00000nam 2200000zi 4500 9.881807 CaOODSP 20221107170134 m o d f cr cn||||||||| 191107t20182018nsca ob f000 0 eng d CaOODSP eng rda CaOODSP D68-11/082-2018E-PDF Dividino, Renata, author. Semantic integration of real-time heterogeneous data streams for ocean-related decision making / Renata Dividino, Amilcar Soares, Stan Matwin, Anthony W. Isenor, Sean Webb, Matthew Brousseau. Dartmouth, Nova Scotia : Defence Research and Development Canada = Recherche et développement pour la défense Canada, 2018. ©2018 1 online resource (12 pages, 2 unnumbered pages) : illustrations. text txt rdacontent computer c rdamedia online resource cr rdacarrier External literature (N) ; DRDC-RDDC-2018-N082 "Can unclassified." "IST-160 Specialists' Meeting on Big Data and Artificial Intelligence for Military Decision Making NATO Science and Technology Board, Bordeaux, France." "STO-MP-IST-160. Pagination info: S1-3 - 1–12." "June 2018." Includes bibliographical references (pages 14-15). "Information deluge is a continual issue in today's military environment, creating situations where data is sometimes underutilized or in more extreme cases, not utilized, for the decision-making process. In part, this is due to the continuous volume of incoming data that presently engulf the ashore and afloat operational community. However, better exploitation of these data streams can be realized through information science techniques that focus on the semantics of the incoming stream, to discover information-based alerts that generate knowledge that is only obtainable when considering the totality of the streams. In this paper, we present an agile data architecture for real-time data representation, integration, and querying over a multitude of data streams. These streams, which originate from heterogeneous and spatially distributed sensors from different IoT infrastructures and the public Web, are processed in real-time through the application of Semantic Web Technologies. The approach improves knowledge interoperability, and we apply the framework to the maritime vessel traffic domain to discover real-time traffic alerts by querying and reasoning across the numerous streams. The paper and the provided video demonstrate that the use of standards-based semantic technologies is an effective tool for the maritime big data integration and fusion tasks"--Abstract, page 1. Defence R&D Canada. Atlantic Research Centre. External literature (N) (Defence R&D Canada) DRDC-RDDC-2018-N082. (CaOODSP)9.858173 PDF 1.11 MB https://publications.gc.ca/collections/collection_2019/rddc-drdc/D68-11-082-2018-eng.pdf