Joint International International Conference on Data Science and Management of Data (CODS-COMAD)

Topics of interest include, but are not limited to the following:

Data Management: Transaction processing, query processing and optimisation, indexing and storage, distributed data platforms, spatio-temporal databases, RDBMS, NoSQL systems, key-value stores, cloud data management, big data systems, data systems for machine learning, Scientific databases, data cleansing, data provenance, data analytics, data integration, performance benchmarking, database tuning, graph database management, data streams management, uncertain and probabilistic databases, crowdsourcing, data warehousing and OLAP, database usability, data management using modern hardware, security and privacy.
Data Science, Machine Learning and AI: Data discovery, data preprocessing and wrangling, Classification and regression, parallel and distributed learning, semi- and unsupervised learning, matrix and tensor methods, graph mining, network analytics, reinforcement learning, feature engineering, deep learning, Bayesian methods, time series analysis, optimization, graphical models, relational models, text analytics and NLP, information retrieval, knowledge representation, knowledge-based systems, human-in-the-loop learning, planning and reasoning, ML for mobiles and other resource constrained environments, data mining, causality, fairness, accountability and transparency, interpretability, data visualisation.
Applications: Social network analysis, recommender systems, online advertising, bioinformatics, computational neuroscience, systems biology, multimedia processing, crowdsourcing, robotics and autonomous systems, analytics on sensor networks and IoT, computer vision, surveillance/monitoring and anomaly detection in networked systems, urban computing, and technology for emerging markets.