• Corpus ID: 6794093

Big Data Management Challenges in SUPERSEDE

@inproceedings{Nadal2017BigDM,
  title={Big Data Management Challenges in SUPERSEDE},
  author={Sergi Nadal and A. Abell{\'o} and Oscar Romero and Jovan Varga},
  booktitle={EDBT/ICDT Workshops},
  year={2017}
}
The H2020 SUPERSEDE (www.supersede.eu) project aims to support decision-making in the evolution and adaptation of software services and applications by exploiting end-user feedback and runtime data, with the overall goal of improving the end-users quality of experience (QoE). Such QoE is defined as the overall performance of a system from the point of view of users, which must consider both feedback and runtime data gathered. End-user’s feedback is extracted from online forums, app stores… 
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References

SHOWING 1-3 OF 3 REFERENCES
LabBook: Metadata-driven social collaborative data analysis
TLDR
The key insight is to collect and use more metadata about all elements of the analytic ecosystem by means of an architecture and user experience that reduce the cost of contributing such metadata.
Big Data: Principles and best practices of scalable realtime data systems
TLDR
Big Data describes a scalable, easy to understand approach to big data systems that can be built and run by a small team that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data.
Linked Data - The Story So Far
TLDR
The authors describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked data community as it moves forward.