Research Statement

The wealth of existing data, often referenced both in space and time, enable novel classes of applications and services of high societal and economic impact. Modern applications, including social networks, scientific databases, bioinformatics, open governmental data, produce vast volumes of data that are often publicly available, although quite diverse, unrelated and raw.

This poses important challenges for next-generation data management systems, targeting to turning data into knowledge, aiming to affect many important sectors of human life.

Our goal is to address the challenging problems related to the wealth data, by advancing research and producing solutions to real world problems related to efficient and scalable management of Big Data (gathering and cleansing data, storing and indexing data, analyzing, and mining data). Our view of processing big data includes both, batch-style analytical processing and real-time processing.
Emerging issues, such as semantic- and privacy-aware querying and mining, as well as distributed processing of data and integration of data sources through scalable solutions for semantic enrichment and alignment, are in focus.


The Piraeus AIS Dataset for Large-scale Maritime Data Analytics
The Piraeus AIS Dataset for…
May 11, 2022
AIS data collected by the University of Piraeus' AIS receiver Contributors: Andreas Tritsarolis; Yannis Kontoulis; Yannis Theodoridis Data Science Laboratory, University of Piraeus ...
May 11, 2022
A Python-based library for interactive spatio-temporal data visualization. Overview ST_Visions (Spatio-Temporal Visualizations) is a python library, able to interactively vis...
MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel Trajectories
MaSEC: Discovering Anchorages and Co-movement…
May 11, 2022
Video Presentation: Contributors: Andreas Tritsarolis, Yannis Kontoulis, Nikos Pelekis and Yannis Theodoridis Data Science Laboratory, University of Piraeus Related Papers: MaS...