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Authors: Ying Zhao 1 ; Ralucca Gera 1 ; Quinn Halpin 2 and Jesse Zhou 3

Affiliations: 1 Naval Postgraduate School, Monterey, CA and U.S.A. ; 2 Cornell University, Ithaca, NY and U.S.A. ; 3 JZ Tech Consulting, San Francisco, CA and U.S.A.

Keyword(s): Visualization, Data-Driven Documents (D3), Network Analysis, Lexical Link Analysis (LLA), Smart Data, Automatic Dependent Surveillance-Broadcast, ADS-B.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems ; Visual Data Mining and Data Visualization

Abstract: Military applications require big distributed, disparate, multi-sourced and real-time data that have extremely high rates, high volumes, and diverse types. Warfighters need deep models including big data analytics, network analysis, link analysis, deep learning, machine learning, and artificial intelligence to transform big data into smart data. Explainable deep models will play a more essential role for future warfighters to understand, interpret, and therefore appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners when facing complex threats. In this paper, we show how visualization is used in two typical deep models with two use cases: network analysis, which addresses how to display and present big data both in the exploratory and discovery process, and link analysis, which addresses how to display and present the smart data generated from these processes. By using various visualization tools such as D3, Tableau, and lexica l link analysis, we derive useful information from discovering big networks to discovering big data patterns and anomalies. These visualizations become intepretable and explainable deep models that can be readily used by warfighters and decision makers to achieve the sense making and decision making superiority. (More)

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Paper citation in several formats:
Zhao, Y.; Gera, R.; Halpin, Q. and Zhou, J. (2019). Visualization Techniques for Network Analysis and Link Analysis Algorithms. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR; ISBN 978-989-758-382-7; ISSN 2184-3228, SciTePress, pages 561-568. DOI: 10.5220/0008377805610568

@conference{kdir19,
author={Ying Zhao. and Ralucca Gera. and Quinn Halpin. and Jesse Zhou.},
title={Visualization Techniques for Network Analysis and Link Analysis Algorithms},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR},
year={2019},
pages={561-568},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008377805610568},
isbn={978-989-758-382-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR
TI - Visualization Techniques for Network Analysis and Link Analysis Algorithms
SN - 978-989-758-382-7
IS - 2184-3228
AU - Zhao, Y.
AU - Gera, R.
AU - Halpin, Q.
AU - Zhou, J.
PY - 2019
SP - 561
EP - 568
DO - 10.5220/0008377805610568
PB - SciTePress