HDMS24 : Time Series Visualisation with Error Limit Guarantees within ExtremeXP

We are delighted to share with you details of the recent participation of Stavros Maroulis, leader of T4.1, at the 18th Hellenic Symposium on Data Management (HDMS24), held in Athens, Greece.

At this prestigious event, Stavros Maroulis presented the work carried out as part of ExtremeXP’s WP4, focusing on the visualisation of time series with guaranteed error limits.  The HDMS (Hellenic Data Management Symposium) is the major annual event for the Greek data management research community. It brings together researchers, professionals and academics to discuss recent advances and current challenges in the field of data management. This year, the symposium was held in Athens, attracting experts from all over the world. In his presentation, Stavros Maroulis explained the methods developed to visualise time series with guaranteed error limits. He detailed how these techniques make it possible to maintain high accuracy in graphical representations of data while reducing the possible margins of error. This approach is particularly beneficial for companies and researchers who depend on the accuracy of data to make informed decisions. Stavros Maroulis’ presentation was well received by the community of researchers and professionals present at HDMS24. Not only did it highlight ExtremeXP’s significant contributions to the field of data visualisation, but it also opened up new perspectives for the application of these techniques in various industries. The participation of Stavros Maroulis at HDMS24 and the presentation of ExtremeXP WP4 work have reinforced our commitment to innovation and excellence in data management.  We’re proud of the progress we’ve made and excited about the future possibilities our research offers. We would like to thank all the participants and the organisers of HDMS24 for this exceptional opportunity to share our work and collaborate with experts in the field.
The ExtremeXP project is co-funded by the European Union Horizon Program HORIZON-CL4-2022-DATA-01-01, under Grant Agreement No. 101093164
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