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Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately our society itself. The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data. The first conference IEEE Big Data 2013 had more than 400 registered participants from 40 countries ( http://bigdataieee.org/BigData2013/) and the regular paper acceptance rate is 17.0%. The IEEE Big Data 2019 ( http://bigdataieee.org/BigData2019/ , regular paper acceptance rate: 18.7%) was held in Los Angeles, CA, Dec 9-12, 2019 with close to 1200 registered participants from 54 countries. The IEEE Big Data 2020 ( http://bigdataieee.org/BigData2020/ , regular paper acceptance rate: 15.7%) was held online, Dec 10-13, 2020 with close to 1100 registered participants from 50 countries. The IEEE Big Data 2021 ( http://bigdataieee.org/BigData2021/ , regular paper acceptance rate: 19.9%) was held online, Dec 15-18, 2021 with close to 1089 registered participants from 52 countries The 2022 IEEE International Conference on Big Data (IEEE BigData 2022) will continue the success of the previous IEEE Big Data conferences. It will provide a leading forum for disseminating the latest results in Big Data Research, Development, and Applications. We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image, etc.) big data systems and applications. The conference adopts single-blind review policy. We expect to have a very high quality and exciting technical program at Osaka this year. Example topics of interest includes but is not limited to the following: 1. Big Data Science and Foundations Novel Theoretical Models for Big Data New Computational Models for Big Data Data and Information Quality for Big Data New Data Standards 2. Big Data Infrastructure Cloud/Grid/Stream Computing for Big Data High Performance/Parallel Computing Platforms for Big Data Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment Energy-efficient Computing for Big Data Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data Software Techniques and Architectures in Cloud/Grid/Stream Computing Big Data Open Platforms New Programming Models for Big Data beyond Hadoop/MapReduce, STORM Software Systems to Support Big Data Computing 3. Big Data Management Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data Algorithms and Systems for Big Data Search Distributed, and Peer-to-peer Search Big Data Search Architectures, Scalability and Efficiency Data Acquisition, Integration, Cleaning, and Best Practices Visualization Analytics for Big Data Computational Modeling and Data Integration Large-scale Recommendation Systems and Social Media Systems Cloud/Grid/Stream Data Mining- Big Velocity Data Link and Graph Mining Semantic-based Data Mining and Data Pre-processing Mobility and Big Data Multimedia and Multi-structured Data- Big Variety Data 4. Big Data Search and Mining Social Web Search and Mining Web Search Algorithms and Systems for Big Data Search Distributed, and Peer-to-peer Search Big Data Search Architectures, Scalability and Efficiency Data Acquisition, Integration, Cleaning, and Best Practices Visualization Analytics for Big Data Computational Modeling and Data Integration Large-scale Recommendation Systems and Social Media Systems Cloud/Grid/StreamData Mining- Big Velocity Data Link and Graph Mining Semantic-based Data Mining and Data Pre-processing Mobility and Big Data Multimedia and Multi-structured Data-Big Variety Data 5. Big Data Learning and Analytics Predictive analytics on Big Data Machine learning algorithms for Big Data Deep learning for Big Data Feature representation learning for Big Data Dimension redution for Big Data Physics informed Big Data learning 6. Data Ecosystem Data ecosystem concepts, theory, structure, and process Ecosystem services and management Methods for data exchange, monetization, and pricing Trust, resilience, privacy, and security issues Privacy preserving Big Data collection/analytics Trust management in Big Data systems Ecosystem assessment, valuation, and sustainability Experimental studies of fairness, diversity, accountability, and transparency 7. Big Data Applications Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication Big Data Analytics in Small Business Enterprises (SMEs) Big Data Analytics in Government, Public Sector and Society in General Real-life Case Studies of Value Creation through Big Data Analytics Big Data as a Service Big Data Industry Standards Experiences with Big Data Project Deployments INDUSTRIAL Track The Industrial Track solicits papers describing implementations of Big Data solutions relevant to industrial settings. The focus of industry track is on papers that address the practical, applied, or pragmatic or new research challenge issues related to the use of Big Data in industry. We accept full papers (up to 10 pages) and extended abstracts (2-4 pages). Paper Submission Please submit a full-length paper (up to 10 page IEEE 2-column format, reference pages counted in the 10 pages) through the online submission system. https://wi-lab.com/cyberchair/2022/bigdata22/scripts/submit.php?subarea=BigD Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to "formatting instructions" below).</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/45222">Mono Zhong</a> in 2022-06-15</div> </div> </div>  <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-th"></i> Related Conferences</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw3" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw3_c0">CCF</th><th id="yw3_c1">CORE</th><th id="yw3_c2">QUALIS</th><th id="yw3_c3">Short</th><th id="yw3_c4">Full Name</th><th 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Applications</a></td><td></td><td>Springer</td></tr> <tr class="even"> <td><a href="/journal/136">The Journal of Strategic Information Systems</a></td><td>8.700</td><td>Elsevier</td></tr> <tr class="odd"> <td><a href="/journal/137">Statistical Analysis and Data Mining</a></td><td> </td><td>John Wiley & Sons, Ltd</td></tr> <tr class="even"> <td><a href="/journal/1081">IEEE Communications Standards Magazine</a></td><td></td><td>IEEE</td></tr> <tr class="odd"> <td><a href="/journal/10">IEEE Transactions on Information Forensics and Security</a></td><td>6.300</td><td>IEEE</td></tr> <tr class="even"> <td><a href="/journal/572">IEEE Transactions on Big Data</a></td><td>7.500</td><td>IEEE</td></tr> <tr class="odd"> <td><a href="/journal/375">Discrete Mathematics</a></td><td>0.700</td><td>Elsevier</td></tr> <tr class="even"> <td><a href="/journal/313">IEEE Signal Processing Magazine</a></td><td>9.400</td><td>IEEE</td></tr> <tr class="odd"> <td><a href="/journal/1017">New Mathematics and Natural Computation</a></td><td></td><td>World Scientific</td></tr> <tr class="even"> <td><a href="/journal/147">IEEE MultiMedia</a></td><td>2.300</td><td>IEEE</td></tr> </tbody> </table><div class="keys" style="display:none" title="/conference/1523"><span>431</span><span>136</span><span>137</span><span>1081</span><span>10</span><span>572</span><span>375</span><span>313</span><span>1017</span><span>147</span></div> </div></div> </div> </div> <div class="portlet" id="yw8"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-eye-open"></i> Recommendation</div> </div> <div class="portlet-content"> <div align="center"> <script async src="//pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"></script>  <ins class="adsbygoogle" style="display:block" data-ad-client="ca-pub-6743617430731451" data-ad-slot="1870704128" data-ad-format="autorelaxed"></ins> <script> (adsbygoogle = window.adsbygoogle || []).push({}); </script> </div> </div> </div> </div> <div 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