Big Data and Big Data analytics have attracted major interest in research and industry and continue to do so. The high demand for capable and scalable analytics in combination with the ever increasing number and volume of application scenarios and data has lead to a large and intransparent landscape full of versions, variants and individual algorithms. As this zoo of methods lacks a systematic way of description, understanding is almost impossible which severely hinders effective application and efficient development of analytic algorithms. To solve this issue we propose our concept of modular analytics that abstracts the essentials of an analytic domain and turns them into a set of universal building blocks. As arbitrary algorithms can be created from the same set of blocks, understanding is eased and development benefits from reusability.
This work was, in part, funded by the German Federal Ministry of Education and Research (BMBF) in the context of the project “ScaDS – Competence Center for Scalable Data Services and Solutions Dresden/Leipzig” (01IS14014A).
©2016 Walter de Gruyter Berlin/Boston