We have experienced a rapidly increasing growth of data and information. A proliferation of automated systems, small scale computing devices, sensor networks, and data capture technologies has contributed to the production of large volumes of data. Data set growth outpaces available storage capacity. The focus of data processing and analysis has moved from offline batch processing of data to the incremental handling of online data streams. Online data streams may originate from sources such as mobile devices, autonomous vehicles, industrial monitoring, financial and meteorological systems, health care, stock market, web traffic and clickstreams, to name a few. Their prominence in real-world systems, along with the necessity of modeling, analyzing, and understanding these systems, has brought new challenges, greater demands, and new research directions. Data stream modeling is fundamentally based on incremental learning methods that process data continuously as an attempt to find similarities in spatio-temporal features and, thereafter, provide insights about the phenomenon that governs the data. Data streams are characterized by nonstationarity, nonlinearity, and heterogeneity; they are potentially endless and may be subject to changes of various kinds. Direct application of computational intelligence and learning algorithms to data streams is very often infeasible because it is difficult to maintain all the data in memory. A particular challenge faced in stream modeling concerns how to handle the inherent uncertainty in the data. 

The key research questions addressed in this Workshop are (i) how to obtain accurate and interpretable models from uncertain data streams; and (ii) how to exploit uncertainty and vague reasoning to better explain adaptive models at any time step. 

The workshop aims at bringing together theorists and practitioners who apply (online) soft computing methods for sequential (and uncertain) data analysis to exchange and discuss ideas that enrich traditional approaches, e.g., computational methods for static datasets. The workshop gets together experts from different research communities including (but not limited to):

• incremental learning from stream data, 

• soft methods for stream data,

• fuzzy statistics, 

• recursive processing of Big data, 

• uncertainty modeling, 

• evolving neural and neuro-fuzzy networks.

OLUD workshop intends to facilitate interdisciplinary discussion on recent advancements in state-of-the-art online learning and pattern recognition methods as well as their use in applied domains.

Submission Guidelines

There are two types of publications related to the WCCI OLUD Workshop: 

• Regular paper: Current,  unpublished work that is being presented for the first time. The length of a submission is at least 10 pages  A standard-length time slot will be allowed for the presentation as well as for a subsequent discussion

• Position paper (ideas and research highlights):  New  research  directions,  opinions,  position  talks:  promising  or interesting work that does not fit the standards for conventional publication. The length of a submission is 5-9 pages A shorter-length time slot will be allowed for the presentation, with more room for the subsequent discussion.

The paper must be written in English and submitted in PDF via the EasyChair system  

Authors are required to use a uniform style for the papers. The ceurart styles can be found are:

• Latex (we strongly encourage the authors to use the latex format, Overleaf Template is also available)

• Word/Opendocument

We encourage authors to include their ORCIDs in their papers.

Important dates

• Title and authors submission: June 1, 2022

• Paper submission: June 15, 2022 

• Notification of acceptance: June 22, 2022

• Final paper submission: June 29, 2022

• Workshop date: July 18, 2022

Committees

Organizing committee

• Gabriella Casalino, University of Bari, Italy
• Giovanna Castellano, University of Bari, Italy
• Katarzyna Kaczmarek-Majer, Polish Academy of Sciences, Poland 
• Daniel Leite, Federal University of Lavras, UFLA, Brazil

Program Committee

• Sašo Blažič  (University of Ljubljana, Slovenia)
• Przemysław Grzegorzewski (Warsaw University of Technology, Poland)
• Leandro Maciel (University of São Paulo, Brazil) 
• Corrado Mencar (Università degli Studi di Bari Aldo Moro, Italy)
• Zied Mnasri (University of Tunis El Manar, Tunisia)
• Daniel Peralta (Ghent University, Belgium)
• Mahardika Pratama (Nanyang Technological University, Singapore)
• Seiichi Ozawa (Kobe University, Japan)
• Sławomir Zadrożny (University of Warsaw, Poland)
• Choiru Za'in (Monash University, Australia)

Invited Speakers

• Plamen Angelov

Publication

All papers submitted to OLUD workshop will be reviewed by independent reviewers, and upon acceptance will be submitted to CEUR Workshop Proceedings for publication,  under a CC-BY 4.0 license (http://ceur-ws.org/). This means that proceedings will be free of charge, as well as of author publication charges, will be open-access, and copyright will be retained by authors.  CEUR-WS proceedings are usually indexed in Scopus.

Post-workshop special issue in a well-reputed journal (details TBA) inviting extensions of accepted papers will be announced. 

Venue

The conference will be held in Padua within the IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE - WCCI2022 (https://wcci2022.org/) 

Contact

All questions about submissions should be emailed to gabriella.casalino@uniba.it