Data Science relies on the massive volumes of diverse data generated from all forms of human activity and interaction with the environment to make decisions and to solve problems. Traditional methods for managing and processing data have been scaled to address its growth, but new approaches are required to deal with these heterogeneous, high velocity, very large data sources of varying quality, coverage, and semantics. There are challenges at every stage. Addressing these challenges requires innovations in a wide range of computing sub-disciplines, from computer architecture to human computer interaction, and from data analytics to recommendations. ACM Transactions on Data Science (TDS) will serve as the premier forum for describing and advancing the state of the art on this important topic.
The scope of ACM Transactions on Data Science includes cross disciplinary innovative research ideas, algorithms, systems, theory and applications for data science. Papers that address challenges at every stage, from acquisition on, through data cleaning, transformation, representation, integration, indexing, modeling, analysis, visualization, and interpretation while retaining privacy, fairness, provenance, transparency, and provision of social benefit, within the context of big data, fall within the scope of the journal.
By its very nature, data science overlaps with many areas of computer science. However, the objective of the journal is to provide a forum for cross-cutting research results that contribute to data science as defined above. Papers that address core technologies without clear evidence that they propose multi/cross-disciplinary technologies and approaches designed for management and processing of large volumes of data, and for data-driven decision making will be out of scope of this journal.