Accession Number:

AD1000842

Title:

Information Mining Technologies to Enable Discovery of Actionable Intelligence to Facilitate Maritime Situational Awareness: I-MINE

Descriptive Note:

Technical Report

Corporate Author:

OODA Technologies Inc Montreal, QC Canada

Report Date:

2013-01-01

Pagination or Media Count:

86.0

Abstract:

Human operators trying to establish individual or collective maritime situational awareness often find themselves overloaded by huge amount of information obtained from multiple and possibly dissimilar sources. This kind of situation has also been identified within Maritime Forces Atlantic MARLANT and its supporting activities in the Regional Joint Operations Center RJOC East andWest and the Marine Security Operations Centres MSOCs as its current information infrastructure e.g. Global Position Warehouse GPW faces a challenge of how to extractdiscover valuable knowledge from the available large volumes of maritime traffic information usually stored in large databases. Applying data mining techniques to large sets of maritime traffic data to extract knowledge will facilitate vessel traffic analysis and management for maritime analysts as well as improved decision-making in the maritime domain. Since maritime traffic data differs from the data commonly mined in business domains, the selection of appropriate data mining tools is crucial for meaningful knowledge extraction.This report provides an extensive review and explores potential use of available informationdata mining technologies by maritime analysts to enable discovery of actionable intelligenceto facilitate maritime situational awareness. The focus is on open source data mining tools, while the data is restricted to spatio-temporal maritime traffic data such as the Automated Identification System AIS data. It includes assessments of selected data mining tools using the scenarios of potential interest to the maritime environment coveringboth user and administrator perspectives. The report also presents an introductory theoretical background on data mining with special attention to spatial and spatio-temporal data mining as well as an overview of organizations and institutions that work on data mining with data sets similar to maritime traffic data.

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE