Special Session 1:5th Special Session on Intelligent Data Mining
Special Session 2:IEEE Big Data 2019 Special Track on Federated Machine Learning
Special Session 3:Information Granulation in Data Science and Scalable Computing
Special Session 4:2nd Special Session on HealthCare Data in IEEE Big Data 2019
Special Session 5:Machine Learning on Big Data (MLBD 2019)
5th Special Session on Intelligent Data MiningDecember 9-12, 2019 Los Angeles, CA, USA
After the successes of the first, second, third and fourth editions of Special Session on Intelligent Data Mining in Santa Clara, CA (2015), Washington, DC (2016), Boston, MA (2017), Seattle, WA, (2018) and the fifth Special Session on Intelligent Data Mining in Los Angeles, CA (2019) 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
- Coin Mining and GPU Applications
- Deep Learning
- Scalable Computing, Cloud Computing
- Knowledge Discovery, Integration, Transformation
- Information Retrieval
- Data Classification, Regression, Cleaning
- Smart Cities & Energy
- Social Media, Social Networking, Social Data
- Semantic Computing
- IoT, Autonomous Systems and Agents
- Mobile Computing
- Sensors, Networks, Devices
- Neuroscience and Bioinformatics
- HPCC and Hadoop
- Recent Theory, Trends, Technologies and Applications
- Future Directions and Challenges in Intelligent Data Mining
- Industrial Challenges in Intelligent Data Mining
- Demo Applications
Extended versions of all session papers will be published on the International Journal of Data Mining Science
Papers should be submitted for this special session by Sept 15, 2019, at the conference special session submission system.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://cci.drexel.edu/bigdata/bigdata2019).
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 (email@example.com, firstname.lastname@example.org, email@example.com)
Special Session Organizer:
Asst. Prof. Dr. Uraz Yavanoglu
Department of Computer Engineering
Gazi University, Turkey
The important dates for this special session are:
Full Paper Submission Deadline : Sept 15, 2019 11:59pm PST
Notification of Acceptance : Oct 5, 2019
Camera-ready papers & Pre-registration : Nov 10, 2019, 11:59pm PST
Conference Dates : Dec 9-12, 2019
IEEE Big Data 2019 Special Track on Federated Machine LearningDecember 9-12, 2019 Los Angeles, CA, USA
Privacy and security are becoming a key concern in our digital age. Companies and organizations are collecting a wealth of data on a daily basis. Data owners have to be very cautious while unlocking the values in the data, since the most useful data for machine learning often tend to be confidential. The European Union’s General Data Protection Regulation (GDPR) brings new legislative challenges to the big data and artificial intelligence (AI) community. Many operations in the big data domain, such as merging user data from various sources for building an AI model, will be considered illegal under the new regulatory framework if they are performed without explicit user authorization.
In order to explore how the AI research community can adapt to this new regulatory reality, we organize this special track on Federated Machine Learning (FML). The special track will focus on machine learning and big data analytics techniques with privacy and security. Technical issues include but not limit to data collection, integration, training and modelling, both in the centralized and distributed setting. The special track intends to provide a forum to discuss the open problems and share the most recent and ground-breaking work on the study and application of GDPR compliant machine learning. It will also serve as a venue for networking. Researchers from different communities interested in this problem will have ample time to share thoughts and experience, promoting possible long-term collaborations. Both theoretical and application-based contributions are welcome.
The special track seeks to explore new ideas with particular focus on addressing the following challenges:
- Security and Regulation Compliance: How to meet the security and compliance requirements? Does the solution ensure data privacy and model security?
- Collaboration and Expansion Solution: Does the solution connect different business partners from various parties and industries? Does the solution exploit and extend the value of data while observing user privacy and data security?
- Promotion and Empowerment: Is the solution sustainable and intelligent? Does it include incentive mechanisms to encourage parties to participate on a continuous basis? Does it promote a stable and win-win business ecosystem?
We welcome submissions on recent advances in privacy-preserving, secure machine learning and artificial intelligence systems. All accepted papers will be presented during the conference. At least one author of each accepted paper is expected to register for and attend the conference. Topics include but not limit to:Techniques:
- Electrical Engineering
- Adversarial learning, data poisoning, adversarial examples, adversarial robustness, black box attacks
- Architecture and privacy-preserving learning protocols
- Federated learning and distributed privacy-preserving algorithms
- Human-in-the-loop for privacy-aware machine learning
- Incentive mechanism and game theory
- Privacy aware knowledge driven federated learning
- Privacy-preserving techniques (secure multi-party computation, homomorphic encryption, secret sharing techniques, differential privacy) for machine learning
- Responsible, explainable and interpretability of AI
- Security for privacy
- Trade-off between privacy and efficiency
- Approaches to make AI GDPR-compliant
- Crowd intelligence
- Data value and economics of data federation
- Open-source frameworks for distributed learning
- Safety and security assessment of AI solutions
- Solutions to data security and small-data challenges in industries
- Standards of data privacy and security
Electronic submission of full papers: Sep 15, 2019
Notification of paper acceptance: Oct 16, 2019
Camera-ready of accepted papers: Nov 10, 2019
Conference: Dec 9-12, 2019
Please submit a full-length paper (up to 10 page IEEE 2-column format) through the online submission system.
Paper Submission Page
Papers should be formatted according to the IEEE Computer Society Proceedings Manuscript Formatting Guidelines (https://www.ieee.org/conferences/publishing/templates.html)
8.5" x 11" (DOC, PDF)
LaTex Formatting Macros
Please send all enquiries about the Special Track on Federated Machine Learning to one or both of the Special Track Co-Chairs, Yang Liu (firstname.lastname@example.org) and Han Yu (email@example.com).
Information Granulation in Data Science and Scalable ComputingDecember 9-12, 2019, Los Angeles, CA, USA
Granular Computing is a general computation approach for effectively using information granules such as classes, clusters, sets, groups, intervals and networks to build an efficient computational model for complex applications with huge amounts of data, information and knowledge (BIG Data). Though the label is relatively recent, the notions and principles of Granular Computing and Information Granulation, under different names, have appeared in many related fields, such as information hiding in programming, granularity in artificial intelligence, divide and conquer paradigms in theoretical computer science, interval computing, cluster analysis, fuzzy and rough set systems, neuro computing, quotient spaces, belief functions, approximate analytics, approximate computing, deep neural networks and many others. Thus, "Information granule = Fundamental of Human Knowledge": As a meta-mathematical methodology, computation of information granules gives a theoretical framework of big data analytics.
Special Session on Information Granulation in Data Science and Scalable Computing will continue to address the theory and practice of computation of information granules. It will provide researchers from universities, laboratories and industry with the means to present state-of-the-art research results and methodologies for information granules. The session will also make it possible for scientists and developers to highlight their new research directions and new interactions with novel computing models. The session will focus particularly on currently important research tracks such as social network computing, cloud computing, cyber-security, data mining, machine learning, knowledge management, intelligent systems and soft computing (neural networks, fuzzy systems, evolutionary computation, rough sets, self-organizing systems), e-Intelligence (Web intelligence, semantic Web, Web informatics), bioinformatics and medical informatics.
-- The session is organized as a part of the IEEE Big Data 2019 conference (December 9-12, Westin Bonaventure Hotel & Suites, Los Angeles, CA, USA), which is a well-established and highly competitive international event targeted at modern trends in big data processing and analytics.
-- The session is intended to be a forum for discussing concepts, issues, and methods by the leading researchers in the fundamental problems of Information Granulation and Granular Computing in an atmosphere promoting the exchange of ideas and viewpoints.
-- Papers accepted to the session will be published in the IEEE Big Data 2019 conference proceedings, together with papers submitted and accepted to the main conference track.
-- Organizers are planning a special issue on mathematical framework of big data analytics in some journal.
Submission Deadline: September 21, 2019
Acceptance Notification: October 19, 2019
Camera-Ready: November 9, 2019
Special Session Organizer:
Department of Medical Informatics, Faculty of Medicine,
Shimane University, Japan
Institute of Informatics,
University of Warsaw
Department of Computer Science and Information Engineering
National University of Kaohsiung
S. L. Wang
Department of Information Management,
National University of Kaohsiung
School of Information Science and Technology,
2nd Special Session on HealthCare Data in IEEE Big Data 2019December 9-12, 2019, Los Angeles, CA, 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 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 improving quality of care. “To become smarter” requires an 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 2019 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 the 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 Information Systems
Pervasive Healthcare Information Systems and Services
Process Management in Health Informatics Systems
Health Decision Support Systems
Special Session Organizer:
Dr. Sultan Turhan
Department of Computer Engineering, Galatasaray University
Dr. Ozgun Pinarer
Department of Computer Engineering, Galatasaray University
Abstract submission: September 7, 2019
Full paper submission: September 14, 2019
Notification of paper acceptance: Oct 12, 2019
Camera-ready of accepted papers: Nov 9, 2019
Conference: Dec 9-12, 2019
Papers should be submitted as a PDF in 2-column IEEE format. Detailed instructions for the authors can be found at the conference website (http://bigdataieee.org/BigData2019/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 (http://bigdataieee.org/BigData2019/).
If you have any question about the special session, please do not hesitate to contact us.
Machine Learning on Big Data (MLBD 2019)December 9-12, 2019, Los Angeles, CA, USA
The Special Session “Machine Learning on Big Data” (MLBD 2019) of the 2019 IEEE International Conference on Big Data (IEEE BigData 2019) follows the great success of three previous editions co-located with the 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 2019 special session focuses on all the research aspects of machine learning on Big Data. Among these, an unrestricted list includes:
The Special Session “Machine Learning on Big Data” (MLBD 2019) of the 2019 IEEE International Conference on Big Data (IEEE BigData 2019) will be held in Los Angeles, CA, USA, during December 9-12, 2019, 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 2019 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 2019 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.
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 10 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”.
Accepted papers will appear in the proper Big Data 2019 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.
Paper submission: September 15, 2019
Notification of acceptance: October 15, 2019
Camera-ready paper due: November 10, 2019
Alfredo Cuzzocrea, University of Calabria, Italy
Michelangelo Ceci, University of Bari, Italy
Alfredo Cuzzocrea, University of Trieste and ICAR-CNR, 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