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</o:shapelayout></xml><![endif]--></head><body lang=EL link=blue vlink=purple style='word-wrap:break-word'><div class=WordSection1><p><span lang=EN-GB>Workshop on Big Nature data analysis: methods and applications (BigNDA 2023)<br><br><b>Where</b>: Taormina, Messina, Italy<br><b>When</b>: 4th December 2023<br><b>Co-located</b>: the 10th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT2023)<br><br><a href="https://fcrlab.unime.it/news/bignda2023">https://fcrlab.unime.it/news/bignda2023</a><br>==========================================================<br>Nature Data Analysis involves the analysis of multimodal and heterogeneous extreme data sources including data captured by autonomous devices (e.g., drones) and smart sensors at the edge, satellite images, topographical data, official meteorological data, predictions or warnings published in the Web, and geosocial media data (including text, image and videos). Besides being heterogeneous, nature data might be unstructured, sparse/missing, lacking and it is difficult to be simulated or visualized. On top of this complexity, the analysis of natural phenomena needs to be performed very fast, to trigger relevant responses from the local authorities, as they are frequently related to natural disasters, such as forest fires or floods.<o:p></o:p></span></p><p style='margin-bottom:0in;text-align:justify'><span lang=EN-GB>Papers are solicited in all areas of algorithms, systems, platform and architecture of Big Data for nature and environmental data, including, but not restricted to:<o:p></o:p></span></p><p style='margin:0in;text-align:justify'><span lang=EN-GB>- Cloud/Edge/IoT architectures for Big data analysis<br>- Learning methods for Big Nature data analysis<br>- Satellite data analysis<br>- Big geo-social media data analysis<br>- Fast and precise meterological data analysis<br>- Phenomenon prediction and modeling<br>- Visualization methods and systems for Natural phenomena evolution<br>- Simulation tools for natural data creation<br>- Application on Natural disaster management<br><br>>> PAPER SUBMISSION<br>Authors are invited to submit papers electronically through the following link:<br><a href="https://cmt3.research.microsoft.com/UCCBDCAT2023">https://cmt3.research.microsoft.com/UCCBDCAT2023</a> track. <o:p></o:p></span></p><p><span lang=EN-GB>BigNDA 2023 - Workshop. <o:p></o:p></span></p><p><span lang=EN-GB>Submitted manuscripts must represent original unpublished research that is not currently under review for any other conference or journal. Manuscripts are submitted in PDF format and may not exceed six (6) ACM-formatted double-column pages, including figures, tables, and references. All manuscripts will be reviewed and judged on correctness, originality, technical strength, rigor in analysis, quality of results, quality of presentation, and interest and relevance to the conference attendees. Your submission is subject to a determination that you are not under any sanctions by ACM. Accepted papers will later be converted into single-column format through the ACM TAPS process, and therefore need to use the new templates that are single-column by default. Switch them to double-column for authoring your paper. This is possible in both the Word and the LaTeX templates.<br><br>At least one author of each paper must be registered for the conference in order for the paper to be published in the proceedings. The conference proceedings will be published by the ACM and made available online via the IEEE Xplore Digital Library and ACM Digital Library.<o:p></o:p></span></p><p><span lang=EN-GB>>> IMPORTANT DATES<br>Papers submission: September 15th, 2023<br>Notification of Acceptance: October 14th, 2023<br>Camera-ready papers due: October 21th, 2023<br>Workshop date: December 4th, 2023<br>Each deadline expires at 23:59:59 UTC-12 (AoE)<br><br>>> ORGANIZING COMMITTEE<br>Lorenzo Carnevale, University of Messina, Italy<br>Vasileios Mygdalis, Aristotle University of Thessaloniki, Greece<br>Ioannis Pitas, Aristotle University of Thessaloniki, Greece<br>Massimo Villari, University of Messina, Italy<o:p></o:p></span></p><div><p class=MsoNormal><span lang=IT>-- <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>The organizing committee,<o:p></o:p></span></p><p class=MsoNormal><span lang=IT>Prof. Lorenzo Carnevale<sup>a</sup>, Dr. Vasileios Mygdalis<sup>b</sup>, Prof. Ioannis Pitas<sup>b</sup>, Prof. Massimo Villari<sup>a<o:p></o:p></sup></span></p><p class=MsoNormal><span lang=IT><o:p> </o:p></span></p><p class=MsoNormal><sup><span lang=EN-US>a </span></sup><span lang=EN-US>Future Computing Research Laboratory<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>University of Messina, Messina, Italy<br></span><span lang=EN-GB><a href="https://fcrlab.unime.it/"><span lang=EN-US>fcrlab.unime.it</span></a><o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><sup><span lang=EN-US>b </span></sup><span lang=EN-US>Computer Vision Machine Learning (AIIA.CVML)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Artificial Intelligence & Information Analysis (AIIA)<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-GB>Aristotle University of Thessaloniki, Thessaloniki, Greece<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-GB><a href="http://www.aiia.csd.auth.gr"><span lang=IT>www.aiia.csd.auth.gr</span></a></span><span lang=IT><o:p></o:p></span></p><p class=MsoNormal><span lang=IT><o:p> </o:p></span></p><p class=MsoNormal><span lang=IT><o:p> </o:p></span></p><p class=MsoNormal><span lang=IT><o:p> </o:p></span></p><p class=MsoNormal><span lang=IT><o:p> </o:p></span></p></div></div></body></html>