Big Data Analytics

Abstract

Big Data has become a core topic in different industries and research disciplines as well as for society as a whole. This is because the ability to generate, collect, distribute, process and analyze unprecedented amounts of diverse data has almost universal utility and helps to fundamentally change the way industries operate, how research can be done and how people live and use modern technology. Different i ndustries such as automotive, finance, h ealthcare o r manufacturing, c an dramatically benefit f rom improved and f aster data analysis, e .g., as illustrated by current industry trends like “Industry 4.0” and “Internet of Things”. Data-driven research approaches utilizing Big Data technology and analysis become increasingly commonplace, e.g., in the life sciences, geo sciences or in astronomy. Users utilizing smartphones, social media, and web resources spend increasing amounts of time online, generate and consume enormous amounts of data and are the target for personalized services, recommendations and advertisements. Most of the possible developments related to Big Data are still in an early stage but there is great promise if the diverse technological and application-specific challenges in managing und using Big Data are successfully addressed. Some of the technical challenges have been associated to different “V” characteristics, in particular Volume (support of very high data volumes), Velocity (fast analysis of data streams), Variety (support for diverse kinds of data) and Veracity (support for high data quality). Other challenges relate to the protection of personal and sensitive data to ensure a high a degree of privacy and the ability to turn the huge amount of data into useful insights or improved operation. A key enabler for the Big Data movement are increasingly powerful and relatively inexpensive computing platforms allowing the fault-tolerant storage andprocessing of petabytes of datawithin large computing clusters typically