Conference Information

ALLDATA 2022: International Conference on Big Data, Small Data, Linked Data and Open Data

Please Login to view website of conference
Free account: view official websites, track deadlines, and get email reminders.
Register Login
Embed deadline badge
ALLDATA
Get this via API
Search and ranking lists need no credentials at all; full detail for this page needs a free API key. See the developer guide.
Submission Date:
2022-01-24
Notification Date:
2022-02-17
Conference Date:
2022-04-24
Location:
Barcelona, Spain
Years:
8
Viewed: 9189   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

43.9 / 100
Ranked #3,261 of 5,682 conferences · Top 58%

#189 of 337 in Data Mining & Databases

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
59
Community attention (10%)
8
Public record completeness (15%)
25

Inputs used: Editions on record: 8 · Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-17

Call For Papers

ALLDATA 2022 (International Conference on Big Data, Small Data, Linked Data and Open Data) is an academic conference held in Barcelona, Spain on 2022-04-24. The paper submission deadline is 2022-01-24. Acceptance notifications are sent on 2022-02-17.

The volume and the complexity of available information overwhelm human and computing resources. Several approaches, technologies and tools are dealing with different types of data when searching, mining, learning and managing existing and increasingly growing information. From understanding Small data, the academia and industry recently embraced Big data, Linked data, and Open data. Each of these concepts carries specific foundations, algorithms and techniques, and is suitable and successful for different kinds of application. While approaching each concept from a silo point of view allows a better understanding (and potential optimization), no application or service can be developed without considering all data types mentioned above. ALLDATA 2022, The International Conference on Big Data, Small Data, Linked Data and Open Data, follows a series of events bridging the concepts and the communities devoted to each of data categories for a better understanding of data semantics and their use, by taking advantage from the development of Semantic Web, Deep Web, Internet, non-SQL and SQL structures, progresses in data processing, and the new tendency for acceptance of open environments. We solicit both academic, research, and industrial contributions. We welcome technical papers presenting research and practical results, position papers addressing the pros and cons of specific proposals, such as those being discussed in the standard fora or in industry consortia, survey papers addressing the key problems and solutions on any of the above topics short papers on work in progress, and panel proposals. Industrial presentations are not subject to the format and content constraints of regular submissions. We expect short and long presentations that express industrial position and status. Tutorials on specific related topics and panels on challenging areas are encouraged. 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 topics and submission formats are open to both research and industry contributions. ALLDATA 2022 conference tracks: 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
Last updated by Dou Sun on

Related Conferences

Related Journals

CCFFull NameImpact FactorPublisherISSN
Journal of Big Data6.4Springer2196-1115
Big Data Research4.2Elsevier2214-5796
Scientific Data6.9Springer2052-4463
Journal on Data SemanticsSpringer1861-2032
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312

Comments 0

No comments yet.

Please Login to post a comment