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DTSTART;VALUE=DATE:20210418
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SUMMARY:ALLDATA 2021
DESCRIPTION:The Seventh International Conference on Big Data\, Small Data\, Linked Data and Open Data\nALLDATA 2021 conference tracks: \nChallenges in processing Big Data and applications\nData 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. \nAdvanced topics in Deep/Machine learning\nDistributed 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. \nApproaches for Data/Big Data processing using Machine Learning\nMachine 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.) \nBig Data\nBig 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 \nSmall Data\nSocial 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 \nLinked Data\nRDF 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 \nOpen Data\nOpen 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
URL:https://ngi.eu/event/alldata-2021/
LOCATION:On-line & on-site (Porto) event
CATEGORIES:Events
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