Enhancing Big Data Warehousing for Efficient, Integrated and Advanced Analytics

The existing capacity to collect, store, process and analyze huge amounts of data that is rapidly generated, i.e., Big Data, is characterized by fast technological developments and by a limited set of conceptual advances that guide researchers and practitioners in the implementation of Big Data systems. New data stores or processing tools frequently appear, proposing new (and usually more efficient) ways to store and query data (like SQL-on-Hadoop). Although very relevant, the lack of common methodological guidelines or practices has motivated the implementation of Big Data systems based on use-case driven approaches. This is also the case for one of the most valuable organizational data assets, the Data Warehouse, which needs to be rethought in the way it is designed, modeled, implemented, managed and monitored. This paper addresses some of the research challenges in Big Data Warehousing systems, proposing a vision that looks into: (i) the integration of new business processes and data sources; (ii) the proper way to achieve this integration; (iii) the management of these complex data systems and the enhancement of their performance; (iv) the automation of some of their analytical capabilities with Complex Event Processing and Machine Learning; and, (v) the flexible and highly customizable visualization of their data, providing an advanced decision-making support environment.