Home

social_final
cloud_final
cities_final
creativity_final-2
big_data_final

Data and Web Science Lab is an active research group engaged in research and innovation on ICT complex data science and engineering technologies, offering end-to-end multi way and multi scope analytics.

Data and Web Science Lab can offer end-to-end solutions from raw data to insightful results and from low-level data management to advanced algorithms and applications. It fosters strong international collaborations with multi-disciplinary synergies (engineering, psychology, social sciences, arts, cultural studies, etc) providing data protection and EU GDPR consultancy and technical support. Our group consists of vibrant researchers and developers that encourage entrepreneurship and innovation, providing project management and coordination of research projects and novel ideas for extensive and advanced software and web development. We also support research and technology transfer to industry through multiple bilateral industry collaborations.

Evolving big data analytics

Social media analytics (usage and content summarization)

Smart cities (IoT) and social networks data intergration

Cloud-based evolving data streams management

Creativity and cultural innovation

social_final
cloud_final
cities_final
creativity_final
big_data_final

Data and Web Science Lab is an active research group engaged in research and innovation on ICT complex data science and engineering technologies, offering end-to-end multi way and multi scope analytics.

Moustaka, V., Maitis, A., Vakali, A.,, Anthopoulos, L. G.: CityDNA Dynamics: A Model for Smart City Maturity and Performance Benchmarking. In: In Companion Proceedings of the Web Conference 2020, pp. 829-833, 2020.
PDF
Naskos, A., Kougka, G., Toliopoulos, T., Gounaris, A., Vamvalis, C., Caljouw, D.: Event-Based Predictive Maintenance on Top of Sensor Data in a Real Industry 4.0 Case Study. In: Machine Learning and Knowledge Discovery in Databases, pp. 345-356, 2020.
PDF
Garcia-Garcia F., Corral A., Iribarne L., Vassilakopoulos M., Manolopoulos Y.: Distance Join Query Processing in SpatialHadoop and LocationSpark. In: Information Sciences, Vol. 512 , pp. 985-1008, 2020.
Fevgas A., Akritidis L., Bozanis P., Manolopoulos Y.: Indexing in flash storage devices: a survey on challenges, current approaches, and future trends. In: The VLDB Journal, Vol. 29 (No. 1), pp. 274-311, 2020.
PDF
Gogoglou A., Manolopoulos Y.: A Data-driven Unified Framework for Predicting Citation Dynamics. In: IEEE Transactions on Big Data, 2020.
PDF
Roumelis G., Velentzas P., Vassilakopoulos M., Corral A., Fevgas A., Manolopoulos Y.: Parallel processing of spatial batch-queries using xBR^+-trees in solid-state drives. In: Cluster Computing, 2020.
PDF
Dimitriadis, I., Psomiadis, V. G., Vakali, A: A crowdsourcing approach to advance collective awareness and social good practices. In: In IEEE/WIC/ACM International Conference on Web Intelligence-Companion Volume, pp. 200-207, 2019.
PDF
Rodríguez-González, A., Vakali, A., Mayer, M. A., Okumura, T., Menasalvas-Ruiz, E., Spiliopoulou, M.: Introduction to the special issue on social data analytics in medicine and healthcare. In: International Journal of Data Science and Analytics, Vol. 8 , 2019.
PDF
Kougka G., Gounaris A : Optimization of data flow execution in a parallel environment. In: Distributed and Parallel Databases, 37 (3), pp. 385–410, 2019.
PDF