About Cracs

The Center for Research in Advanced Computing Systems (CRACS) is a R&D unit of INESCTEC Research Laboratory. CRACS was founded in 2007, recognized and evaluated by the Portuguese National Science Foundation (FCT - Fundação para a Ciência e Tecnologia). CRACS is partially supported by the University of Porto, Faculty of Sciences, Department of Computer Science (UP/FCUP/DCC), where it is located. Since 2011, CRACS is formally associated with the INESCTEC, a large Institute for Systems and Computer Engineering that for the 2014 evaluation exercise integrates 12 research units whose members belong to UPorto, UMinho, UTAD and ISEP.

CRACS aims to be actively involved in fundamental and applied research in scalable computing both at national and international level; to foster training of young researchers both at post-graduate and post-doctoral level; and to actively establish partnerships with companies to promote and disseminate research results. The research team is composed by around 50 researchers, of which 13 are integrated members holding a PhD degree.
CRACS focuses on three main areas of research that include a broad number of issues related to scalable computing as listed next:

1. Programming Languages and Distributed Computing

  • Implementation of Logic Programming Systems
  • Parallel Logic Programming and Inductive Logic Programming systems
  • Linear Logic and Type Theory
  • Programming Languages for Mobile Computing an Sensor Networks
  • Parallel and Distributed Computing
  • Peer-to-peer middleware and cloud-computing platforms

2. Security and Privacy

  • Algorithms to improve security usability and privacy in software and systems
  • User-controlled identity management systems
  • Secure identity cards and authentication mechanisms
  • Algorithms and tools for sharing sensitive data while preserving privacy
  • Ethical hacking and penetration testing for preemptive vulnerability detection

3. Knowledge in a World of Data

  • Machine Learning and Discovery
  • Inductive Logic Programming
  • Statistical Relational Learning
  • Ubiquitous Data Streams
  • Biologically Inspired Computing
  • Applications
  • High-throughput genomics data
  • Understanding Cellular Pathways
  • Medical Records Data
  • Intrusion Detection Systems
  • SPAM Classification
  • Semantic Relatedness
  • Sentiment Discovery
  • Interesting node discovery
  • Sensor Data Streams
  • Semantic Digital Libraries
  • e-Learning Environments and Tools
  • Service Oriented Architectures and Open Data