OSWINDS team has participated in several research programs funded by national and European Commission research funds. OSWINDS has participated in more than 25 research and development projects from which it has scientifically leaded more than 15 projects in the area of social networks, Web content mining and big data analytics. The most recent research activity involves relevant projects funded by EU FP7 framework in the areas of Internet Science, smart cities and cloud based frameworks and implementations. The most recent projects are presented next.
ONLINE-S3 will develop an e-policy platform, augmented with a toolbox of applications and online services, able to assist national and regional authorities in the EU to elaborate their smart specialisation agenda. In other words, the project will investigate, develop and test new and innovative technologies, tools and services which are in line with the methodological steps proposed by the European Commission. Authorities, researchers and leading IT labs and companies in the 4 pilots will be able to use the solution as to support the advancement of knowledge-based policy advice.
ENCASE: ENhancing seCurity and privAcy in the Social wEb
ENCASE will leverage the latest advances in usable security and privacy to design and implement a browser-based architecture for the protection of minors from malicious actors in online social networks. The overall vision of the project is to provide research and innovation contributions to end-user experience assessment, large scale data processing, machine learning and data mining, and content confidentiality.
Fix the Fixing
Fix the Fixing aims to develop a user-friendly educational tool will that will be used by stakeholders to increase people’s involved in sport awareness about corruption, fraud and match-fixing in different types and levels of sport; teach coping skills on resisting offers and temptations to engage in match-fixing; and indicate ways to properly report match-fixing incidents to the relevant authorities.
REVITAL: Real and Virtual Social Interactions
Real and Virtual Social Interactions and Reciprocities via efficient sentiment and affective analysis methodologies for extracting or recording and analyzing emotional information in real and virtual life. Detect and characterize patterns of ‘online’ communication with patterns of ‘offline’ or face to face communication in real social networks and studying patterns of human emotions in real and virtual life, to offer rich information for detecting implicit interactions and reciprocities.
ARCHIMEDIS: Enterprise Architecture for Digital Cities
SEN2SOC : Sensors talk and humans sense
Social and sensor data streams integration via data mining and statistical analysis methodologies and practices utilized to communicate sensor measurements to the public (citizens, authorities, etc), while at the same time human sensing is utilized in order to improve IoT infrastructures. Design and develop applications (Web and/or mobile) which will leverage evolving data streams occurring in smart city contexts with emphasis on social networks interactions to capture wisdom of the crowds.
Social networks graphs studies
The project aimed at detecting user communities (in Twitter) based on users’ mentions and conversations in the context of specific topics. It has managed monitoring activity in microblogs (Twitter) to detect when and how communities evolve (emerge, gain / lose strength, merge, collapse) and also analyzed users’ activity in the identified communities to discover their interest / views / opinions and with further outlook on how to find power users in communities by performing sentiment analysis on tweets.
Cloud4Trends : Leveraging the cloud infrastructure for localized real-time trend detection in social media
Cloud-based framework for social networks trends detection and analysis via real-time large-scale data clustering techniques, evolving social graph mining with tailored data preprocessing and cleaning. Emphasis placed on analyzing societal concerns and reaching consensus on collective decision-making via tailored web mining techniques which utilize the cloud infrastructure Venus-C to help address the challenges posed by data and time-intensive processes.
HERAKLITOS II : Web data clustering techniques
PhD funding program with the research been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) – Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund, the project has focused on data mining techniques which have dealt with the scalability and qualitative content detection in recommender systems.