The rapid growth of multimodal data—such as text, images, sensor streams, graphs, and structured records—has made cross-modal integration critical for modern large-scale data analysis. However, the heterogeneous nature of multimodal sources and the limitations of conventional fusion techniques hinder effective semantic alignment, representation learning, and scalable analytics.

Although large language models (LLMs) offer strong capabilities in reasoning, abstraction, and cross-domain understanding, current data pipelines still lack efficient mechanisms to incorporate LLM-driven semantics into multimodal fusion workflows. This thematic series aims to bridge this gap by exploring innovative approaches that leverage LLMs to enhance multimodal data fusion and enable more powerful, comprehensive data-driven insights.

This collection focuses on advancing LLM-Augmented Multimodal Data Fusion for Large-Scale Data Analysis, encouraging research on:

LLM-enhanced representation learning Semantic alignment across heterogeneous modalities Generative or retrieval-assisted fusion strategies Scalable system designs for real-world applications The goal is to promote new analytical paradigms where LLM-driven intelligence reshapes multimodal integration and utilization in complex scientific and industrial ecosystems.

The topics include, but are not limited to:

LLM-augmented cross-modal semantic alignment for large-scale analytics

Generative and retrieval-assisted fusion for multimodal data integration

Representation learning for heterogeneous and multi-source data fusion

Knowledge grounding and reasoning across diverse data modalities

Scalable fusion architectures for large-volume multimodal datasets

Foundation-model-assisted modality completion and data annotation

Graph–text–sensor fusion for scientific and engineering data analysis

Temporal–spatial multimodal fusion for real-world big data applications

Self-supervised learning for multimodal representation and alignment

Benchmarking, datasets, and evaluation protocols for multimodal fusion

Efficient fusion mechanisms for high-dimensional industrial and IoT data

Domain-specific multimodal fusion applications for data-driven intelligence

SDG 9: Industry, Innovation & Infrastructure

The Journal of Big Data invites submissions to a Special Issue associated with the 24th Australasian Data Science and Machine Learning Conference (AusDM 2026), to be held in Sydney, Australia, from 2–4 December 2026.

AusDM is the premier Australasian forum for researchers and practitioners in data science, data analytics, data mining, machine learning, and artificial intelligence. Since 2002, the conference has showcased advances in algorithms, systems, software, and real-world applications while fostering collaboration across academia, industry, and government.

This Special Issue welcomes substantially extended versions of selected papers presented at AusDM 2026. Submissions must include significant new research beyond the conference version, such as novel methodologies, additional experiments, expanded analyses, deeper theoretical or empirical insights, or new application studies.

Topics include, but are not limited to:

• Machine learning, deep learning, generative AI, large language models, multimodal learning, reinforcement learning, transfer learning, and federated learning
• Data mining, knowledge discovery, learning from structured, unstructured, graph, temporal, spatial, multimedia, IoT, and sensor data
• Data-centric AI, data engineering, privacy-preserving data mining, and large-scale data management
• Big data analytics, scalable and distributed machine learning, data stream mining, edge and cloud AI, and real-time analytics
• Visual analytics, explainable and interpretable AI, causal machine learning, responsible AI, fairness, transparency, and trustworthy AI
• Applications of data science and AI in business, finance, healthcare, education, agriculture, engineering, cybersecurity, environmental science, social sciences, and other domains

All submissions will undergo the journal's standard peer-review process. Invitation to submit does not guarantee acceptance.

Aligned Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure, SDG 4: Quality Education, and SDG 17: Partnerships for the Goals.