IEEE/ACM/ASA DSAA’2021
Porto, Portugal, 06-09 October 2021
Website: dsaa2021.dcc.fc.up.pt/
Submission website: cmt3.research.microsoft.com/DSAA2021
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Important Dates Research, Application and Tutorials: submission deadline: 23 May 2021 Research and Application notification: 25 Jul. 2021 Research and Application camera ready due: 8 Aug. 2021
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Highlights of DSAA’2021 *A strong interdisciplinary research program spanning the areas of data science, including statistics, machine learning, computing, and analytics. *Strong Journal, Research, and Applications tracks with reproducible and open results. *Industry track session with lightning results highlighting research advances and industry’s best practices. *Special sessions on the foundations and emerging areas for data science. *Tutorials on hot topics, and hands-on tutorials *Special panel on the trends and controversies of data science and analytics. *Strong cross-domain interactions among researchers and industry and government policymakers and practitioners. *Industry and research exhibits. *Financially sponsored by IEEE CIS, proceedings by IEEE Xplore and EI indexed. *Technically supported by ACM SIGKDD and ASA.
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Precaution of COVID-19 Due to ongoing uncertainty about future travel due to COVID-19, DSAA’2021 commits to allowing video presentations of accepted papers by authors who are unable to attend due to COVID-19 travel restrictions.
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About DSAA’2021 The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA’2021) will provide a premier forum that brings together researchers, industry and government practitioners, as well as developers and users in statistics, computing science, and intelligence science for the exchange of the latest theoretical developments in Data Science and Analytics and the best practice for a wide range of applications. The conference invites submission of papers describing innovative research on all aspects of data science and advanced analytics as well as application-oriented papers that make significant, original, and reproducible contributions to improving the practice of data science and analytics in real-world scenarios. DSAA’2021 is a multi-track conference consisting of a Journal Track, Research Track, Application Track, and Industry Track. DSAA’2021 will also feature a peer-reviewed Poster session whose purpose is to showcase recent and early-stage research developments on topics that are of interest to students and industry/government practitioners in data science and analytics.
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Research Track The Research Track solicits the latest, original, and significant contributions related to foundations and theoretical developments of Data Science and Advanced Analytics. Topics of interest include but are not limited to: *Data Science and Advanced Analytics Methods *Mathematics, Statistics for data science and analytics *Understanding data characteristics and complexities *Deep Learning *Data quality and misinformation *Bayesian learning, optimization, inference, and regularization *Infrastructures, and systems *Evaluation, explanation, visualization, and presentation *Fairness, accountability, and trustworthy Machine Learning *Survey and review The paper submission deadline for the Research Track is May 23, 2021 (no separate abstract deadline) and the acceptance notifications will be sent out by July 25, 2021.
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Applications Track The Application track solicits original, impactful, and actionable application results of Data Science and Advanced Analytics across various disciplines and domains, including business, government, healthcare and medical science, physical sciences, and social sciences. Submissions address a real problem on real-life data that is reproducible ideally through a public git repository, providing inspiring results to policymakers, end-users or practitioners or highlighting new practical challenges for researchers. Topics of interest include but are not limited to: *Domain-driven data science and analytics practice *Real-world applications and case studies *Operationalizable infrastructures, platforms, and tools *Deployment, management, and decision-making *System and software demonstrations *Social and economic impact modelling *Ethics, social issues, privacy, trust, and bias
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Industrial Track The IEEE DSAA’2021 Industry track aims to highlight the challenges of putting together production-grade software solutions to different business verticals based on data science. Our goal is to promote the debate, the exchange of ideas and/or collaborations on how to add business value to society through data science.
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Submission Submissions to the main conference including Research Track & Application Track are available from CMT: cmt3.research.microsoft.com/DSAA2021.
Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt

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IEEE/ACM/ASA DSAA’2021
CALL FOR SPECIAL SESSION PROPOSALS

dsaa2021.dcc.fc.up.pt/calls/special-sessions
======================================================================

Important Dates
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* Special Session Proposal Due: 21 February 2021
* Special Session Proposal Notification: 28 February 2021
* Paper Submission Deadline: as for the main conference 23 May 2021
* Special Session Paper Notification: as for the main conference 25 July 2021

About DSAA Special Sessions ——————————————————————————
DSAA Special Sessions are an important part of the main conference program. They bring together researchers, industry experts, practitioners, and potential users who are interested in cultivating specialized and important aspects of data science and analytics.

DSAA Special Sessions are intended to promote EMERGING data science research areas that are not well established and covered in the main conference tracks, while featuring much higher quality, integrity and impact of presentations than classic workshops typically hosted in all major conferences. The same evaluation criteria and quality level apply as for the main conference, but the papers must adhere to the area of the special session they are submitted to, and the reviewers are experts in that area.

Many real-world challenges call for interdisciplinary solutions and a dialog of cultures. In DSAA 2021, we particularly encourage proposals for special sessions that promote such a dialog, e.g. on statistics and data mining, pattern recognition and statistics, data mining and simulation.

We welcome proposals that promote a more intensive interaction between different communities and proposals that promote cooperation to solve interdisciplinary problems. Proposals on special sessions on how interdisciplinary data science can make the world stronger against disease, outbreaks are strongly encouraged.

Thus, special sessions might focus on:

a) topics on the border of data science research area,
b) advanced topics within the data science research area, or
c) specific application areas for data science.

Special Session Proposal Submission and Review
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Proposals for hosting special sessions at DSAA 2021 are welcome. The proposals must address:

(1) Title
(2) Aims and scope
(3) Topics of interest
(4) Relevance to the DSAA main conference tracks and topics
(5) Organizers
(6) Past special sessions or relevant experiences or track records
(7) Potential committee members
(8) Potential invited speakers

For each organizer in (5), provide name, affiliation, country, email and a short biographical sketch, describing relevant qualifications and experience; identify at least one organizer as the contact person.

For (6), list any special session or relevant events (e.g., workshops) the organizers have organized in recent years in DSAA or other major conferences; for each, list the year, the conference, number of submissions, number of papers accepted, number of participants, etc.

For (7), give a list of qualified committee members who would be invited.

For (8), please provide the names of one or two authoritative speakers that could open the special session, and that can deliver a comprehensive overview of the topic of interest.

Special session proposals will be reviewed based on the above criteria and quality of the proposals as well as their relationship to the main conference topics. Preference may be given to timely topics that are critical for data science and analytics, inspire highly interactive discussions, and showcase the impact of data science and analytics.

Proposers are encouraged to give an estimation of the number of submissions they expect.

Submission of a special session:
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cmt3.research.microsoft.com/DSAA2021
Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt

====================================================================== IEEE/ACM/ASA DSAA’2021 CALL FOR SPECIAL SESSION PROPOSALS
dsaa2021.dcc.fc.up.pt/calls/special-sessions ======================================================================
Important Dates —————————————————————————— * Special Session Proposal Due:30 November 2020 * Special Session Proposal Notification:15 December 2020 * Special Session Paper Submission Deadline:23 May 2021 * Special Session Paper Notification: 25 July 2021
The DSAA conference series —————————————————————————— * Strong Research track and Applications track. * Student Poster and Industry Poster sessions highlighting students’ research advances and industry’s best practices. * One-day Industry Day with Data Science School for business * Special sessions on the foundations and emerging areas for data science. * Special panel on the trends and controversies of data science and analytics * A strong interdisciplinary research program spanning the areas of data science, including statistics, machine learning, computing, and analytics. * Strong cross-domain interactions among researchers and industry and government policy-makers and practitioners. * Industry and research exhibits. * Technically sponsored and supported by IEEE CIS, ACM SIGKDD and ASA * EI indexed proceedings, hosted at IEEEXplore
More on this year’s DSAA:https://dsaa2021.dcc.fc.up.pt
About DSAA Special Sessions —————————————————————————— DSAA Special Sessions are an important part of the main conference program. They bring together researchers, industry experts, practitioners and potential users who are interested in cultivating specialized and important aspects of data science and analytics.
DSAA Special Sessions are intended to promote EMERGING data science research areas that are not well established and covered in the main conference tracks, while featuring much higher quality, integrity and impact of presentations than classic workshops typically hosted in all major conferences. The same evaluation criteria and quality level apply as for the main conference, but the papers must adhere to the area of the special session they are submitted to, and the reviewers are experts in that area.
Many real world challenges call for interdisciplinary solutions and a dialog of cultures. In DSAA 2021, we particularly encourage proposals for special sessions that promote such a dialog, e.g. on statistics and data mining, pattern recognition and statistics, data mining and simulation.
We welcome proposals that promote a more intensive interaction between different communities and proposals that promote cooperation to solve interdisciplinary problems. Proposals on special sessions on how interdisciplinary data science can make the world stronger against disease outbreaks are strongly encouraged.
Thus, special sessions might focus on:
a) topics on the border of data science research area, b) advanced topics within the data science research area, or c) specific application areas for data science.
Special Session Proposal Submission and Review —————————————————————————— Proposals for hosting special sessions at DSAA 2021 are welcome. The proposals must address:
(1) Title (2) Aims and scope (3) Topics of interest (4) Relevance to the DSAA main conference tracks and topics (5) Organizers (6) Past special sessions or relevant experiences or track records (7) Potential committee members (8) Potential invited speakers
For each organizer in (5), provide name, affiliation, country, email and a short biographical sketch, describing relevant qualifications and experience; identify at least one organizer as the contact person.
For (6), list any special session or relevant events (e.g., workshops) the organizers have organized in recent years in DSAA or other major conferences; for each, list the year, the conference, number of submissions, number of papers accepted, number of participants, etc.
For (7), give a list of qualified committee members who would be invited.
For (8), please provide the names of one or two authoritative speakers that could open the special session, and that can deliver a comprehensive overview of the topic of interest.
Special session proposals will be reviewed based on the above criteria and quality of the proposals as well as their relationship to the main conference topics. Preference may be given to timely topics that are critical for data science and analytics, inspire highly interactive discussions, and showcase the impact of data science and analytics.
Proposers are encouraged to give an estimation of the number of submissions they expect.
Special Session Organization, Paper Submission and Review —————————————————————————— Once a special session proposal has been accepted, its organizers should widely publicize the session for calling for papers.
Papers for a special sessionshould be submitted to the special session track instead of the main conference, but using the same submission system.
Special session papers strictly follow the same specifications, requirements and policies as the main conference submissions in terms of the paper submission deadline, notification deadline, paper formatting and length, and important policies.
Reviewing of the submissions in each special session is coordinated by the special session organizers, and is fully aligned to the main conference evaluation process. In particular:
* All papers submitted to special sessions will be double-blind reviewed * Organizers of each special session will recommend program committee members(PCMs)to the DSAA Program Chairs and Special SessionsChairs. * Approved PCMs will be invited by the conference Special Session Chairs into the submission system for each Special Session; a reviewer is allowed toserve as PCM in more than one Special Session. * Papers will be assigned to appropriatePCMs by the Special Session organizers for review. * Special Session organizers will make recommendations of acceptance/rejection for papers in their sessions, which will be double-checked by the conference Special Session Chairs.
To guarantee uniform quality control for all special sessions and to be consistent with the main conference, the final decisions of special session paper acceptance/rejection are made by the DSAA Program Chairs.
Proceedings, Indexing and Special Issues —————————————————————————— All accepted full-length special session papers will be published by IEEE in the DSAA main conference proceedings under its Special Session scheme. All papers will be submitted for inclusion in the IEEEXplore Digital Library. The conference proceedings will be submitted for EI indexing through INSPEC by IEEE.
Organizers of Special Sessionsmay additionally arrange for special issues to further publish the extended journal versions of the papers. Several past special sessions have published special issues with the International Journal of Data Science and Analytics (JDSA, Springer).
General Inquiries and Submission —————————————————————————— Special session proposals should be submitted to the DSAA 2021 Special Session chairs atsessions-dsaa2021@dsaa.co
Specific enquiries about a Special Session should be submitted to the session organizers, who are advised to set up a joint email address, once their proposal is accepted.
Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt

Special Issue on Foundations of Data Science – Machine Learning Journal
Data science is currently a very active topic with an extensive scope, both in terms of theory and applications. Machine Learning is one of its core foundational pillars. Simultaneously, Data Science applications provide important challenges that can often be addressed only with innovative Machine Learning algorithms and methodologies. This special issue focuses on the latest developments in Machine Learning foundations of data science, as well as on the synergy between data science and machine learning. We welcome new developments in statistics, mathematics and computing that are relevant for data science from a machine learning perspective, including foundations, systems, innovative applications and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for data science. Theoretically well-founded contributions and their real-world applications in laying new foundations for machine learning and data science are welcome.
This special issue solicits the attention of a broad research audience. Since it brings together a variety of foundational issues and real-world best practices, it is also relevant to practitioners and engineers interested in machine learning and data science.
Accepted papers will be presented at the IEEE DSAA conference in Porto, October 2021.
———————————————————
Topics of Interest
———————————————————
We welcome original research papers on all aspects of data science in relation to machine learning, including the following topics:
*Machine Learning Foundations of Data Science
Auto-ML
Fusion of information from disparate sources
Feature engineering, Feature embedding and data preprocessing
Learning from network data
Learning from data with domain knowledge
Reinforcement learning
Evaluation of Data Science systems
Risk analysis
Causality, learning causal models
Multiple inputs and outputs: multi-instance, multi-label, multi-target
Semi-supervised and weakly supervised learning
Data streaming and online learning
Deep Learning
*Emerging Applications
Autonomous systems
Analysis of Evolving Social Networks
Embedding methods for Graph Mining
Online Recommender Systems
Augmented Reality, Computer Vision
Real-Time Anomaly, Failure, image manipulation and fake detection
*Human Centric Data Science
Privacy preserving, Ethics, Transparency
Fairness, Explainability, and Algorithm Bias
Accountability and responsibility
Reproducibility, replicability and retractability
Green Data Sciences
*Infrastructures
IoT data analytics and Big Data
Large-scale processing and distributed/parallel computing;
Cloud computing
*Data Science for the Next Digital Frontier
in: Telecommunications and 5G
Retail,
Green Transportation
Finance, Blockchains, Cryptocurrencies
Manufacturing, Predictive Maintenance, Industry 4.0
Energy, Smart Grids, Renewable energies
Climate change and sustainable environment
Contributions must contain new, unpublished, original and fundamental work relating to the Machine Learning journal’s mission. All submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution will be crucial.
———————————————————
Submission Instructions
——————————————————–
Submit manuscripts to: MACH.edmgr.com. Select “SI: Foundations of Data Science” as the article type. Papers must be prepared in accordance with the Journal guidelines: www.springer.com/journal/10994
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.
All papers will be reviewed following standard reviewing procedures for the Journal.
———————————————————
Key Dates
———————————————————
Continuous submission/review process
Cutoff dates: 30 September, 30 December and 1st March
Last paper submission deadline: 1 March 2021
Paper acceptance: 1 June 2021
Camera-ready: 15 June 2021
———————————————————
Guest Editors
———————————————————
Alípio Jorge, University of Porto,
João Gama, University of Porto
Salvador García, University of Granada

Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt

Special Issue on Foundations of Data Science – Machine Learning Journal
Data science is currently a very active topic with an extensive scope, both in terms of theory and applications. Machine Learning is one of its core foundational pillars. Simultaneously, Data Science applications provide important challenges that can often be addressed only with innovative Machine Learning algorithms and methodologies. This special issue focuses on the latest developments in Machine Learning foundations of data science, as well as on the synergy between data science and machine learning. We welcome new developments in statistics, mathematics and computing that are relevant for data science from a machine learning perspective, including foundations, systems, innovative applications and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for data science. Theoretically well-founded contributions and their real-world applications in laying new foundations for machine learning and data science are welcome.
This special issue solicits the attention of a broad research audience. Since it brings together a variety of foundational issues and real-world best practices, it is also relevant to practitioners and engineers interested in machine learning and data science.
Accepted papers will be presented at the IEEE DSAA conference in Porto, October 2021.
———————————————————
Topics of Interest
———————————————————
We welcome original research papers on all aspects of data science in relation to machine learning, including the following topics:
*Machine Learning Foundations of Data Science
Auto-ML
Fusion of information from disparate sources
Feature engineering, Feature embedding and data preprocessing
Learning from network data
Learning from data with domain knowledge
Reinforcement learning
Evaluation of Data Science systems
Risk analysis
Causality, learning causal models
Multiple inputs and outputs: multi-instance, multi-label, multi-target
Semi-supervised and weakly supervised learning
Data streaming and online learning
Deep Learning
*Emerging Applications
Autonomous systems
Analysis of Evolving Social Networks
Embedding methods for Graph Mining
Online Recommender Systems
Augmented Reality, Computer Vision
Real-Time Anomaly, Failure, image manipulation and fake detection
*Human Centric Data Science
Privacy preserving, Ethics, Transparency
Fairness, Explainability, and Algorithm Bias
Accountability and responsibility
Reproducibility, replicability and retractability
Green Data Sciences
*Infrastructures
IoT data analytics and Big Data
Large-scale processing and distributed/parallel computing;
Cloud computing
*Data Science for the Next Digital Frontier
in: Telecommunications and 5G
Retail,
Green Transportation
Finance, Blockchains, Cryptocurrencies
Manufacturing, Predictive Maintenance, Industry 4.0
Energy, Smart Grids, Renewable energies
Climate change and sustainable environment
Contributions must contain new, unpublished, original and fundamental work relating to the Machine Learning journal’s mission. All submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution will be crucial.
———————————————————
Submission Instructions
——————————————————–
Submit manuscripts to: MACH.edmgr.com. Select “SI: Foundations of Data Science” as the article type. Papers must be prepared in accordance with the Journal guidelines: www.springer.com/journal/10994
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.
All papers will be reviewed following standard reviewing procedures for the Journal.
———————————————————
Key Dates
———————————————————
Continuous submission/review process
Cutoff dates: 30 September, 30 December and 1st March
Last paper submission deadline: 1 March 2021
Paper acceptance: 1 June 2021
Camera-ready: 15 June 2021
———————————————————
Guest Editors
———————————————————
Alípio Jorge, University of Porto,
João Gama, University of Porto
Salvador García, University of Granada

Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt

Special Issue on Foundations of Data Science – Machine Learning Journal
Data science is currently a very active topic with an extensive scope, both in terms of theory and applications. Machine Learning is one of its core foundational pillars. Simultaneously, Data Science applications provide important challenges that can often be addressed only with innovative Machine Learning algorithms and methodologies. This special issue focuses on the latest developments in Machine Learning foundations of data science, as well as on the synergy between data science and machine learning. We welcome new developments in statistics, mathematics and computing that are relevant for data science from a machine learning perspective, including foundations, systems, innovative applications and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for data science. Theoretically well-founded contributions and their real-world applications in laying new foundations for machine learning and data science are welcome.
This special issue solicits the attention of a broad research audience. Since it brings together a variety of foundational issues and real-world best practices, it is also relevant to practitioners and engineers interested in machine learning and data science.
Accepted papers will be presented at the IEEE DSAA conference in Porto, October 2021.
———————————————————
Topics of Interest
———————————————————
We welcome original research papers on all aspects of data science in relation to machine learning, including the following topics:
*Machine Learning Foundations of Data Science
Auto-ML
Fusion of information from disparate sources
Feature engineering, Feature embedding and data preprocessing
Learning from network data
Learning from data with domain knowledge
Reinforcement learning
Evaluation of Data Science systems
Risk analysis
Causality, learning causal models
Multiple inputs and outputs: multi-instance, multi-label, multi-target
Semi-supervised and weakly supervised learning
Data streaming and online learning
Deep Learning
*Emerging Applications
Autonomous systems
Analysis of Evolving Social Networks
Embedding methods for Graph Mining
Online Recommender Systems
Augmented Reality, Computer Vision
Real-Time Anomaly, Failure, image manipulation and fake detection
*Human Centric Data Science
Privacy preserving, Ethics, Transparency
Fairness, Explainability, and Algorithm Bias
Accountability and responsibility
Reproducibility, replicability and retractability
Green Data Sciences
*Infrastructures
IoT data analytics and Big Data
Large-scale processing and distributed/parallel computing;
Cloud computing
*Data Science for the Next Digital Frontier
in: Telecommunications and 5G
Retail,
Green Transportation
Finance, Blockchains, Cryptocurrencies
Manufacturing, Predictive Maintenance, Industry 4.0
Energy, Smart Grids, Renewable energies
Climate change and sustainable environment
Contributions must contain new, unpublished, original and fundamental work relating to the Machine Learning journal’s mission. All submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution will be crucial.
———————————————————
Submission Instructions
——————————————————–
Submit manuscripts to: MACH.edmgr.com. Select “SI: Foundations of Data Science” as the article type. Papers must be prepared in accordance with the Journal guidelines: www.springer.com/journal/10994
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.
All papers will be reviewed following standard reviewing procedures for the Journal.
———————————————————
Key Dates
———————————————————
Continuous submission/review process
Cutoff dates: 30 September, 30 December and 1st March
Last paper submission deadline: 1 March 2021
Paper acceptance: 1 June 2021
Camera-ready: 15 June 2021
———————————————————
Guest Editors
———————————————————
Alípio Jorge, University of Porto,
João Gama, University of Porto
Salvador García, University of Granada

Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt