PAKDD 2024 solicits novel, high-quality, and original research papers that provide innovative insights into all facets of knowledge discovery and data science, including but not limited to theoretical foundations of mining, inference, and learning, Big Data technologies, as well as security, privacy, and integrity. We also encourage visionary papers on emerging topics and application-based papers offering innovative technical advancements to interdisciplinary research and applications of data science.
Important Dates
- Paper Submission Deadline: November 15, 2023 November 29, 2023
- Paper Acceptance Notification: January 10, 2024 February 1, 2024
- Camera Ready Papers Due: February 5, 2024 February 15, 2024
*All deadlines are 23:59 Pacific Standard Time (PST)
Topics
Topics of relevance for the conference include, but are not limited to, the following:
Theoretical Foundations of Mining, Inference, and Learning Methods and Algorithms
- Generative AI, quantum ML, decision-focussed learning, neuro-symbolic methods and reasoning, non-IID learning, and OOD generalization
- LLM, enlarged language models, retrieval-augmented text generation, vision-language pretraining, and vision transformers
- Association rule, classification, clustering, indexing, pattern mining, query analysis, and query processing
- Data/entity/event/relationship extraction, integration, cleaning, and summarization
- Anomaly and outlier detection, information retrieval and search, natural language processing, question answering, and recommendation systems
- Asymptotic analysis, intelligence analysis, online and streaming algorithms, and optimization methods
- Dimensionality detection and model selection, feature extraction and selection, kernel methods, matrices and tensors, neural network architectures, neuronal diversity, polynomial nets, hyperbolic neural networks, probabilistic models and statistical inference, and regression
- Deep and representation learning, reinforcement learning, relational/structured learning, Gromov-Wasserstein learning, semi-supervised learning, and unsupervised learning
- Few-shot learning, transfer learning, self-supervised learning, and meta learning
- Model misspecification detection, interpretability, explainability, safe learning, and control
- Measurements, evolution, and models of graphs and networks
- Social network/media analysis and dynamics, reputation, influence, trust, opinion mining, sentiment analysis, link prediction, and community detection
- Methods for detecting and combating spamming, trolling, aggression, toxic online behaviors, bullying, hate speech, and low-quality and offensive content
- Semi-structured data, text, web, and social media mining
- Spatio-temporal, time-series, sequential, and streaming data mining
- User activity and behavior data analysis, exploitation, and modeling
- Robustness, spurious correlations, invariance, stability, online verification, sampling, benchmarking, experiments, and evaluations
Big Data Technologies
- Learning-based data cleaning and preparation
- Algorithmically efficient data transformation and integration and the trade-off between the complexity of the transformation and algorithmic efficiency
- Novel algorithmic and statistical techniques for scalable and high-performance big data mining
- Parallel and distributed data mining and searching on emerging architectures
- Analysis, sampling, evaluation, and search in big data and large-scale systems
- Collaborative search, sponsored search, voice search, conversational search, zero-query and implicit search, semantic search, and faceted search
- Human computation and crowdsourcing of big data
- User interfaces and visual analytics of big data analysis
Security, Privacy, Ethics, Information Integrity, and Social Issues
- Modeling credibility, trustworthiness, and reliability
- Differences between human creativity and AI creativity
- Privacy-preserving data mining and privacy models
- Philosophical and ethical issues in data mining, transparency, auditing bias, algorithmic bias, and fairness models
- Sources, origins, prevalence, and virality of misinformation, and monitoring and detection of misinformation
- Social issues, such as early childhood development, health inequities, mental health and well-being support systems, online education systems, social development, and poverty
- Climate, ecological, and environmental science, and resilience and sustainability
Interdisciplinary Research and Applications of Data Science
- Symbiotic human-AI interaction, human-agent collaboration, and socially interactive robots
- Internet of Things, logistics management, network traffic and log analysis, and supply chain management
- Business and financial data, computational advertising, customer relationship management, intrusion and fraud detection, and intelligent assistants
- Medical and public health applications, drug discovery, healthcare management, and epidemic monitoring and prevention
- Astronomy and astrophysics, genomics and bioinformatics, high energy physics, robotics, AI-assisted programming, and scientific data
Paper Submission
Submissions of papers must be conducted in English. The Program Committee will undertake a double-blind review of all papers based on their technical merit, relevance to the field of data mining, originality, significance, and clarity. Every submitted paper should include an abstract of no more than 200 words and should not exceed 12 single-spaced pages using a 10pt font size, including references, appendices, etc. Authors are instructed to follow the Springer LNCS/LNAI manuscript submission guidelines for their submissions.
All papers must be electronically submitted via the CMT paper submission system and only in PDF format. Although authors can submit supplementary material in a separate PDF file, the reviewers are not obliged to consider it. Any papers failing to adhere to the Submission Policy will be rejected without review. Submitting a paper signifies that if the paper is accepted, at least one author will undertake regular registration and present the paper.
All submissions to PAKDD must be original work and not be under review or published in any other conference or journal. Submissions of papers must conform to the double-blind review policy, requiring the removal of any information identifying the author(s) from the main manuscript and any supplementary files. In discussing previous work, authors should reference their own studies in the third person and include all appropriate citations.
Formatting Template
https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
All manuscripts should strictly adhere to the aforementioned format for preparation and submission. Deviation from this format may result in the disqualification of the paper from the conference.
Contact Information
De-Nian Yang (Academia Sinica)
Xing Xie (Microsoft Research Asia)
Program Co-Chairs of PAKDD2024
