Thanks to crowdsourcing platforms, several problems that seemed impossible to tackle from a machine learning perspective, can now be solved. To discover how crowdsourced data is used as well as what are the main problems derived from its use, we analyze many applications in several fields of interest.

Zooming in a portion of a network of profesional relationships (relations between the cicles) as well as other relations between subjects and objects of a system to protect. Relationship‐based access control policies are defined as paths between nodes that the network must have. For example, the secretary at any department has access to salary information of any employee that reports to the manager of the department: Bob can access salary information of Doug because of the paths (Bob, secretary_at, Wireless Dept) and (Doug, reports_to, Alice, manages, Wireless Dept).

Evaluating Big Data processing tools: Drill, HAWQ, Hive, Impala, Presto, and Spark using TPC‐H benchmark with 10 GB, 50 GB, and 100 GB datasets.

Optimal number of factors in exploratory factor analysis applied in data mining.

We present a state‐of‐the‐art review on off‐line and on‐line hotel recommendation supported by crowdsourced ratings, which covers profiling (single and multicriteria rating together with trust & reputation modeling), prediction, and evaluation.

Overview of the experimental setup utilizing OBDA