Special Session 1:

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:

  • Privacy of Big Data: Fundamentals
  • Privacy of Big Data: Modelling
  • Privacy of Big Data: Statistical Approaches
  • Privacy of Big Data: Novel Paradigms
  • Privacy of Big Data: Innovative Protocols
  • Privacy of Big Data: Algorithms
  • Privacy of Big Data: Query Optimization
  • Privacy of Big Data: Non-Conventional Environments (e.g., Spatio-Temporal Data, Streaming Data, Cloud Data, Probabilistic Data, Uncertain Data)
  • Privacy of Big Data: Systems
  • Privacy of Big Data: Architectures
  • Privacy of Big Data: Advanced Topics (e.g., NoSQL Databases)
  • Privacy of Big Data: Case Studies and Applications
  • Security of Big Data: Fundamentals
  • Security of Big Data: Modelling
  • Security of Big Data: Statistical Approaches
  • Security of Big Data: Novel Paradigms
  • Security of Big Data: Innovative Protocols
  • Security of Big Data: Algorithms
  • Security of Big Data: Query Optimization
  • Security of Big Data: Non-Conventional Environments (e.g., Spatio-Temporal Data, Streaming Data, Cloud Data, Probabilistic Data, Uncertain Data)
  • Security of Big Data: Systems
  • Security of Big Data: Architectures
  • Security of Big Data: Advanced Topics (e.g., NoSQL Databases)
  • Security of Big Data: Case Studies and Applications

  • 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:

  • Full Papers: 10 pages
  • Short Papers: 6 pages
  • Demo Papers: 6 pages
  • Position Papers: 6 pages

  • 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
    Maurizio Atzori, University of Cagliari, Italy
    Roberto Baldoni, University of Rome “Sapienza”, Italy
    Elisa Bertino, CERIAS and Purdue University, USA
    Pietro Colombo, University of Insubria, Italy
    Alfredo Cuzzocrea, University of Calabria, Italy
    Rinku Dewri, University of Denver, USA
    Katerina Doka, NTUA, Greece
    Josep Domingo-Ferrer, Universitat Rovira i Virgili, Spain
    Yucheng Dong, Sichuan University, China
    Murat Kantarcioglu, University of Texas at Dallas, USA
    Thorsten Strufe, Technische Universitat Darmstadt, Germany
    Vicenc Torra, IIIA-CSIC, Spain
    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).

    The MLBD 2024 special session focuses on all the research aspects of machine learning on Big Data. Among these, an unrestricted list includes:

    • 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 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:
  • Full Papers: 10 pages
  • Short Papers: 6 pages
  • Demo Papers: 6 pages
  • Position Papers: 6 pages

  • 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
    Michelangelo Ceci, University of Bari, Italy
    Alfredo Cuzzocrea, University of Calabria, Italy
    Joao Gama, University of Porto, Portugal
    Marwan Hassani, TU Eindhoven, The Netherlands
    Mark Last, Ben-Gurion University of the Negev, Israel
    Rocco Langone, Deloitte, Belgium
    Carson K. Leung, University of Manitoba, Canada
    Sofian Maabout, LABRI, Bordeaux University, France
    Anirban Mondal, Shiv Nadar University, India
    Enzo Mumolo, University of Trieste, Italy
    Apostolos Papadopoulos, Aristotle University of Thessaloniki, Greece

    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, USA

    Health 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 3, 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, USA

    Link 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.

    Data-oriented Application Areas:
    • 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
    Case Studies on Data Exchange and Collaboration:
    • 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
    Data-focused Cognitive Research:
    • 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
    Empirical and Comprehension Focused Data Utilization:
    • Modeling of Machine Learning for Social Data
    • Ontology and Dictionary
    • Business Efficiency
    • Cognition and Perception Issues
    • Retrieval/recommender systems
    Data Market and Ecosystem:
    • 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
    NLP in Social Science:
    • 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, USA

    Combining 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 the keynote presentation by Renate Fruchter, Founding Director of the Project Based Learning Laboratory (PBL Lab) at Stanford University, a designer of physical and virtual interactive learning and workspaces. Focusing on the relationship between technology, people, place, and process. As you will find in her talk, although the target is "places" where a space turns into a valued environment for human(s) to live in, the information useful for adding value to the place and fostering social interactions of individuals can be beyond what we believe "mobility data" are. 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.

    Relevant areas

    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:
    Methods and approaches to create meaningful and engaging environments that foster social interaction, community cohesion, and a sense of belonging, on the data on daily human experiences and activities.
    Participatory Design:
    Approaches to involve stakeholders, including residents, businesses, and community groups, in the design process to ensure that their needs, values, and aspirations are reflected in the final outcomes.
    Environmental Psychology:
    Relationship between the built environment and human behavior, emotions, and well-being, and explore design interventions to create supportive and restorative places from behavioral and emotional data.
    Smart Cities and Technologies:
    Integration of digital technologies, data analytics, and Internet of Things (IoT) solutions to enhance the functionality, efficiency, and sustainability of urban spaces and infrastructure.
    Placemaking for Health and Well-being:
    The role of design in promoting physical activity, mental health, and social well-being through the creation of accessible, inclusive, and health-promoting environments, involving data on human health and social/natural phenomena, for example, weather data.
    Sustainable Design Practices:
    Research strategies for integrating principles of sustainability, resilience, and ecological stewardship into the design of buildings, landscapes, and urban infrastructure to minimize environmental impact and resource consumption --- the proposal of data for this and the following topics-are expected.
    Long and short-term interventions:
    Interventions, such as revitalizing historic sites and landmarks, street festivals, and community gardens, to activate underutilized spaces, foster community engagement, and catalyze long-term urban transformation, on the data on visitor and habitant communication.

    [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:
    Integration of data which may emanate from several different sources and are represented in several different formats, resolving entities within and across data for deriving utility from data. Acquisition of knowledge for decision-making, which may be beyond the reach of a single dataset, involving the interaction with data marketplaces and the cyber/real world. New aspects of computation, cognition, or communication for learning from integrated and/or visualized data to reinforce predictive performance and interpretability of knowledge. This is tightly linked to the cope of Design.
    Data Quality Assurance:
    The focus is on ensuring the utility, accuracy, reliability, and completeness of the data collected from places. Scientists may challenge the analysis by scrutinizing the data collection/analysis methods, identifying potential biases, or verifying the integrity of the data.
    Statistical Analysis:
    Application of statistical techniques to analyze the activities of individuals and their communication and mobility with or without vehicles in the target places. Authors of papers may challenge the analysis by exploring different statistical models, testing hypotheses, or conducting inferential analyses to extract meaningful insights from the data.
    Machine Learning and Predictive Modeling:
    Utilization or development of machine learning algorithms to predict the behavior of a crowd or its individuals or to obtain patterns based on historical data. This is also related to challenging the design of a synergetic community, marketplace, or public place by experimenting with analysis models, feature engineering techniques, or tuning the model parameters to simulate the quality of lives of diverse people in a local region.
    Data Visualization and Interpretation:
    Data visualization techniques to foster insights into the values in the place. The author may challenge the analysis by creating interactive visualizations, exploring different visualization tools, or designing intuitive dashboards to effectively communicate insights.
    Ethical and Privacy Considerations:
    Ethical implications and privacy issues related to the analysis of the above or other categories. The author may challenge the design by implementing privacy-preserving techniques, ensuring data anonymization, or adhering to ethical guidelines to protect individuals' rights and confidentiality.

    [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:
    Creation of value-added services, such as analytics, insights, or consulting, for clients or customers in the market. Proprietary algorithms or models to extract actionable insights from the data and methods to provide these insights as part of service offerings.
    Data Monetization:
    Methods and technologies to monetize the data collected from crowds or their activities by selling them to businesses, researchers, or government agencies interested in analyzing the behavior of crowds. Methods of generating revenue through data licensing agreements or subscription-based models.
    Product/Service Development:
    Insights gained from analysis of live individuals. Communities or crowds to inform the development of new products or features. For example, crowd management solutions, event planning tools, and location-based services are tailored to the needs of businesses or event organizers.
    Customer Engagement:
    Methods for making personalized experiences or recommendations based on the analysis of human individual/community/crowd/social behaviors based on data-driven insights to improve customer satisfaction. In addition, there are methods for driving user engagement on these platforms.
    Partnerships and Collaborations:
    Methods for exploring partnerships or collaborations with other organizations, such as other individuals, companies, or government agencies, to leverage complementary expertise and resources to activate the interactions of those acting in a place.
    Regulatory Compliance and Risk Management:
    Ensuring regulatory compliance and mitigating the risks associated with privacy, security, and ethical concerns. Robust governance frameworks for personal data, security measures, and compliance processes to address these issues.

    Information for Authors.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
    Instructions
    Papers should be submitted as PDF in a 2-column IEEE format. Detailed instructions for the authors can be found on the paper submission page provided by the conference.
    https://wi-lab.com/cyberchair/2024/bigdata24/index.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

    Organizing Committe Members
    Ohsawa, Yukio (chair: session originator)
    Professor, School of Engineering in The University of Tokyo, Nigiwai Lab., Japan
    Kondo, Sae (co-chair: session originator)
    Assoc. Professor, School of Engingeering in Mie University, and RCAST in The University of Tokyo, Japan
    Koshizuka, Noboru (co-chair: connection to the special session on DFFT)
    Professor, Interfaculty Initiative in Information Studies, The University of Tokyo, Japan

    The committee members are sorted alphabetically
    Auernhammer, Jan
    Casual Academic, University of Technology Sydney, Australia
    Agrawal, Jitendra
    Senior Lecturer, School of Civil, Aerospace and Design Engineering, Bristol University, UK
    Bandini, Stefania
    Professor, Department of Computer Science, Systems and Communication, University of Milano-Bicocca, Italy
    Bewong, Michael
    Senior Lecturer in Computing, Charles Sturt University, Australia
    Chen, Lieu-Hen
    Professor, Department of Computer Science and Information Engineering, National Chi Nan University, Taiwan
    Correa da Silva, Flavio
    Associate Professor, Department of Computer Engineering and Digital Systems, Universidade de Sao Paulo, Brazil
    Milella, Frida
    Assistant Professor, Department of Informatics, Systems and Communication, University of Milano-Bicocca, Italy
    Navez, Didier
    Senior Vice President, Data Policy & Governance, Dawex, France
    Fruchter, Renate
    Funding Director, Project Based Learning Laboratory (PBL Lab), Stanford University, USA
    Jugulum, Rajesh
    Affiliate Professor, Dr., Northeastern University, USA
    Nishinari, Katsuhiro
    Professor, School of Engineering in The University of Tokyo, Japan
    Sekiguchi, Kaira
    Project Researcher, School of Engineering in The University of Tokyo, Japan
    Van den Poel, Dirk
    Professor of Data Analytics/Big Data, Ghent University, Belgium
    Wang, Hao (Henry)
    Vice President, Industry Large Model Team, Alibaba Cloud Group, China

    Organizing Committe Members
    We exchange thoughts on society and marketplace of data with:
    Special Session on Data Free Flow with Trust (DFFT) in IEEE Bigdata 2024
    We will exchange ideas and news on social contribution of bigdata with:
    Yokohama Co-creation Consortium
    We will exchange topics on data-federative innovation with:
    Data Federative Innovation Social Cooperative Program, The University of Tokyo

    Special Session 6:

    Special Session on Dataspaces and DFFT (Data Free Flow with Trust)

    December 15-18, 2024, Washington DC, USA

    Abstract

    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

    Important Dates
    • Electronic submission of papers: September 15, 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, USA
    IEEE 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

    The important dates for this special session are:
    • Full Paper Submission Deadline : Sept 15, 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, USA
    IEEE 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.

    Topics of interest include, but are not limited to, the following:
    • 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: Sept 27, 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, USA
    IEEE 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: TBD
    • Notification of paper acceptance: TBD
    • Camera-ready of accepted papers: TBD
    • 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, USA
    IEEE 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: TBD
    Notification of paper acceptance: TBD
    Camera-ready of accepted papers: TBD
    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