Foundational Research

Rapid advances and growth in the connected world across different application domains are fast pacing the data volumes generated from a range of devices. Such high growth is further endorsed by digital transformation, and systematic adoption of advanced Internet of Things (IoT), Cyber Physical Systems, and Smart Things. Data being the centre of innovative systems, its overall realization in solution building depends on several aspects such as the quality of data; data management; computation; knowledge discovery; decision support, etc. ICDDS'24 aims to look broadly at data based innovation and technologies in interdisciplinary areas. The conference is particularly interested in all aspects of data and decision science, applications, solution design, and system challenges in all such new paradigms ranging from theory to applied research

The areas of interest are broadly categorized into the following three streams:

(1) Foundational Research: theory to novel findings in all aspects of Data/Decision science

The topics of interest include, but are not limited to:

• Probabilistic Inference (Bayesian methods, graphical models, Monte Carlo methods, etc.)
• Online learning
• Generative models
• Large Language Models
• Data privacy
• New Computational Models for Big Data
• New Data Standards
• Semantic-based Data Mining and Data Pre-processing
• Learning Theory (bandits, game theory, statistical learning theory, etc.)
• Multi agent systems
• ML and Deep learning
• Web analytics
• Data mining
• Optimization
• Ethics in artificial intelligence
• Social computing and analytics
• Algorithms and systems for database
• ML for embedded platforms
• Recommender systems
• Multimedia processing
• Analytics method and systems
• Text analytics and NLP
• Time-series analysis
• Information retrieval
• Semantic-based Data Mining and Data Pre-processing
• Machine Reasoning and Hybrid AI
• Urban Computing and Analytics

(2) Systems Research: system aspects of data/decision science

The topics of interest include, but are not limited to:

• CPU/GPU architectures for AI/ML applications
• VLSI and architecture design for data science applications
• Compiler optimization using AI/ML
• Systems for resource constrained computing
• Systems for Big data
• Energy efficient computing and Inferencing architectures
• Software techniques and architectures in Cloud/Grid/Stream Computing
• Algorithms and systems for database
• Resource constrained computing architectures

(3) Applications: real-world problems and their solution approach using data/decision science

• Data Science use Cases in areas such as:

• Enterprise: Supply chain, enterprise mobility solution, mobile systems, edge computing, manufacturing, demand forecasting, finance, retail, wireless communication and networking, smart mobility, cyber security.
• Society: Healthcare, education, smart campus, smart city and buildings, energy, social computation, crowd sensing, environmental policy, climate change and control, internet of personalized things.

• Challenges in Data Science and Applications:
Bias and fairness, Algorithm explainability, safety, Scalability, Transparency, Lessons from real-word deployments, Benchmarking.