@inproceedings{deed1c906e374e1f8d848766df1769ab,
title = "Multi-Domain Integration and Correlation Engine",
abstract = "As Machine Learning becomes more prominent in the military, we are faced with a different take on the old problem of how to collect data relevant to some military mission need. We can now embrace the paradigm of too much data where previously we needed to focus on data reduction because humans can only process a finite amount of information. Commanders, analysts, and intelligence officers are often tasked with understanding the current situation in a mission area to create a common operating picture in order to complete their mission objectives. Data pertaining to missions can often be scraped from multiple domains, including patrol reports, newswire, and RF sensors, image sensors, and various other sensor types in the field. In this paper, we describe a system called the Multi-Domain Integration and Correlation Engine (MD-ICE), which ingests data which ingests data from two domains: textual open source information (newswire and social media)and sensor network information, and processes it using tools from various machine learning research areas. MD-ICE manipulates the resulting data into a machine readable unified format to allow for labelling and inference of inter-domain correlations. The goal of MD-ICE is to utilize these information domains to better understand situational context, where open source information provides semantic context (i.e. what type of event, who is involved, etc...)and the sensor network information provides the fine-grain detail (how many people involved, exact area of the event, etc...). This understanding of situational context in turn can, with further research, help commanders reach their mission objectives faster through better situational understanding and prediction of future needs.",
keywords = "Entity extraction, Integration, Machine learning, Multi domain battle, Multimodal, Natural language processing, Network monitoring, Open source, Phrase mining, Sensor networks, Signal processing, Social media, Social sensing, Source selection",
author = "William Dron and Andrew Hunter and Ali Sydney and Siddharth Pal and John Hancock and Lisa Scott and Tarek Abdelzaher and Jiawei Han and Sibel Adali and Benjamin Horne",
note = "Funding Information: Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053. 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 Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. This document does not contain technology or technical data controlled under either the U.S. International Traffic in Arms Regulations or the U.S. Export Administration Regulations. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Military Communications Conference, MILCOM 2018 ; Conference date: 29-10-2018 Through 31-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/MILCOM.2018.8599706",
language = "English (US)",
series = "Proceedings - IEEE Military Communications Conference MILCOM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1074--1079",
booktitle = "2018 IEEE Military Communications Conference, MILCOM 2018",
address = "United States",
}