A Categorical Approach in Handling Event-Ordering in Distributed Systems

Feature Extraction and Feature Selection : Reducing Data Complexity with Apache Spark

“…By following the proposed approach, we managed to achieve our primary objectives: reduce computational time, reduce data complexity and provide solutions to interoperability issues, while analyzing vast amount of heterogeneous data from different sources. The approach can be formalized in the next steps by utilizing novel structures derived from the theory of categories as it has been presented in [10] towards an overall optimization, in terms of quantitative and qualitative metrics.…”

“…By solving such inconsistencies, we achieve to further reduce data complexity as well as to reduce for data analysis. In [10] we have presented an approach to utilizing means derived by the theory of categories.…”

“…• different representations of date attributes • namespace inconsistencies (e.g. attributes with names such as prot, protocol, connectionProtocol) • cope with complex structures of different number of inner levels • deal with event ordering and time-inconsistencies (as it is described in [10])…”