![](bg8.png)
given moment and animate the planes movement from
minute to minute. It can also display all planes that
take off at the selected time and show where that plane
goes in the scope of all the files loaded into the server.
This visualization is limited to the size of the data set.
To play 10 minutes of flights, ten 5KB files need to
be loaded in and looped through to show all of the
data. Therefore, as more time is displayed, latency in-
creases. Current features of this visualization include
zooming- in and out and panning to different sections
of the world as shown in Figure 12.
4 CONCLUSION
Visualizations help users gain insight from big data.
We showed in this paper various visualizations in or-
der to help users understanding big data as well as to
extend the users’ understanding of smart data through
deep models such as link analysis and network anal-
ysis. The visualizations implemented for LLA and
network analysis vary in complexity and offer some
breadth to the viewers. By using D3, Tableau, and
MATLAB visualizations, we derived useful informa-
tion from discovering big networks to discovering big
data patterns and anomalies.
What are the challenges of future visualization?
Assessing data visualizations includes using heuris-
tic evaluation and user studies. Future work for these
visualizations includes designing and developing vi-
sualization types associated with the nature of deep
models, data types and business problems, and mak-
ing the visualization easy to use for human analysts
both in the pre- and post- analyses of big data. This
should be an ongoing effort to improve understand-
able, intepretable and explainable deep models that
can be readily used by warfighters and decision mak-
ers to achieve superiority.
ACKNOWLEDGEMENTS
Thanks to the Naval Research Program at the Naval
Postgraduate School, the Office of Naval Research
(ONR), and the SBIR contract N00014-07-M-0071
for the research of lexical link analysis and collabora-
tive learning agents at Quantum Intelligence, Inc. The
views and conclusions contained in this document are
those of the authors and should not be interpreted as
representing the official policies, either expressed or
implied of the U.S. Government.
REFERENCES
ADSBexchange.com, L. (2017). Ads-b exchange.
Alonso, L., Crawford, B., Gera, R., House, J., Knuth, T.,
M
´
endez-Berm
´
udez, J., and Miller, R. (2017). Identi-
fying network structure similarity using spectral graph
theory. Applied Network Science.
Bosack, M. (2017). D3 data-driven documents.
CED3 (2018). Center for educational design, development,
and distribution.
Chen, S., Debnath, J., Gera, R., Greunke, B., Sharpe, N.,
and Warnke, S. (2017). Discovering community struc-
ture using network sampling. In 32nd ISCA Interna-
tional Conference on Computers and Their Applica-
tions. Springer.
Crawford, B., Gera, R., House, J., Knuth, T., and Miller,
R. (2016). Graph structure similarity using spectral
graph theory. In International Workshop on Com-
plex Networks and their Applications, pages 209–221.
Springer.
Davis, B., Gera, R., Lazzaro, G., Lim, B. Y., and Rye, E. C.
(2016). The marginal benefit of monitor placement on
networks. In Complex Networks VII, pages 93–104.
Springer.
Gera, R., Juliano, N. R., and Schmitt, K. R. (2017). Opti-
mizing network discovery with clever walks. In Pro-
ceedings of the 2017 IEEE/ACM International Con-
ference on Advances in Social Networks Analysis and
Mining 2017, pages 1217–1224. ACM.
Wijegunawardana, P., Ojha, V., Gera, R., and Soundarajan,
S. (2017). Seeing red: Locating people of interest in
networks. In Workshop on Complex Networks Com-
pleNet, pages 141–150. Springer.
Zhao, Y., Kendall, W. A., and Young, B. W. (2016). Big
data and deep analytics applied to the common tac-
tical air picture (ctap) and combat identification(cid).
In Proceedings of the 8th International Joint Confer-
ence on Knowledge Discovery, Knowledge Engineer-
ing and Knowledge Management, volume 1 of IC3K
2016, pages 443–449.
Zhao, Y., MacKinnon, D., and Gallup, S. (2015). Big data
and deep learning for understanding dod data. In Jour-
nal of Defense Software Engineering, Special Issue:
Data Mining and Metrics.
KDIR 2019 - 11th International Conference on Knowledge Discovery and Information Retrieval
568