Call for Papers |
|
CALL FOR PAPERS
Learning from Temporal Data (LearnTeD) special session of the 10th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2023) October 9-13, 2023, Thessaloniki, Greece Website link: https://dsaa2023.inesctec.pt/ ---------------------------------------------------------------------------------------- Aims and Scope ---------------------------------------------------------------------------------------- Temporal information is all around us. Numerous important fields, including weather and climate, ecology, transport, urban computing, bioinformatics, medicine, and finance, routinely work with temporal data. Temporal data present a number of new challenges, including increased dimensionality, drifts, complex behavior in terms of long-term interdependence, and temporal sparsity, to mention a few. Hence, learning from temporal data requires specialized strategies that are different from those used for static data. Continuous cross-domain knowledge exchange is required since many of these difficulties cut over the lines separating various fields. This special session aims to integrate the research on learning from temporal data from various areas and to synthesize new concepts based on statistical analysis, time series analysis, graph analysis, signal processing, and machine learning. The scope of the special session includes but is not limited to the following: - Temporal data clustering - Classification and regression of univariate and multivariate time series - Early classification of temporal data - Deep learning for temporal data - Learning representation for temporal data - Metric and kernel learning for temporal data - Modeling temporal dependencies - Time series forecasting - Time series annotation, segmentation, and anomaly detection - Spatial-temporal statistical analysis - Functional data analysis methods - Data streams - Interpretable/explainable time-series analysis methods - Dimensionality reduction, sparsity, algorithmic complexity, and big data challenges - Benchmarking and assessment methods for temporal data - Applications, including transport, urban computing, weather and climate, ecology, bio-informatics, medical, and energy consumption on temporal data ---------------------------------------------------------------------------------------- Submission procedure ---------------------------------------------------------------------------------------- All papers should be submitted electronically via EasyChair (under the “Special Session” Track): https://easychair.org/my/conference?conf=dsaa2023 The length of each paper submitted to the Research tracks should be no more than ten (10) pages and should be formatted following the standard 2-column U.S. letter style of the IEEE Conference template. For further information and instructions, see the IEEE Proceedings Author Guidelines. All submissions will be blind-reviewed by the Program Committee on the basis of technical quality, relevance to the conference’s topics of interest, originality, significance, and clarity. Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with paper formatting and authors’ anonymity will be rejected without reviews. Because of the double-blind review process, non-anonymous papers that have been issued as technical reports or similar cannot be considered for DSAA’2023. An exception to this rule applies to arXiv papers that were published in arXiv at least a month prior to the DSAA’2023 submission deadline. Authors can submit these arXiv papers to DSAA provided that the submitted paper’s title and abstract are different from the one appearing in arXiv. All accepted full-length special session papers will be published by IEEE in the DSAA main conference proceedings under its Special Session scheme. All papers will be submitted for inclusion in the IEEEXplore Digital Library. High-quality accepted papers will be recommended to a Special Issue of the International Journal of Data Science and Analytics on "Learning from temporal data" through a fast-track process. ---------------------------------------------------------------------------------------- Important Dates ---------------------------------------------------------------------------------------- Paper Submission Deadline: May 29, 2023 Paper Notification: July 17, 2023 Camera-ready Submission: August 7, 2023 ---------------------------------------------------------------------------------------- Organizing Committee ---------------------------------------------------------------------------------------- ---------------------- Track Chairs ---------------------- Albert Bifet, Waikato University, New Zealand João Mendes Moreira, University of Porto & LIAAD-INESC TEC, Portugal Joydeep Chandra, Indian Institute of Technology Patna, India ---------------------- Program Committee ---------------------- Animesh Chaturvedi, IIIT Dharwad, India Balaraman Ravindran, IIT Madras, India Bivas Mitra, IIT Kharagpur, India Carlos Abreu Ferreira, INESC TEC, Portugal Debraj Das, IIT Bombay, India Heitor Murilo Gomes, Victoria University of Wellington, New Zealand Ingo Scholtes, University of Würzburg, Germany Maria Eduarda Silva, Universidade do Porto, Portugal Mirco Nanni, ISTI-CNR, Italy Nuno Moniz, University of Notre Dame, USA Paulo Cortez, Universidade do Minho, Portugal Raquel Menezes, Universidade do Minho, Portugal Rita Ribeiro, Universidade do Porto, Portugal Sourangshu Bhattacharya, IIT Kharagpur, India Srijith P.K., IIT Hyderabad, India Vitor Cerqueira, Dalhousie University, Canada ---------------------- Publicity Chairs ---------------------- Carlos Abreu Ferreira, Instituto Politécnico do Porto, Portugal Shruti Saxena, Indian Institute of Technology Patna, India ---------------------- Contacts ---------------------- Organizing Committee Contact Person: [email protected] ---------------------- |
Credits and Sources |
| [1] LearnTeD 2023 : Learning from Temporal Data - DSAA 2023 |
