DataBench, project funded by the European Horizon 2020 Programme, participated at the annual KDD Conference 2018 at London, the premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data, from the 19th to the 23rd of August.

At the Project Showcase Track, which offered a full day agenda focused exclusively on innovative KDD-relevant projects from different funding programs, organizers looked up on bringing together a diverse community of researchers in Machine Learning and Data Analytics to show the state-of-the-art in research and applications in this field.

Marko Grobelnik, Member of the DataBench Consortium and Researcher at the AI Lab from the Institut Jozef Stefan in Slovenia, presented “DataBench: Evidence Based Big Data Benchmarking to Improve Business Performance” focusing on how organizations rely on evidence from the Benchmarking domain to provide answers on how processes are performing but with a lack of objective, evidence-based methods, to measure the correlation between Big Data Technology (BDT) benchmarks and an organization’s business benchmarks demonstrating return on investment (ROI). The DataBench project addresses this gap in the current benchmarking community’s activities evaluating both technical and business metrics.

In a more general way. Nuria de Lama, member of the DataBench project and Vice-Secretary General of BDVA was also invited as keynote speaker to share experiences about the European Funding ecosystem and future opportunities in Big Data.

For more information: Presentation Abstract – KDD Conference / Nuria de Lama – Presentation (VIDEO) / Marko Grobelnik – Presentation (Video)

About DataBench
At the heart of DataBench is the goal to design a benchmarking process helping European organizations developing BDT to reach for excellence and constantly improve their performance, by measuring their technology development activity against parameters of high business relevance.

DataBench will investigate existing Big Data benchmarking tools and projects, identify the main gaps and provide a robust set of metrics to compare technical results coming from those tools.

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