Special Sessions
Special Session 1:
11th IEEE Special session on Privacy and Security of Big Data (PSBD 2024)Special Session 2:
9th IEEE Special Session on Machine Learning on Big Data (MLBD 2024)Special Session 3:
7th Special Session on HealthCare Data in IEEE Big Data 2024Special Session 4:
2nd Special Session on Understanding New Markets by Data Science, Social Science, and EconomicsSpecial Session 5:
Synergizing Mobility Data for Creating and Discovering Valuable PlacesSpecial Session 6:
Special Session on Dataspaces and DFFT (Data Free Flow with Trust)Special Session 7:
10th Special Session on Intelligent Data MiningSpecial Session 8:
Special Session on Federated Learning on Big DataSpecial Session 9:
Special Session on Social Cognitive Computing in Digital Education and LearningSpecial Session 10:
Data-Driven Designation and Implementation of Automated Guided VehiclesSpecial Session 11:
10th Special Session on Information Granulation in Data Science and Scalable ComputingSpecial Session 1:
11th IEEE Special session on Privacy and Security of Big Data (PSBD 2024)
December 15-18, 2024, Washington DC, USA
Best Papers of PSBD 2024 will be Invited for Extended Submission to a Top-Quality
Journal
Aim and Scope
The 11th IEEE Special Session “Privacy and Security of Big Data” (PSBD 2024) of the
2024 IEEE International Conference on Big Data (IEEE BigData 2024)
follows the great success of ten previous editions co-located with the IEEE BigData and
ACM CIKM conference series and focuses the attention on privacy and security research issues in
the context of Big Data,
a vibrant and challenging research context which is playing a leading role in the Database
research community. Indeed,
while Big Data is gaining the attention from the research community, also driven by some
relevant technological
innovations (like Clouds) as well as novel paradigms (like social networks), the issues of
privacy and security of Big
Data represent a fundamental problem in this research context, due to the fact Big Data are
typically published online
for supporting knowledge management and fruition processes and, in addition to this, such data
are usually handled by
multiple owners, with possible secure multi-part computation issues. Some of the hot topics in
the context privacy and
security of Big Data include: (i) privacy and security of Big Data integration and exchange;
(ii) privacy and security
of Big Data in data-intensive Cloud computing; (iii) system architectures in support of privacy
and security of Big
Data, e.g., GPUs: (iv) privacy and security issues of Big Data querying and analysis.
The PSBD 2024 special session focuses on all the research aspects of privacy and
security of Big Data. Among these, an
unrestricted list is the following one:
The 11th IEEE Special Session “Privacy and Security of Big Data” (PSBD 2024) of the
2024 IEEE International Conference on Big Data (IEEE BigData 2024)
will be held in Washington DC, USA, during December 15-18, 2024, and it aims to
synergistically connect the research community and industry practitioners. It provides an
international forum where
scientific domain experts and Privacy and Security researchers, practitioners and developers can
share their findings in
theoretical foundations, current methodologies, and practical experiences on Privacy and
Security of Big Data. PSBD 2024
will provide a stimulating environment to encourage discussion, fellowship, and exchange of
ideas in all aspects of
research related to Privacy and Security of Big Data. This includes both original research
contributions and insights
from practical system design, implementation and evaluation, along with new research directions
and emerging application
domains in the target area. An expected outcome from PSBD 2024 is the identification of new
problems in the main topics,
and moves to achieve consolidated solutions to already-known problems. Other goals are to help
in creating a focused
community of scientists who create and drive interest in the area of Privacy and Security of Big
Data, and additionally
to continue on the success of the event across future years.
Special Session Location
Washington DC, USA
Submission Guidelines and Instructions
Contributions are invited from prospective authors with interests in the indicated session
topics and related areas of
application. All contributions should be high quality, original and not published elsewhere or
submitted for publication
during the review period.
Submitted papers should strictly follow the
IEEE official
template. Maximum paper length allowed is:
Submitted papers will be thoroughly reviewed by members of the Workshop Program Committee for quality, correctness, originality and relevance. All accepted papers must be presented by one of the authors, who must register.
Papers must be submitted via the CyberChair System by selecting the track “Special Session on Privacy and Security of Big Data”.
Paper Publication
Accepted papers will appear in the official IEEE Big Data 2024 main conference proceedings, published by IEEE.
Authors of selected papers from the workshop will be invited to submit an extended version of their paper to a special issue of a high-quality international journal.
Important Dates:
Paper submission: September 27, 2024
Notification of acceptance: October 27, 2024
Camera-ready paper due: November 17, 2024
Special Session: December 15-18, 2024
Program Committee Chair
Alfredo Cuzzocrea, University of Calabria, Italy
Program Committee
Mst Shapna Akter, University of Oklahoma, USA
Maurizio Atzori, University of Cagliari, Italy
Roberto Baldoni, University of Rome “Sapienza”, Italy
Islam Belmerabet, University of Calabria, Italy
Ismail Benlaredj, University of Calabria, Italy
Elisa Bertino, CERIAS and Purdue University, USA
Giuseppe Cascavilla, Eindhoven University, The Netherlands
Pietro Colombo, University of Insubria, Italy
Alfredo Cuzzocrea, University of Calabria, Italy
Rinku Dewri, University of Denver, USA
Josep Domingo-Ferrer, Universitat Rovira i Virgili, Spain
Yucheng Dong, Sichuan University, China
Carmine Gallo, University of Calabria, Italy
Abderraouf Hafsaoui, University of Calabria, Italy
Mojtaba Hajian, University of Calabria, Italy
Michela Iezzi, Banca d'Italia Research Center, Italy
Murat Kantarcioglu, University of Texas at Dallas, USA
Carson K. Leung, University of Manitoba, Canada
Mohamed Maouche, University of Lyon & INSA Lyon, France
Anifat M. Olawoyin, University of Manitoba, Canada
Rajesh Pasupuleti, University of Miami, USA
Md Abdur Rahman, University of West Florida, USA
Md Mostafizur Rahman, University of West Florida, USA
Antonino Rullo, ICAR-CNR, Italy
Hossain Shahriar, University of West Florida, USA
Annalisa Socievole, ICAR-CNR, Italy
Thorsten Strufe, Technische Universitat Darmstadt, Germany
Traian Marius Truta, Northern Kentucky University, USA
Xiaokui Xiao, Nanyang Technological University, Singapore
For more information and any inquire, please contact Alfredo Cuzzocrea.
Special Session 2:
9th IEEE Special Session on Machine Learning on Big Data (MLBD 2024)
December 15-18, 2024, Washington DC, USA
Best Papers of MLBD 2024 will be Invited for Extended Submission to a Top-Quality
Journal
Aim and Scope
The 9th IEEE Special Session “Machine Learning on Big Data” (MLBD 2024) of the
2024 IEEE International Conference on Big Data (IEEE BigData 2024)
follows the great success of eight previous editions co-located with the IEEE BigData and IEEE
ICMLA conference series and focuses on machine learning models, techniques and algorithms
related to Big Data, a vibrant
and challenging research context playing a leading role in the Machine Learning and Data Mining
research communities.
Big data is gaining attention from researchers, being driven among others by technological
innovations (such as cloud
interfaces) and novel paradigms (such as social networks). Devising and developing machine
learning models, techniques
and algorithms for big data represent a fundamental problem stirred-up by the tremendous range
of critical applications
incorporating machine learning tools in their core platforms. For example, in application
settings where big data arise
and machine is useful, we recognize, among other things: (i) machine-learning-based processing
(e.g., acquisition,
knowledge discovery, and so forth) over large-scale sensor networks introduces important
advantages over classical
data-management-based approaches; similarly, (ii) medical and e-heath information systems
usually include successful
machine learning tools for processing and mining very large graphs modelling patient-to-disease,
patient-to-doctor, and
patient-to-therapy networks; (iii) genome data management and mining can gain important benefits
from machine learning
algorithms. Some hot topics in machine learning on big data include: (i) machine learning on
unconventional big data
sources (e.g., large-scale graphs in scientific applications, strongly-unstructured social
networks, and so forth); (ii)
machine learning over massive big data in distributed settings; (iii) scalable machine learning
algorithms; (iv) deep
learning - models, principles, issues; (v) machine-learning-based predictive approaches; (vi)
machine-learning-based big
data analytics; (vii) privacy-preserving machine learning on big data; (viii) temporal analysis
and spatial analysis on
big data; (ix) heterogeneous machine learning on big data; (x) novel applications of machine
learning on big data (e.g.,
healthcare, cybersecurity, smart cities, and so forth).
- Fundamentals
- Modelling
- Statistical Approaches
- Novel Paradigms
- Innovative Techniques
- Algorithms
- Innovative Architectures (GPU, Clouds, Clusters)
- Non-Conventional Big Data Settings (e.g., Spatio-Temporal Big Data, Streaming Big Data, Graph Big Data, Cloud Big Data, Probabilistic Big Data, Uncertain Big Data)
- Systems
- Architectures
- Advanced Topics (e.g., Dimensionality Reduction, Matrix Approximation Algorithms, Multi-Task Learning, Semi-Supervised Learning, Integration with NoSQL Databases)
- Case Studies and Applications
The 9th IEEE Special Session “Machine Learning on Big Data” (MLBD 2024) of the 2024 IEEE International Conference on Big Data (IEEE BigData 2024) will be held in Washington DC, USA, during December 15-18, 2024, and it aims to synergistically connect the research community and industry practitioners. It provides an international forum where scientific domain experts and Machine Learning and Data Mining researchers, practitioners and developers can share their findings in theoretical foundations, current methodologies, and practical experiences on Machine Learning on Big Data. MLBD 2024 will provide a stimulating environment to encourage discussion, fellowship, and exchange of ideas in all aspects of research related to Machine Learning on Big Data. This includes both original research contributions and insights from practical system design, implementation and evaluation, along with new research directions and emerging application domains in the target area. An expected outcome from MLBD 2024 is the identification of new problems in the main topics, and moves to achieve consolidated solutions to already-known problems. Other goals are to help in creating a focused community of scientists who create and drive interest in the area of Machine Learning on Big Data, and additionally to continue on the success of the event across future years.
Special Session LocationWashington DC, USA
Submission Guidelines and Instructions
Contributions are invited from prospective authors with interests in the indicated session topics and related areas of application. All contributions should be high quality, original and not published elsewhere or submitted for publication during the review period.
Submitted papers should strictly follow the IEEE official template. Maximum paper length allowed is:
Submitted papers will be thoroughly reviewed by members of the Special Session Program Committee for quality, correctness, originality and relevance. All accepted papers must be presented by one of the authors, who must register.
Papers must be submitted via the CyberChair System by selecting the track “Special Session on Machine Learning on Big Data”.
Paper Publication
Accepted papers will appear in the official IEEE Big Data 2024 main conference proceedings, published by IEEE.
Authors of selected papers from the workshop will be invited to submit an extended version of their paper to a special issue of a high-quality international journal.
Important Dates:
Paper submission: September 27, 2024
Notification of acceptance: October 27, 2024
Camera-ready paper due: November 17, 2024
Special Session: December 15-18, 2024
Program Committee Chair
Alfredo Cuzzocrea, University of Calabria, Italy
Program Committee
Manasvi Aggarwal, MasterCard AI Garage, India
Mst Shapna Akter, University of Oklahoma, USA
Lulwah Alkulaib, Kuwait University, Kuwait
Md Abdul Barek, University of West Florida, USA
Islam Belmerabet, University of Calabria, Italy
Ismail Benlaredj, University of Calabria, Italy
Giuseppe Cascavilla, Eindhoven University, The Netherlands
Philippe Cudre-Mauroux, University of Fribourg, Switzerland
Alfredo Cuzzocrea, University of Calabria, Italy
Edoardo Fadda, Politecnico di Torino, Italy
Carmine Gallo, University of Calabria, Italy
Joao Gama, University of Porto, Portugal
Abderraouf Hafsaoui, University of Calabria, Italy
Mojtaba Hajian, University of Calabria, Italy
Marwan Hassani, TU Eindhoven, The Netherlands
Carson K. Leung, University of Manitoba, Canada
Enzo Mumolo, University of Trieste, Italy
Apostolos Papadopoulos, Aristotle University of Thessaloniki, Greece
Giovanni Pilato, ICAR-CNR, Italy
Md Bajlur Rashid, University of West Florida, USA
Danda Rawat, Howard University, USA
Antonino Rullo, ICAR-CNR, Italy
Hossain Shahriar, University of West Florida, USA
For more information and any inquire, please contact Alfredo Cuzzocrea.
Special Session 3:
7th Special Session on HealthCare Data in IEEE Big Data 2024
December 15-18, 2024, Washington DC, USAHealth data differs from other industries' data in terms of structure, context, importance, volatility, availability, traceability, liquidity, change speed, usage and sources from which it is collected. As medicine is a constantly developing science, healthcare sector also. In this new emerging research area which stands at the intersection of several different discipline such as Medicine, Behavioral Science, Supply Chain Management or Big Data Analytics, techniques, methods, applications and devices are continuously developed to be used for the acquisition, storage, processing, analysis, standardization and optimization of every process in the health sector. As the healthcare sector is so challenging and related data are consistently explosive, healthcare organizations are focusing to become smarter in order to overcome the industry's inefficiencies to improve quality of care. “To become smarter” requires impeccable data analytics. All stakeholders in the sector should reveal the deep value of this valuable data in order to apply insights to improve quality of care, clinical outcomes and deliver personalized healthcare value, while reducing medical costs, collaborate across care settings to deliver integrated, personalized care experiences, prevent disease, promote wellness and manage care, build flexibility into operations to support cost reduction and excellence in clinical and business performance and practices.
The general purpose of this special session in IEEE BigData 2024 conference is to bring together researchers, academicians and sector employees from different fields and disciplines and provide them an independent platform to exchange information on their researches, ideas and findings about healthcare data and its analytics. It is also aimed to encourage debate on how big data can effectively support healthcare in terms of diagnosis, treatment and population health, and to develop a common understanding for research conducted in this multidisciplinary field.
Topics of interest include, but are not limited to, the following:- Healthcare Data
- Health data collection and analysis
- Problems in health data processing
- Protection and security of personal health data
- Electronic health records and standards
- Healthcare Information Systems
- Medical Imaging Systems
- Medical Applications
- Mobile Solutions
- Pervasive Healthcare Information Systems and Services
- Sensor nodes
- Wearable health information
- Information solutions developed for the disabled
- Process Management in Health Informatics Systems
- Health Decision Support Systems
- E-health Applications
Special Session Organizers:
Sultan Turhan (sturhan@gsu.edu.tr), PhD., Department of Computer Engineering, Galatasaray University
Assist. Prof. Ozgun Pinarer (opinarer@gsu.edu.tr), Department of Computer Engineering, Galatasaray University
Important Dates:
Full paper submission: Sept 27, 2024
Notification of paper acceptance: Oct 27, 2024
Camera-ready of accepted papers: Nov 17, 2024
Conference: Dec 15-18, 2024
Papers should be submitted as a PDF in 2-column IEEE format. Detailed instructions for the
authors can be
found at the conference website
(https://www3.cs.stonybrook.edu/~ieeebigdata2024/bigdata2024/CallPapers.html).
Accepted papers will be published in the conference proceedings.
All accepted papers must be presented by one of the author/s in the conference to include the
article in the
proceedings.
If you have any questions about the special session, please do not hesitate to contact us.
Special Session 4:
2nd Special Session on Understanding New Markets by Data Science, Social Science, and Economics
December 15-18, 2024, Washington DC, USALink to the special session: https://tetsuwaka.net/UNMDSSSE2024/
Recent innovations with Big Data and Artificial Intelligence have created new markets and
dramatically increased the
importance of data. Despite these social changes, existing economics, market design, management,
information systems,
engineering, social science, and data science approaches to these new social issues have
limitations. New market
understanding schemes and solutions for social implementation are needed.
To address these gaps, we propose a special session named “Understanding New Markets by Data
Science, Social Science,
and Economics” to discuss the processes and interactions among data, humans, and society with
researchers from
engineering, information systems, data science, social science, management, and economics. The
topics to be covered in
this session are practical issues for understanding new societies and markets, including
analytical work with data and
solutions to complex social problems. The session will cover not only cleanly formatted,
homogeneous data but also
heterogeneous data that influence human behavior, thinking, and intentions across different
domains. Discussions will
focus on how large-scale data can be used in healthcare, business management, and public
systems, as well as discussions
of quantitative assessments of what works in these areas and the obstacles to advancing their
use. In addition to these
research areas, we will explore utilizing large-scale data and designing mechanisms and
institutions that consider
social and cultural backgrounds across disciplines. We believe that this special session
focusing on the new schemes for
market understanding and design will be of great significance to academia and society.
We call for anyone interested in the following topics related to this special session.
- Statistical Graphics and Mathematics
- Finance and Business
- Physical Sciences and Engineering
- Earth, Space, and Environmental Sciences
- Text, Documents, and Software
- Social, Ambient, and Information Sciences
- Multimedia (Image/Video/Music) Mining
- Methods for Data Evaluation and Utilization
- Data Management and Curation
- Risks and Challenges of Data Exchange
- Trust, Resilience, Privacy, and Security Issues
- Design of Data
- Human-Computer Interaction
- Behavioral Science and Modeling
- Theoretical Models and Experimental Methods in Human-Computer Interaction
- Subjects and Field Experiments
- Cognitive Science and Human Behavior
- Modeling of Machine Learning for Social Data
- Ontology and Dictionary
- Business Efficiency
- Cognition and Perception Issues
- Retrieval/recommender systems
- Representation of Knowledge and Requirements
- Pricing and Evaluating Mechanism of Data
- Design of Data Platform
- Data Acquisition and Sensors
- Strategic Manipulation and Incentives
- Fairness and Social Welfare
- Practical Text Mining
- Financial/Economic NLP
- Summarization
- Topic Analysis
- Report Generation
- Large Language Model for Social Science
Organizers
Teruaki Hayashi, University of Tokyo, Japan (Co-chair)Hiroki Sakaji, Hokkaido, Japan (Co-chair)
Naoki Watanabe, Keio University, Japan (Co-chair)
Special Session 5:
Synergizing Mobility Data for Creating and Discovering Valuable Places
December 15-18, 2024, Washington DC, USACombining and utilizing data related to mobility in innovative ways, identifying, creating, and offering significant value
Scope
Places with the well-being of participants and the prosperity of the region may be regarded as a
lively, active, and
bustling atmosphere associated with crowds - where a lot of people gather close to each other.
However, not only
liveliness or activeness, but the mutual interaction of participants enhances the expected
prosperity of the local
society which can grow to make a region preferable to live and work in.
It is an open problem to discover such a place because the criteria for evaluating the value of
things, events, or even
that information to be available about a place have not yet been established. For example, just
a large number of
people, such as a marching army in which participants walk in the same direction, is not
expected to create new value in
the place. Even if they are shouting to fire themselves up, that will not still make the place a
residential town or a
market where various intellectual and commercial values emerge, which is often required to make
a location valuable in
terms of sustainable prosperity. To enjoy such prosperity with creativity, a crowd, a workplace,
a venue, or marketplace
should embrace the diversity of participants' interests and knowledge, which often cannot be
evaluated in a few
dimensions of value criteria but should be combine with other sources of dimensions via
physical, mental, and
intellectual interactions.
Here, we stand on the belief that such a valued place is the basis of the sustainable prosperity
of human society, where
a lively society with active markets is created via the synergetic interaction of individuals,
which are observed as
activities involving movements, communication, and exchange of values and information. Through
such activities, the
place can provide social, financial, physical, and community well-being to young, working, and
elderly people to enjoy
wellness and careers by which they are working to develop the values evaluated in the created
dimensions.
In this special session, we would like to have papers and presentations about methods or
theories for creating,
collecting, combining, or utilizing data on the activities of humans or relevant events so that
the values or potential
values of places can be discovered.
We will have a keynote presentation by Noboru Koshizuka, Professor
at The University of Tokyo and Director of the Data Society Alliance
(DSA). His talk will urge you to consider the relationship between
technology, people, places, and data. You will discover that data,
which can add value to locations and enhance social interactions among
individuals, extends beyond what we commonly refer to as "mobility
data."
We should involve data on human words, thoughts, health, weather
etc., for mining values
that may have been invisible or undetected so far. The authors are welcome to show approaches
for creating and using
novel data as well as novel values in the places. Hence, we communicate studies using mobility
data or their extensions
for value discovery and creation.
We call for presentations relevant to, but not restricted (as far as it is relevant to our interest above) to the three scopes below.
[Scope of Design with Big Data from/for Places] The topics below closely align with our focus on designing valuable places and creating meaningful physical environments for people to inhabit, experience, and enjoy. Each area offers rich opportunities for research and innovation in this field.:
Place-Making Strategies with Big data:
[Scope of Data Science] Overall, data scientists bring a diverse set of perspectives to the analysis and design of scenarios involving activities in various places and their synergetic effects, with a focus on ensuring data quality, creating and applying analysis techniques, leveraging advanced technologies, and addressing ethical considerations. Potential interests of the presentations include:
Data Integration:
[Scope of the Data Society] Overall, data providers involved in the analysis or design of data on activities in the real space are motivated by a combination of revenue generation, value creation, product innovation, customer engagement, partnerships, and risk management, all aimed at maximizing the value derived from the data collected and analyzed. Thus, the following interests in the data society fit this special session. In addition, scientific analysis and the design of the data market with synergetic interactions among participants are related:
Value creation:
Notification of acceptance: October 27, 2024
Camera-ready paper due: November 17, 2024
Special Session: December 15-18, 2024
https://wi-lab.com/cyberchair/2024/bigdata24/scripts/submit.php?subarea=SP05&undisplay_detail=1&wh=/cyberchair/2024/bigdata24/scripts/ws_submit.php
Accepted papers will be published in conference proceedings. All accepted papers must be presented by one of the authors to include the article in the proceedings. If you have any questions about this special session, please feel free to contact us: info@panda.sys.t.u-tokyo.ac.jp
Ohsawa, Yukio (chair: session originator)
The committee members are sorted alphabetically
Auernhammer, Jan
We exchange thoughts on society and marketplace of data with:
Special Session 6:
Special Session on Dataspaces and DFFT (Data Free Flow with Trust)
December 15-18, 2024, Washington DC, USAAbstract
Data is the most important property for bringing innovation and digital transformation. By using highly developed information and communication technology, we can generate, store, replicate, transfer, process and analyze data at very low cost, which realizes democratization of innovation, that is to give everyone in the world a chance for innovation. Today, the world is using the power of data to solve all kinds of issues, from global ones to everyday life ones.
Data-driven society achieves both economic development and resolution of social problems in parallel by connecting everyone and everything with each other, sharing various knowledge and information, and creating new value. From the perspective of economy, it will contribute to sustainable and harmonious economic development in the world. On the other hands, from the perspective of domestic social issues, it will contribute to the sustainability of regions and national security, including the declining birthrate, increasing aging population, depopulation of rural areas, economic disparity, and prevention of natural disasters and pandemics. For this purpose, vast Big Data in cyber space (virtual space) and physical space (real space) are linked across all over the world among various stakeholders to realize the vision of "Dataspace" and "Data Free Flow with Trust" (DFFT). The privacy, security, quality assurance and ease of use of the data itself must also be considered.
The general purpose of this special session in IEEE BigData 2024 conference is to bring together researchers, academicians, and sector employees from different fields and disciplines and to exchange information on their practical activities, research, ideas and findings about Dataspaces and platforms for DFFT. It is also aimed to encourage debate on how global platform can effectively support big data distribution, sharing, and application in terms of infrastructure, technology, governance, business, and so on, and to develop a common understanding for research conducted in this multidisciplinary field.
Topics of interest include, but are not limited to, the following:- Global Data Space Platform
- Federated Data Platform
- Federated Data Catalog
- Supply Chain Data Management
- Data Platform Federation Technology
- Connector and Broker Technology
- Data Collection Technology
- Fediverse
- Trust Federation
- Data Business Platform
- Data Trading Market
- Data Processing Platform
- DWH (Data Warehouse) Platform
- Data Lake Platform
- ETL (Extract, Transform, Load) Platform
- IoT Realtime Data Collection Platform
- Open Source Data Processing Platform
- Secure Data Sharing/Processing Platform
- Data Platform with Secure Multiparty Computation
- Federated Learning Platform
- Blockchain-based Data Platform
- Trusted Web Platform
- DIDs: Decentralized Identifiers
- Data Governance Rules, Law, and Policy
- Data sovereignty
- Cross-Border Data Flow Policy
- Personal Data and Privacy Protection
- Industry Confidential Data Protection
- Data Sharing Applications
- Smart City
- Digital Government
- Smart Mobility, MaaS (Mobility as a Services)
- Disaster Prevention and Response
- Supply Chain Management
- Carbon Neutral, CO2 Calculation, CO2 Proof
- Geographical Information Management
- Information Bank
Session Organizers
Noboru Koshizuka (Chair)
The University of Tokyo
noboru@koshizuka-lab.org
Stephan Haller
Bern University of Applied Sciences
stephan.haller@bfh.ch
Hiroshi Mano
Data Society Alliance
h.mano@data-society-alliance.org
Yukio Ohsawa
The University of Tokyo
ohsawa@sys.t.u-tokyo.ac.jp
Boris Otto
Fraunhofer ISST
Boris.Otto@isst.fraunhofer.de
Shinji Shimojo
Osaka University
shinji.shimojo.cmc@osaka-u.ac.jp
Hideaki Takeda
National Institute of Informatics
takeda@nii.ac.jp
Hirotsugu Seike
The University of Tokyo
hirotsugi.seike@koshizuka-lab.org
Information for Authors
Paper Submission
Please submit a full-length paper (up to 10 page IEEE 2-column format, reference pages counted
in the 10 pages), or a
short vision paper (up to 5 pages IEEE 2-column format, including references) through the online
submission system.
https://wi-lab.com/cyberchair/2024/bigdata24/index.php
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines
(see link to
"formatting instructions" below).
https://www.ieee.org/conferences/publishing/templates.html
- Electronic submission of papers: September 30, 2024
- Notification of paper acceptance: October 27, 2024
- Camera-ready of accepted papers: November 17, 2024
- Conference: December 15-18, 2024
Related Events and Activities
We exchange thoughts on society and marketplace of data with: Special Session on Synergizing
Mobility Data for Creating
and Discovering Valuable Places in IEEE Bigdata 2024.
Special Session 7:
10th Special Session on Intelligent Data Mining
December 15-18, 2024 Washington DC, USAIEEE BigData 2024
After the successes of the first, second, third, fourth, fifth, sixth, seventh, eighth and ninth
editions of Special
Session on Intelligent Data Mining in Santa Clara, CA (2015); Washington, DC (2016); Boston, MA
(2017); Seattle, WA,
(2018); Los Angeles, CA, (2019); Online Pandemic Session (2020), Online Pandemic Session (2021);
Osaka, JAPAN (2022);
Sorrento, ITALY (2023) and the 10th Special Session on Intelligent Data Mining in Washington DC,
USA will continue
promoting and disseminating the knowledge concerning several topics and technologies related to
data mining science.
Artificial Intelligence (AI) & Machine Learning (ML) fields are interdisciplinary, including
computer science,
mathematics, psychology, linguistics, philosophy, neuroscience etc. This interdisciplinary
special session seeks
scientific understanding on data and intelligence.
This session may help to create scientific evolution to propose robust and powerful schemes
between human nature and big
data processing.
Intelligent Data Mining session open to every researcher as well as industrial partners,
The aims of this Special Session on Intelligent Data Mining are to:
- Bring researchers and experts together to discuss and share their experiences
- Share the current and new research topics and ideas
- Improve and enhance personal, enterprise, national and international awareness
- Provide a platform to present and discuss recent advancements
- Increase international collaborations among university-industry-institutions
In the fields of theory and applications of data mining, artificial intelligence, computer science, mathematics, psychology, linguistics, philosophy, neuroscience and other disciplines to discuss better understanding of big data and intelligence.
The papers submitted to this special session might be in a large range of topics that include theory, application and implementation of artificial intelligence, machine learning and data mining including but not limited to the topics given below,
Use of Artificial Intelligence || Machine Learning in Data Mining as- Data Mining, Data Science and Big Data
- Data Warehouse, Clustering, Visualization
- Big Data and Services
- Graph Mining
- Data Security and Privacy
- Homeland Security and Data Analysis
- GPU Applications
- Medical Imaging
- Deep Learning
- Scalable Computing, Cloud Computing
- Knowledge Discovery, Integration, Transformatio
- Information Retrieval
- Information Security
- Data Classification, Regression, Cleaning
- Smart Cities & Energy
- Social Media, Social Networking, Social Data
- Semantic Computing
- IoT, Autonomous Systems and Agents
- Algorithms
- Mobile Computing
- Sensors, Networks, Devices
- Mathematics
- NLP
- Philosophy
- Neuroscience and Bioinformatics
- Biometric
- Sustainability
- HPCC and Hadoop,
- Recent Theory, Trends, Technologies and Applications in Data Mining
- Future Directions and Challenges in Data Mining
- Industrial Challenges in Data Mining
- Demo Applications in Data Mining
Papers should be submitted for this special session by Sept 15, 2024
Papers should be submitted as a PDF in 2-column IEEE format. Detailed instructions for the authors can be found at the conference website. Accepted papers will be published in the conference proceedings. All accepted papers must be presented by one of the author/s in the conference to include the article in the proceedings (http://bigdataieee.org/BigData2024/).If you have any question about this special session, please do not hesitate to direct your question to the special session organizer Asst. Prof. Dr. Uraz YAVANOGLU (urazyavanoglu@gmail.com , uraz@gazi.edu.tr )
Special Session Organizer:
Asst. Prof. Dr. Uraz YAVANOGLU,
Department of Computer Engineering(CS)
Gazi University, Turkey
- Full Paper Submission Deadline : Sept 30, 2024 11:59 pm PST
- Notification of Acceptance : Nov 3, 2024
- Camera-ready papers & Pre-registration : Nov 17, 2024, 11:59pm PST
- Conference Dates : Dec 15-18, 2024
Special Session 8:
Special Session on Federated Learning on Big Data
December 15-18, 2024 Washington DC, USAIEEE BigData 2024
Aim and Scope
The "Special Session on Federated Learning on Big Data" aims to bring together researchers,
industry practitioners, and
policymakers to explore cutting-edge advancements and address pressing challenges in the
application of federated
learning to Big Data. Federated learning is revolutionizing the way organizations handle machine
learning across
distributed data sources, enabling collaborative model training without compromising data
privacy. With the
proliferation of data from various sources such as healthcare, finance, IoT, and multimedia,
this session provides an
invaluable opportunity to delve into the practical and theoretical aspects of federated
learning, focusing on its
integration with the 5Vs of Big Data: Volume, Velocity, Variety, Value, and Veracity.
The session will highlight recent innovations in federated learning algorithms and frameworks
designed to handle the
unique challenges posed by Big Data, such as heterogeneous data distributions and resource
constraints. Furthermore, it
will explore the interplay between federated learning and privacy-preserving mechanisms,
ensuring secure data exchange
across institutions and organizations. Special emphasis will be placed on real-world
applications in healthcare, IoT,
and finance, where federated learning allows organizations to harness the potential of
decentralized data while
respecting privacy regulations.
We aim to foster cross-disciplinary collaboration and knowledge-sharing that leads to new
methods, architectures, and
systems that push the boundaries of federated learning research. This session will also shed
light on the emerging
policy and ethical considerations in the deployment of federated learning models, providing a
comprehensive view of this
rapidly evolving field. Ultimately, our goal is to build a vibrant community that propels
federated learning into a
pivotal role in addressing the challenges and opportunities of Big Data analytics.
- Federated learning algorithms for Big Data processing
- Privacy-preserving mechanisms in federated learning
- Security challenges and solutions in federated learning
- Efficient model aggregation and optimization techniques
- Applications of federated learning in healthcare, finance, and IoT
- Data governance and compliance in federated learning systems
- Challenges and solutions for model updates in non-IID data distributions
- Resource-efficient federated learning for edge devices
- Collaborative learning frameworks for multi-institutional Big Data analytics
- Evaluation metrics and benchmarking for federated learning systems
- Novel architectures and platforms for federated learning deployment
- Adaptive and personalized federated learning models
Special Session Organizers
Prof. Francesco Piccialli, University of Naples Federico II, Italy
Dr. Diletta Chiaro, University of Naples Federico II, Italy
Prof. David Camacho, Universidad Politecnica de Matrid, Spain
Prof. Antonella Guzzo, University of Calabria, Italy
Prof. Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway
Important Dates
Full paper submission: Oct 4, 2024
Notification of paper acceptance: Oct 27, 2024
Camera-ready of accepted papers: Nov 17, 2024
Conference: Dec 15-18, 2024
Instructions
Paper Submission Please submit a full-length paper (up to 10 page IEEE 2-column format,
reference pages counted in the
10 pages), or a short vision paper (up to 5 pages IEEE 2-column format, including references)
through the online
submission system.
https://wi-lab.com/cyberchair/2024/bigdata24/index.php
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines
(see link to
"formatting instructions" below).
https://www.ieee.org/conferences/publishing/templates.html
Accepted papers will be published in conference proceedings. All accepted papers must be
presented by one of the authors
to include the article in the proceedings. If you have any questions about this special session,
please feel free to
contact us: francesco.piccialli@unina.it
Special Session 9:
Special Session on Social Cognitive Computing in Digital Education and Learning
December 15-18, 2024 Washington DC, USAIEEE BigData 2024
Organizers
- Jerry Chun-Wei Lin (contact person),
Western Norway University of Applied Sciences, Bergen, Norway - Ilona Heldal,
Western Norway University of Applied Sciences, Bergen, Norway
The integration of social cognitive computing into education and learning has the potential to revolutionize the way we teach and learn. This workshop aims to explore the application of artificial intelligence, machine learning, and cognitive computing in education and how these technologies can improve teaching and learning outcomes by creating new and innovative educational experiences. The focus will be on understanding the impact of social cognitive computing on education and how these technologies can be used to improve learning outcomes, increase student engagement, and create more personalized learning experiences. Another focus is on utilizing visualization, games, or gamification solutions for these applications and their evaluations. This workshop will bring together educators, instructional designers, researchers, and technology experts to discuss the current state of AI-powered educational technology and its impact on teaching and learning, as well as the challenges and opportunities of integrating social cognitive computing into education and learning. Attendees will have the opportunity to learn from experts in the field, engage in interactive discussions, and take away best practices and successful case studies for implementing social cognitive computing in their own educational context. Topics are listed below but not limited to:
- Intelligent Tutoring Systems (ITS) development for personalized instruction using AI and pattern analysis techniques
- Collection, analysis, and interpretation of data from learners' interactions with educational technology to improve effectiveness and identify struggling students
- Interactions with each other and with instructors in social and collaborative learning environments
- Using game-like elements in educational software to motivate and engage learners.
- Tailoring educational content and instruction to the individual learner's needs and preferences.
- Adjusting the level and pace of instruction to meet the individual learner's needs using AI techniques
- Using AI to analyze and understand human language in educational software to provide feedback and guidance in natural language
This project is partially supported by the HORIZON Research and Innovation Actions with project title: Design and evaluation of technological support tools to empower stakeholders in digital education and project number is: 101060918
Important Dates- Electronic submission of full papers: Oct 10, 2024
- Notification of paper acceptance: Nov 10, 2024
- Camera-ready of accepted papers: Nov 17, 2024
- Conference: Dec 15-18, 2024
Instructions
Papers should be submitted as a PDF in 2-column IEEE format. Detailed instructions for the authors can be found on the
conference website
https://www3.cs.stonybrook.edu/~ieeebigdata2024/CallPapers.html
Accepted papers will be published in the conference proceedings.
All accepted papers must be presented by one of the authors in the conference to include the article in the proceedings.
If you have any questions about the special session, please do not hesitate to contact us.
Paper submission page:
https://wi-lab.com/cyberchair/2024/bigdata24/index.php
Special Session 10:
Data-Driven Designation and Implementation of Automated Guided Vehicles
December 15-18, 2024 Washington DC, USAIEEE BigData 2024
Special Session Organizer
Jerry Chun-Wei Lin
Western Norway University of Applied Sciences, Norway
Francesco Piccialli
University of Naples Federico II, Italy
Rafał Cupek
Silesian University of Technology, Poland
Dariusz Mrozek
Silesian University of Technology, Poland
Brief Description and Justification
The growing popularity of Autonomous Guided Vehicles (AGVs) has not only been the result of their technical
features but also their ability to cooperate. Cooperative-based internal logistics enables increased
production flexibility. AGVs have become a key enabling technology for the flexible internal logistics that
are required for agile production systems. Modern production systems are characterized by frequent changes
that result from orders that are changed by customers, low material buffers, the agile production
technologies that are performed by robotized production stands and the many variants of production
technology that can be used. All of the above-mentioned factors require the production process to be
supported online by highly advanced information services, which are performed during successive steps in the
production chain. In addition, big and remote sensing data play a fundamental role in AGV, which helps
acquire the patterns of driving/travel behaviour, human mobility, and traffic flow, and in sensing a more
large-scale environment and giving more accurate, traffic-aware navigation. This means that the production
activities cannot be centrally planned but have to be performed cooperatively concerning the sensing data,
ongoing production tasks, available materials, production equipment and technologies. The new generation of
data-driven systems in AGVs has to support the autonomy and distribution of decision-making processes.
Thus, this special session is focused on the following issues but not limited to:
- Data Mining support for Energy and Resource Efficient Internal Logistics
- Communication between Automated Guided Vehicles and Production Stands and Production System
- Automated Guided Vehicle Integrated with Collaborative Robot
- Use Case-based CoBotAGVs Integration with Industry4.0 Production Systems
- Multi-source sensor data collection, processing and data fusion by collaborative AGV
- Machine learning-based traffic safety analysis, trajectory and route prediction
- Data-driven autonomous driving assistance
- Prediction of traffic flow based on sensing data of AGVs
- Pavement performance evaluation and predictions of AGVs
- Architecture design, implementation and case studies of AGVs
Important Dates
Full paper submission: Oct 10, 2024
Notification of paper acceptance: Nov 10, 2024
Camera-ready of accepted papers: Nov 17, 2024
Conference: Dec 15-18, 2024
Instructions
Papers should be submitted as a PDF in 2-column IEEE format. Detailed instructions for the authors can be
found on the conference website
https://www3.cs.stonybrook.edu/~ieeebigdata2024/CallPapers.html.
Accepted papers will be published in the conference proceedings.
All accepted papers must be presented by one of the authors in the conference to include the article in the
proceedings.
If you have any question about the special session, please do not hesitate to contact us.
Paper submission page:
https://wi-lab.com/cyberchair/2024/bigdata24/index.php
Special Session 11:
10th Special Session on Information Granulation in Data Science and Scalable Computing
December 15-18, 2024 Washington DC, USAIEEE BigData 2024
| TWO JOINT EVENTS @ IEEE BigData 2024 10th Special Session on Information Granulation in Data Science and Scalable Computing & BigData Cup Challenge on Predicting Chess Puzzle Difficulty @ KnowledgePit.ai |
||
| 15 December 2024 (ONLINE, LINK WILL BE PROVIDED) | ||
| 09:00-09:10 | Introduction | |
| Language Granularity (Part of Special Session) | Chair: Shusaku Tsumoto | |
| 09:10-09:30 | SP14202: On Text Granularity and Metric Frameworks for Large Language Model Content Detection | Linh Le, Dung Tran |
| 09:30-09:50 | SP14210: KeyMinES: Extracting Minimal Keyphrases for Sub-events in Disaster Situations | Ademola Adesokan, Sanjay Madria |
| 09:50-10:10 | BigD726: Textual Out-of-Distribution Data Detection Based on
Granular Dictionary |
Tinghui Ouyang, Toshiyuki Amagasa |
| 10:10-10:30 | SP14215: EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educational Support | Qiaomu Li, Ying Xie, Sumit Chakravarty, Dabae Lee |
| 10:30-10:50 | Coffee Break | |
| Granularity in Data Mining (Part of Special Session) | Chair: Tzung Pei Hong | |
| 10:50-11:10 | SP14203: A Utility-Mining-Driven Active Learning
Approach for Analyzing Clickstream Sequences |
Danny Y.C. Wang, Lars Arne Jordanger, Jerry Chun-Wei Lin |
| 11:10-11:30 | SP14212: A Federated Mining Framework for Complete Erasable Itemsets | Tzung-Pei Hong, Meng-Jui Kuo, Chun-Hao Chen, Katherine Shu-Min Li |
| 11:30-11:50 | SP14216: Hierarchical Approach to Data Quality Understanding | Alina Powała, Dominik Ślęzak |
| 11:50-12:10 | SF14207: RFMI-based Customer Segmentation with K-means | Wensheng Gan, Pinlyu Zhou, Shicheng Wan, Jiyuan Zeng, Zhenlian Q |
| 12:10-12:30 | SP14208: Recursive Queries: Twenty-Five Years After SQL:1999 | Marta Burzańska, Piotr Wiśniewski, Krzysztof Stencel |
| 12:30-14:00 | Lunch Break | |
| Predicting Chess Puzzle Difficulty, Part 1 (BigData Cup) | Chair: Dominik Ślęzak | |
| 14:00-14:20 | SC01207: IEEE Big Data Cup 2024 Report: Predicting Chess Puzzle Difficulty at KnowledgePit.ai | Jan Zyśko, Maciej Świechowski, Sebastian Stawicki, Katarzyna
Jagieła, Andrzej Janusz, Dominik Ślęzak |
| 14:20-14:40 | SC01205: Moves Based Prediction of Chess
Puzzle Difficulty with Convolutional Neural Networks |
Dymitr Ruta, Ming Liu, Ling Cen |
| 14:40-15:00 | SC01203: Predicting Chess Puzzle Difficulty with Transformers | Szymon Miłosz, Paweł Kapusta |
| 15:00-15:20 | SC01208: Do Data Scientists Dream About Their Skills' Assessment? - Transforming a Competition Platform Into an Assessment Platform | Dominik Ślęzak, Andrzej Janusz, Maciej
Świechowski, Agnieszka Chądzyńska- Krasowska, Jacek Kamiński |
| 15:20-15:40 | Coffee Break | |
| Granular Computing Applications (Part of Special Session) | Chair: Weiping Ding | |
| 15:40-16:00 | SP14201: Premenstrual Syndrome Detection Based on Granular Computing and AI in Home Environment | Łukasz Sosnowski, Iwona Szymusik |
| 16:00-16:20 | SP14211: About Granular Rough Computing: Concept-Dependent Granulation Powered by Map Reduce | Radosław Cybulski |
| 16:20-16:40 | SP14214: Determination of Disease Codes from Electronic Patient Records | Tomohiro Kimura, Shoji Hirano, Shusaku Tsumoto |
| 16:40-17:00 | SP14213: Big Data Analytics in Patient Navigation Service | Tomohiro Kimura, Shoji Hirano, Shusaku Tsumoto |
| 16 December 2024 (ONSITE, YELLOWSTONE ROOM) | ||
| Information
Granulation (Part of Special Session) & Predicting Chess Puzzle Difficulty, Part 2 (BigData Cup) |
Chair: Dominik Ślęzak | |
| 10:30-10:50 | SP14206: Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark | Bożena Małysiak-Mrozek, Paulina Pawlowicz, Vaidy Sunderam, |
| 10:50-11:10 | SP14209: An incremental approach for the detection of legend text in digital maps | Salem Benferhat, Arthur Marzinkowski, Anastasia Paparrizou, Cédric Piette |
| 11:10-11:30 | SC01206: The bread emoji Team’s Submission to the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty Challenge | Tyler Woodruff, Oleg Filatov, Marco Cognetta |
| 11:30-11:50 | SC01202:
Estimating Chess Puzzle Difficulty Without Past Game Records Using a Human Problem-Solving Inspired Neural Network Architecture |
Anan Schütt, Tobias Huber, Elisabeth André |
| 11:50-12:10 | SC01201: Estimating the Puzzlingness of Chess Puzzles | Sebastian Björkqvist |
| 12:10-12:30 | SC01204: Pairwise Learning to Rank for Chess Puzzle Difficulty Prediction | Andry Rafaralahy |
BACKGROUND:
Granular Computing is a general computation approach for a usage of information granules such as data blocks, clusters,
groups, as well as value intervals, sets, hierarchies, etc., to build efficient computational models for complex Big
Data applications, characterized by huge amounts of diverse data and associated domain knowledge. Information
Granulation, under different names, has appeared in many fields, such as granularity in artificial intelligence, divide
and conquer methods for scaling calculations, approximate computing, knowledge representation, topological data
analysis, image processing, deep learning and many others related with human and machine intelligence. Recently,
coarse-grained approaches in convolutional networks have been paid attention to theorical analysis of deep learning from
physics. Physicist pointed out that Renormalization flow controls the behavior of deep neural networks, whose mechanism
is corresponding to the control of granularity in information theory.
The principles of Granular Computing can be also helpful to design simplified descriptions of complex data systems and
to bridge the gap between the humans and AI. Herein we may follow the phrase "Information Granules = Fundamental Pieces
of Human Knowledge" and treat Granular Computing as one of important meta-mathematical methodologies for Big Data
Analytics.
SESSION SCOPE:
The 10th session in this series continues to address the theory and practice of derivations and computations based on
various types of granular models and structures. It provides researchers from both academia and industry with the means
to present the state-of-the-art results and methodologies related to Information Granulation and Granular Computing,
with a special emphasis on applications in Data Science and Scalable Computing. The session also refers - from the
particular viewpoint of Information Granulation - to currently important research tracks such as social network
computing, cloud computing, cyber-security, data mining, process mining, machine learning, statistics, knowledge
management, AI-based systems, soft computing, e-Intelligence, business intelligence, bioinformatics, health informatics
and IoT. The papers addressing Information Granulation in the emerging field of XAI and using its principles to
construct interpretable AI models are highly welcome as well. Particularly, we encourage the papers which deliver
experimental results but in the same time, provide theoretical foundations to justify those results.
HIGHLIGHTS:
The session is organized as a part of the IEEE Big Data 2024 conference (December 15-18), which is a well-established
and competitive international event targeted at modern trends in big data processing and analytics.
The session is intended to be a forum for discussing ideas, issues and methods based on and inspired by Information
Granulation and Granular Computing, in an atmosphere promoting free exchange of viewpoints and perspectives coming from
different application areas.
Papers accepted to the session will be published in the IEEE Big Data 2024 conference proceedings, together with papers
accepted to the main conference track.
Organizers are planning a special issue in a relevant scientific journal, such as Big Data Research (Elsevier), Granular
Computing (Springer) or Big Data Mining and Analytics (Tsinghua University Press).
Organizers particularly encourage papers which deliver experimental results but in the same time, provide theoretical
foundations to justify those results.
ORGANIZERS
Shusaku Tsumoto
Shimane University, Japan
tsumoto@med.shimane-u.ac.jp
Dominik Slezak
University of Warsaw & QED Software, Poland
dominik.slezak@qed.pl
Tzung-Pei Hong
National University of Kaohsiung, Taiwan
tphong@nuk.edu.tw
Weiping Ding
Nantong University, China
dwp9988@hotmail.com
Paper submission: September 27, 2024
Notification of acceptance: October 27, 2024
Camera-ready paper due: November 17, 2024
Special Session: December 15-18, 2024