2017/07/24 by Longbing Cao · 1 citation
Business, Management and Accounting · Computer Science · Decision Sciences · #Anomaly Detection Techniques and Applications #Big Data and Business Intelligence #Knowledge Management and Technology #cs.CY
paper · pdf · doi:10.1145/3015456
published as Communications of the ACM, Vol. 60 No. 8, Pages 59-68, 2017
openalex publication_date 2017/07/24 · arxiv created 2020/06/28 · arxiv updated 2020/07/01 · openalex created_date 2020/11/23 · openalex updated_date 2026/07/14
While data science has emerged as a contentious new scientific field, enormous debates and discussions have been made on it why we need data science and what makes it as a science. In reviewing hundreds of pieces of literature which include data science in their titles, we find that the majority of the discussions essentially concern statistics, data mining, machine learning, big data, or broadly data analytics, and only a limited number of new data-driven challenges and directions have been explored. In this paper, we explore the intrinsic challenges and directions inspired by comprehensively exploring the complexities and intelligence embedded in data science problems. We focus on the research and innovation challenges inspired by the nature of data science problems as complex systems, and the methodologies for handling such systems.