Towards efficient processing of RDF Data Streams

In the last years, there has been an increase in the amount of real-time data generated. Sensors attached to things are transforming how we interact with our environment. Extracting meaningful information from these streams of data is essential for some application areas and requires processing systems that scale to varying conditions in data sources, complex queries, and system failures. This paper describes ongoing research on the development of a scalable RDF streaming engine.

Reviewing technical approaches for sharing and preservation of experimental data

Empirical Software Engineering (ESE) replication researchers need to store and manipulate experimental data for several purposes, in particular analysis and reporting. Current research needs call for sharing and preservation of experimental data as well. In a previous work, we analyzed Replication Data Management (RDM) needs. A novel concept, called Experimental Ecosystem, was proposed to solve current deficiencies in RDMapproaches. The empirical ecosystem provides replication researchers with a common framework that integrates transparently local heterogeneous data sources. A typical situation where the Empirical Ecosystem is applicable, is when several members of a research group, or several research groups collaborating together, need to share and access each other experimental results. However, to be able to apply the Empirical Ecosystem concept and deliver all promised benefits, it is necessary to analyze the software architectures and tools that can properly support it.