Big Data and Deep Analytics Applied to the Common Tactical Air Picture (CTAP) and Combat Identification (CID)
Ying Zhao, Tony Kendall, Bonnie Johnson
2016
Abstract
Accurate combat identification (CID) enables warfighters to locate and identify critical airborne objects as friendly, hostile or neutral with high precision. The current CID processes include processing and analysing data from a vast network of sensors, platforms, and decision makers. CID plays an important role in generating the Common Tactical Air Picture (CTAP) which provides situational awareness to air warfare decision-makers. The Big “CID” Data and complexity of the problem pose challenges as well as opportunities. In this paper, we discuss CTAP and CID challenges and some Big Data and Deep Analytics solutions to address these challenges. We present a use case using a unique deep learning method, Lexical Link Analysis (LLA), which is able to associate heterogeneous data sources for object recognition and anomaly detection, both of which are critical for CTAP and CID applications.
References
- Blei, D., Ng, A. and Jordan, M. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022. Retrieved from http://jmlr.csail.mit.edu/ papers/volume3/blei03a/blei03a.pdf.
- BigDAWG, 2016. A Demonstration of the BigDAWG polystore system. http://livinglab.mit.edu/wp-content/ uploads/2016/01/bigdawg-polystore-system.pdf.
- Center for Computational Analysis of Social and Organizational Systems (CASOS) 2009. AutoMap: extract, analyze and represent relational data from texts. Retrieved from http://www.casos.cs.cmu.edu.
- DeepMind, 2016. https://deepmind.com/
- Dumais, S. T., Furnas, G. W., Landauer, T. K. and Deerwester, S. 1988. Using latent semantic analysis to improve information retrieval. In Proceedings of CHI'88: Conference on Human Factors in Computing, 281-285.
- Freeman, L.C. 1979. Centrality in social networks I: conceptual clarification. Social Networks, 1: 215-239.
- Flenner, A. 2015. Representation learning through topic models. NFCS NAWCWD, China Lake, in the 2015 National Fire Control Symposium.
- GraphBLAS, 2016. Graph algorithms for basic linear algebra subprograms (BLAS). http://istc-bigdata.org/ GraphBlas/
- Hofmann, T. 1999. Probabilistic latent semantic analysis. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden.
- Kepner J., Chaidez, J., Gadepally, V., Jansen, H. 2014. Associative Arrays: Unified Mathematics for Spreadsheets, Databases, Matrices, and Graphs. http://db.csail.mit.edu/nedbday15/pdf/Paper7.pdf.
- LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324. Retrieved from http://yann.lecun.com/exdb/publis/pdf/lecun- 01a.pdf.
- Miller, G. A. 1995. WordNet: a lexical database for English. Communications of the ACM, 38(11).
- Mahout, 2016. http://mahout.apache.org/
- NBD, 2014. Navy Big Data. http://defensesystems.com/ articles/2014/06/24/navy-onr-big-data-ecosystem.aspx.
- Olshausen, B. and Field, D. 1996. Emergence of simplecell receptive field properties by learning a sparse code for natural images. Nature.
- Raina, R., Battle, A., Lee, H., Packer, B., and Ng, A.Y. 2007. Self-taught learning: transfer learning from unlabeled data. In ICML.
- Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G., Dean, J., Le, Q. V. and Ng, A.Y. 2012. Building high-level features using large scale unsupervised learning. Proceedings of the 29th International Conference on Machine Learning (ICML-12). Retrieved from http://arxiv.org/pdf/1112.6209v5.pdf.
- Soar, 2016. http://soar.eecs.umich.edu/
- Spark, 2016. http://spark.apache.org/
- SiFT, 2016. http://www.vlfeat.org/
- Zhou, C., Zhao, Y., and Kotak, C. 2009. The Collaborative Learning agent (CLA) in Trident Warrior 08 exercise. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR), Madeira, Portugal.
- Zhao, Y., Gallup, S.P. and MacKinnon, D.J. 2011. System self-awareness and related methods for improving the use and understanding of data within DoD. Software Quality Professional, 13(4): 19-31. http://asq.org/ pub/sqp/
- Zhao, Y., Mackinnon, D. J., Gallup, S. P. 2015. Big data and deep learning for understanding DoD data. Journal of Defense Software Engineering, Special Issue: Data Mining and Metrics.
- Zhao, Y., Mackinnon, D. J., Gallup, S. P. 2015. Big data and deep learning for understanding DoD data. Journal of Defense Software Engineering, Special Issue: Data Mining and Metrics.
- Zhao, Y., Zhou, C. 2016. System Self-Awareness Towards Deep Learning and Discovering High-Value Information. The 7th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, New York City, USA 20 - 22 October 2016.
- Zhao, Y. and Zhou, C. 2014. System and method for knowledge pattern search from networked agents. US patent 8,903,756. https://www.google.com/patents/ US8903756.
Paper Citation
in Harvard Style
Zhao Y., Kendall T. and Johnson B. (2016). Big Data and Deep Analytics Applied to the Common Tactical Air Picture (CTAP) and Combat Identification (CID) . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 443-449. DOI: 10.5220/0006086904430449
in Bibtex Style
@conference{kdir16,
author={Ying Zhao and Tony Kendall and Bonnie Johnson},
title={Big Data and Deep Analytics Applied to the Common Tactical Air Picture (CTAP) and Combat Identification (CID)},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={443-449},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006086904430449},
isbn={978-989-758-203-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Big Data and Deep Analytics Applied to the Common Tactical Air Picture (CTAP) and Combat Identification (CID)
SN - 978-989-758-203-5
AU - Zhao Y.
AU - Kendall T.
AU - Johnson B.
PY - 2016
SP - 443
EP - 449
DO - 10.5220/0006086904430449