We invite submission of papers describing innovative and original research contributions on all aspects of data management, data science, machine learning, AI and their applications focussing on one or more aspects of Theory, System Design, Experiments and Benchmarking, Scalability, Algorithms, etc. The research track invites submissions in the form of full (8 pages + 2 pages for references) as well as short (4 pages + 1 page for references) papers. The goal of short papers is to provide a venue for relatively simpler but innovative ideas such as engineered solutions, exciting work-in-progress or even negative results that would be interesting to the broader community.

Authors of accepted papers will get an opportunity to showcase their work as an oral presentation. Accepted papers will appear in the proceedings of the conference, which will be published in ACM Digital Library.

Topics of interest include, but are not limited to:

    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: 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 visualization.
    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