DATA ANALYTICS 2021 : The Tenth International Conference on Data Analytics
Call For Papers
CfP: DATA ANALYTICS 2021 || October 3 - 7, 2021 - Barcelona, Spain
Please consider to contribute to and/or forward to the appropriate groups the following opportunity to submit and publish original scientific results to:
- DATA ANALYTICS 2021, The Ninth International Conference on Data Analytics
DATA ANALYTICS 2021 is scheduled to be October 3 - 7, 2021 in Barcelona, Spain under the NexTech 2021 umbrella.
The submission deadline is July 5, 2021.
Authors of selected papers will be invited to submit extended article versions to one of the IARIA Journals: https://www.iariajournals.org
All events will be held in a hybrid mode: on site, online, prerecorded videos, voiced presentation slides, pdf slides.
============== DATA ANALYTICS 2021 | Call for Papers ===============
CALL FOR PAPERS, TUTORIALS, PANELS
DATA ANALYTICS 2021, The Ninth International Conference on Data Analytics
General page: https://www.iaria.org/conferences2021/DATAANALYTICS21.html
Submission page: https://www.iaria.org/conferences2021/SubmitDATAANALYTICS21.html
Event schedule: October 3 - 7, 2021
- regular papers [in the proceedings, digital library]
- short papers (work in progress) [in the proceedings, digital library]
- ideas: two pages [in the proceedings, digital library]
- extended abstracts: two pages [in the proceedings, digital library]
- posters: two pages [in the proceedings, digital library]
- posters: slide only [slide-deck posted at www.iaria.org]
- presentations: slide only [slide-deck posted at www.iaria.org]
- demos: two pages [posted at www.iaria.org]
Submission deadline: July 5, 2021
Extended versions of selected papers will be published in IARIA Journals: https://www.iariajournals.org
Print proceedings will be available via Curran Associates, Inc.: https://www.proceedings.com/9769.html
Articles will be archived in the free access ThinkMind Digital Library: https://www.thinkmind.org
The topics suggested by the conference can be discussed in term of concepts, state of the art, research, standards, implementations, running experiments, applications, and industrial case studies. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas.
All tracks are open to both research and industry contributions.
Before submission, please check and comply with the editorial rules: https://www.iaria.org/editorialrules.html
DATA ANALYTICS 2021 Topics (for topics and submission details: see CfP on the site)
Call for Papers: https://www.iaria.org/conferences2021/CfPDATAANALYTICS21.html
DATA ANALYTICS 2021 Tracks (topics and submission details: see CfP on the site)
Fundamentals for data analytics
Tools, frameworks and mechanisms for data analytics; Open API for data analytics; In-database analytics; Pre-built analytics (pattern, time-series, clustering, graph, statistical analysis, etc.); Analytics visualization; Multi-modal support for data analytics; Google/FaceBook/Twitter/etc. analytics; High-performance data analytics
Advanced topics in Deep/Machine learning
Distributed and parallel learning algorithms; Image and video coding; Deep learning and Internet of Things; Deep learning and Big data; Data preparation, feature selection, and feature extraction; Error resilient transmission of multimedia data; 3D video coding and analysis; Depth map applications; Machine learning programming models and abstractions; Programming languages for machine learning; Visualization of data, models, and predictions; Human behavioral predictions; Hardware-efficient machine learning methods; Model training, inference, and serving; Trust and security for machine learning applications; Testing, debugging, and monitoring of machine learning applications; Machine learning for systems.
Specific Machine Learning approaches and Data Processing
Machine learning models (supervised, unsupervised, reinforcement, constrained, etc.); Generative modeling (Gaussian, HMM, GAN, Bayesian networks, autoencoders, etc.); Explainable AI (feature importance, LIME, SHAP, FACT, etc.); Bayesian learning models; Prediction uncertainty (approximation learning, similarity); Training of models (hyperparameter optimization, regularization, optimizers); Active learning (partially labels datasets, faulty labels, semi-supervised); Applications of machine learning (recommender systems, NLP, computer vision, etc.); Data in machine learning (no data, small data, big data, graph data, time series, sparse data, etc.)
Mechanisms and features
Scalable data analytics; Big data analytics; Deep data analytics; Mass data analytics; Storing, dropping and filtering data; Relevant/redundant/obsolete data analytics; Volume vs. semantics analytics; Nomad analytics; Predictive analytics; Trust in data analytics; Legal issues analytics; Failure on data analytics
Architectures for generic sentiment analysis systems; Sentiment analysis techniques on social media; Document-level analysis; Sentence-level analysis; Aspect-based analysis; Comparative-sentiment analysis; Sentiment lexicon acquisition; Optimizing sentiment analysis algorithms; Applications of sentiment analysis.
Statistical applications; Simulation applications; Crawling web services; Cross-database analytics; Forecast analytics; Financial risk management; ROI analytics
Business analytics; Malware analytics; Cyber-threats analytics; Mining user logs; Reputation analytics; User choice analytics; Branding analytics; Utility proximity-search analytics; Survey-based online asset analytics; Online employment analytics; Geology analytics; Global climate analytics; Remote learning analytics; Homecare analytics; Population growth and migration analytics; Food-borne illness outbreaks analytics
Foundational models for Big Data; Big Data Analytics and Metrics; Big Data processing and management; Big Data search and mining; Big Data platforms; Big Data persistence and preservation; Big Data and social networks; Big Data economics
Knowledge Discovery from Huge Data; Computational Intelligence for Huge Data; Linked Huge Data; Security Intelligence with Huge Data
DATA ANALYTICS 2021 Committee: https://www.iaria.org/conferences2021/ComDATAANALYTICS21.html
Lorena Parra, Universitat Politecnica de Valencia, Spain
José Miguel Jiménez, Universitat Politecnica de Valencia, Spain