14th International Conference on Artificial Intelligence, Data Mining & Knowledge Management (AIDMK 2026)

Memo

14th International Conference on Artificial Intelligence, Data Mining & Knowledge Management (AIDMK 2026) June 27 ~ 28, 2026, Copenhagen, Denmark https://bdml2026.org/dkmp/index SCOPE 14th International Conference on Artificial Intelligence, Data Mining & Knowledge Management (AIDMK 2026) serves as a premier global forum for presenting and discussing the latest innovations, research findings, and emerging trends across the rapidly evolving fields of AI driven data mining and knowledge management. As organizations increasingly rely on intelligent systems to extract insights, automate decision-making, and manage vast volumes of complex data, the need for advanced methodologies and deeper theoretical understanding has never been greater. Topics of interest Foundations of Data Mining, Machine Learning & AI Scalable, Parallel, and Distributed Data Mining Algorithms Mining Data Streams, Real Time Analytics, and Online Learning Graph Mining, Network Science, and Knowledge Graph Mining Spatial, Temporal, and Spatio Temporal Data Mining Text, Multimedia, Video, and Multimodal Data Mining Web Mining, Social Media Mining, and Behavioral Analytics Feature Engineering, Data Cleaning, and Pre Processing Pipelines Robust Learning from Noisy, Incomplete, or Low Quality Data Explainable AI (XAI), Interpretable Models, and Transparent Mining Privacy Preserving Data Mining (DP, MPC, Federated Analytics) Adversarial Machine Learning, Robustness, and Secure Data Mining Advanced AI & Machine Learning for Knowledge Discovery Deep Learning Architectures for Knowledge Extraction Foundation Models, LLMs, and Multimodal AI for Data Mining Retrieval Augmented Generation (RAG) and LLM Integrated Knowledge Discovery Self -Supervised, Weakly Supervised, and Semi Supervised Learning Reinforcement Learning, Sequential Decision Making, and Policy Learning Causal Discovery, Causal Inference, and Counterfactual Reasoning AutoML, Neural Architecture Search, and Automated Knowledge Discovery Generative AI, Synthetic Data, and Data Augmentation Trustworthy AI: Fairness, Bias Mitigation, and Responsible Data Mining Big Data Platforms, AI Systems & Scalability Distributed Data Processing Frameworks (Spark, Flink, Ray, etc.) High Performance Data Mining on Cloud, Edge, and Hybrid Systems AI Systems: Training, Serving, and Optimization for Large Models Data Mining on GPUs, TPUs, and Accelerator Rich Architectures Streaming Systems, Event Driven Pipelines, and Real Time Analytics Data Lakehouse Architectures and Large Scale Data Management Scalable ML Systems, MLOps, and End to End AI Pipelines Benchmarking, Performance Evaluation, and Optimization of AI/ML Systems Knowledge Management, Reasoning & Representation Knowledge Representation, Ontologies, and Semantic Technologies Knowledge Graph Construction, Completion, Alignment, and Reasoning Integration of Data Warehousing, OLAP, and Modern Data Platforms Knowledge Discovery Frameworks, Pipelines, and Process Automation Pre and Post Processing for KDD: Summarization, Explanation, Validation Interactive Data Exploration, Visualization, and Human in the Loop Mining Languages, Interfaces, and Tools for Knowledge Management Mining and Reasoning over Foundation Model Outputs AI Driven Knowledge Management and Enterprise Intelligence Applications of Data Mining, Knowledge Management & AIBioinformatics, Genomics, and Computational Biology Healthcare Analytics, Medical AI, and Clinical Decision Support Financial Modeling, Fraud Detection, and Risk Analytics Cybersecurity Analytics and Threat Intelligence Image, Video, and Sensor Data Analysis Social Network Analysis and Community Detection Educational Data Mining and Learning Analytics Recommender Systems, Personalization, and User Modeling Smart Cities, IoT Analytics, and Cyber Physical Systems Environmental, Climate, and Sustainability Data Mining Scientific Discovery, Simulation Driven Analytics, and Digital Twins AI Enhanced Decision Support and Intelligent Automation Emerging Trends & Future Directions in AI Driven Knowledge DiscoveryFederated Learning, Collaborative Mining, and Edge Intelligence Data Centric AI: Data Quality, Curation, Governance, and Lineage Responsible AI: Ethics, Transparency, and Compliance in Data Mining Temporal Knowledge Discovery, Event Forecasting, and Sequence Modeling Graph Neural Networks (GNNs), Graph Transformers, and Dynamic Graph Mining Mining Massive Knowledge Bases and Large Scale Foundation Models AI for Scientific Discovery and Research Acceleration Opportunities, Risks, and Societal Impact of AI Driven Data Mining   Paper Submission Authors are invited to submit papers through the conference Submission System by April 25, 2026. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed). Selected papers from DKMP 2026, after further revisions, will be published in the special issue of the following journal. International Journal of Database Management Systems (IJDMS) International Journal of Data Mining & Knowledge Management Process (IJDKP) International Journal of Web & Semantic Technology (IJWesT) – IS Indexed International Journal of Grid Computing & Applications (IJGCA) Computer Applications: An International Journal (CAIJ) Information Technology in Industry (ITII) Important Dates Second Batch : Submissions after March 23, 2026   Submission Deadline : April 25, 2026 Authors Notification : May 23, 2026 Registration & Camera-Ready Paper Due : May 30, 2026 Contact Us Here’s where you can reach us : This email address is being protected from spambots. You need JavaScript enabled to view it. (or) This email address is being protected from spambots. You need JavaScript enabled to view it.