object
Figure 4
A pseudo example of returned JSON from
requirement endpoint
Acknowledgements
I’d like to start out by thanking all of JPL,
Caltech and its staff. My excellent team led
by Kim Simpson. Dan Isla for providing the
technical assistance and tools SEEMAV
needed to get started. Tony Valderamma for
getting my foot in the door. Tyler Ryan for
technical and educational assistance. Lastly,
I’d like to thank my mentor for the summer,
Jan Van Bruggen. Thank you for the
opportunity to be here, for the baseline of
my work, for the time you’ve given to me
through explanations and teaching, for
introducing me to new methodologies and
programs, and for being a good friend.
References
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