Edit Event: ALLDATA 2021

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LLDATA 2021 is colocated with the following events as part of NexComm 2021 Congress INVITATION: ================= Please consider to contribute to and/or forward to the appropriate groups the following opportunity to submit and publish original scientific results to: - ALLDATA 2021, The Seventh International Conference on Big Data, Small Data, Linked Data and Open Data ALLDATA 2021 is scheduled to be April 18 - 22, 2021 in Porto, Portugal under the NexComm 2021 umbrella. The submission deadline is January 19, 2021. Authors of selected papers will be invited to submit extended article versions to one of the IARIA Journals: https://www.iariajournals.org ================= ============== ALLDATA 2021 | Call for Papers =============== CALL FOR PAPERS, TUTORIALS, PANELS ALLDATA 2021, The Seventh International Conference on Big Data, Small Data, Linked Data and Open Data General page: https://www.iaria.org/conferences2021/ALLDATA21.html Submission page: https://www.iaria.org/conferences2021/SubmitALLDATA21.html Event schedule: April 18 - 22, 2021 Contributions: - 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: January 19, 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 Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas.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, in terms of Regular papers, Posters, Work in progress, Technical/marketing/business presentations, Demos, Tutorials, and Panels. Before submission, please check and comply with the editorial rules: https://www.iaria.org/editorialrules.html ALLDATA 2021 Topics (for topics and submission details: see CfP on the site) Call for Papers: https://www.iaria.org/conferences2021/CfPALLDATA21.html ============================================================ ALLDATA 2021 Tracks (topics and submission details: see CfP on the site) Challenges in processing Big Data and applications Data classification: small/big/huge, volume, velocity, veridicity, value, etc; Data properties: syntax, semantics, sensitivity, similarity, scarcity, spacial/temporal, completeness, accuracy, compactness, etc.; Data processing: mining, searching, feature extraction, clustering, aggregating, rating, filtering, etc.; Data relationships: linked data, open data, linked open data, etc. Exploiting big/linked data: upgrading legacy open data, integrating probabilist models, spam detection, datasets for noise corrections, predicting reliability, pattern mining, linking heterogeneous dataset collections, exploring type-specific topic profiles of datasets, efficient large-scale ontology matching etc.; Applications: event-based linked data, large scale multi-dimensional network analysis, error detection of atmospheric data, exploring urban data in smart cities, studying health fatalities, estimating the energy demand at real-time in cellular networks, multilingual word sense disambiguation, creating open source tool for semantically enriching data, etc. 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; 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. Approaches for Data/Big Data processing using Machine Learning 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.) Big Data Big data foundations; Big data architectures; Big data semantics, interoperability, search and mining; Big data transformations, processing and storage; Big Data management lifecycle, Big data simulation, visualization, modeling tools, and algorithms; Reasoning on Big data; Big data analytics for prediction; Deep Analytics; Big data and cloud technologies; Big data and Internet of Things; High performance computing on Big data; Scalable access to Big Data; Big data quality and provenance, Big data persistence and preservation; Big data protection, integrity, privacy, and pseudonymisation mechanisms; Big data software (libraries, toolkits, etc.); Big Data visualisation and user experience mechanisms; Big data understanding (knowledge discovery, learning, consumer intelligence); Unknown in large Data Graphs; Applications of Big data (geospatial/environment, energy, media, mobility, health, financial, social, public sector, retail, etc.); Business-driven Big data; Big Data Business Models; Big data ecosystems; Big data innovation spaces; Big Data skills development; Policy, regulation and standardization in Big data; Societal impacts of Big data Small Data Social networking small data; Relationship between small data and big data; Statistics on Small data; Handling Small data sets; Predictive modeling methods for Small data sets; Small data sets versus Big Data sets; Small and incomplete data sets; Normality in Small data sets; Confidence intervals of small data sets; Causal discovery from Small data sets; Deep Web and Small data sets; Small datasets for benchmarking and testing; Validation and verification of regression in small data sets; Small data toolkits; Data summarization Linked Data RDF and Linked data; Deploying Linked data; Linked data and Big data; Linked data and Small data; Evolving the Web into a global data space via Linked data; Practical semantic Web via Linked data; Structured dynamics and Linked data sets; Quantifying the connectivity of a semantic Linked data; Query languages for Linked data; Access control and security for Linked data; Anomaly detection via Linked data; Semantics for Linked data; Enterprise internal data 'silos' and Linked data; Traditional knowledge base and Linked data; Knowledge management applications and Linked data; Linked data publication; Visualization of Linked data; Linked data query builders; Linked data quality Open Data Open data structures and algorithms; Designing for Open data; Open data and Linked Open data; Open data government initiatives; Big Open data; Small Open data; Challenges in using Open data (maps, genomes, chemical compounds, medical data and practice, bioscience and biodiversity); Linked open data and Clouds; Private and public Open data; Culture for Open data or Open government data; Data access, analysis and manipulation of Open data; Open addressing and Open data; Specification languages for Open data; Legal aspects for Open data; Open Data publication methods and technologies, Open Data toolkits; Open Data catalogues, Applications using Open Data; Economic, environmental, and social value of Open Data; Open Data licensing; Open Data Business models; Data marketplaces ------------------------ ALLDATA 2021 Committee: https://www.iaria.org/conferences2021/ComALLDATA21.html Publicity Chairs Lorena Parra, Universitat Politecnica de Valencia, Spain Jose Luis García, Universitat Politecnica de Valencia, Spain -----------------------

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