Modern big data frameworks (such as Hadoop and Spark) allow multiple users to do large-scale analysis simultaneously, by deploying data-intensive workflows (DIWs). These DIWs of different users share many common tasks (i.e, ...

Ad-hoc analysis implies processing data in near real-time. Thus, raw data (i.e., neither normalized nor transformed) is typically dumped into a distributed engine, where it is generally stored into a hybrid layout. Hybrid ...

Modern organizations typically store their data in a raw format in data lakes. These data are then processed and usually stored under hybrid layouts, because they allow projection and selection operations. Thus, they allow ...

A concrete classification algorithm may perform differently on datasets with different characteristics, e.g., it might perform better on a dataset with continuous attributes rather than with categorical attributes, or the ...

Modern organizations produce and collect large volumes of data, that need to be processed repeatedly and quickly for gaining business insights. For such processing, typically, Data-intensive Flows (DIFs) are deployed on ...