Overview

Research Topics

Research Projects

Infrastructure

Scientific Contributions

Research Prototypes

Software Systems

Research Topics

The Software Systems Research Area covers a wide variety of research topics, including:

• Systems Dependability and Self-Properties. Investigate means for building more trustworthy software systems that can withstand intentional or unintentional attacks, while exhibiting a degree of robustness, security, and tolerance against misuse.
• Self-aware software systems. Investigate software systems that maintain a level of self awareness for supporting their autonomic behavior.
• Distributed Architectures and P2P Computing: Study and application of classical distributed applications, as well as novel ones like P2P.
• Software Middleware: Development of abstraction layers that provide fundamental middleware services supporting distributed applications, mobile applications and games, mobile P2P computing, etc.
• Retargeting of Mobile Applications: Development of methodologies and programming tool chains for the efficient retargeting of mobile applications and games to a variety of devices.
• Configuration Management: Investigation and development of software platforms for the automatic configuration of devices, with emphasis on ones that reside in the networked home environment.
• Applications for Wireless Sensor Networks. Development of applications for the intelligent collection, manipulation and correlation of data collected from WSNs, including the use of knowledge management techniques for the formulation and enforcement of business level policies.
• Mobile and Social Networking Platforms and Applications. Development of platforms for building mobile applications, including social networking ones.
• Business Intelligence: Development of novel techniques for decision support of both low level (operational) and high level (executive) business problems, ranging from the selection of partners in supply chains to inventory control.
• Machine Learning and Data Mining: Development and tuning of machine learning and data mining techniques to a variety of areas of interest, including fraud detection, classification of normal and abnormal system behavior in large scale distributed systems, etc.
• Supply Chain Management and Optimization: Modeling supply-chain problems and the development of algorithms for finding exact or near optimal solutions to problems in this area.