ALLDATA 2021 : The Seventh International Conference on Big Data, Small Data, Linked Data and Open Data

in Conferences   Posted on November 23, 2020 

Conference Information

Submission Deadline Friday 05 Feb 2021
Tuesday 19 Jan 2021
Proceedings indexed by :
Conference Dates Apr 18, 2021 - Apr 22, 2021
Conference Address Porto, Portugal
Conference & Submission Link
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Call for Papers



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:


============== ALLDATA 2021 | Call for Papers ===============


ALLDATA 2021, The Seventh International Conference on Big Data, Small

Data, Linked Data and Open Data

General page:

Submission page:

Event schedule: April 18 – 22, 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]

– presentations: slide only [slide-deck posted at]

– demos: two pages [posted at]

Submission deadline: January 19, 2021

Extended versions of selected papers will be published in IARIA


Print proceedings will be available via Curran Associates, Inc.:

Articles will be archived in the free access ThinkMind Digital Library:

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are invited to submit complete unpublished papers, which are not under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:

ALLDATA 2021 Topics (for topics and submission details: see CfP on the site)

Call for Papers:


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:

Publicity Chairs

Lorena Parra, Universitat Politecnica de Valencia, Spain

Jose Luis García, Universitat Politecnica de Valencia, Spain


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