Keynotes will be given in alignment with ACOMP'2910 conference themes.
Director of Research, Department of Computing, Macquarie University, Sydney
Title: Big Data Analytics: Opportunities in Business and Service Revolution
Abstract: There are growing demands in smart and integrated services in the areas such as health care, aging and aged care, travel planning. Big data analytics offers technical support for getting insights and making decisions. In this talk we will look at the landscape of services particularly services exposed in the social media: service trust and reputation, service recommendation, service credibility, and service quality. These are the building blocks for building smart and integrated services.
Institute for Application-oriented Knowledge Processing, Johannes Kepler University Linz, Austria
School of Information Technologies, Tallinn University of Technology, Estonia
Abstract: I will present recent work on Test Suite Reduction (TSR) within the scope of Software testing, which is a widely accepted practice that ensures the quality of a System under Test. In this respect, TSR is considered as a potential approach to deal with the test suite size problem. Moreover, a complete automation support is highly recommended for software testing to adequately meet the thriving challenges raised by the big data era. The originality of the work that I will present stands in the unveil of a connection between the concept of minimal transversals of an hypergraph with the TSR issue. I will also highlight the connection between minimal transversals and the Formal Concept analysis. The latter has typically been steadily applied in the field of software engineering to support software maintenance and object-oriented class identification tasks.
Ludwig-Maximilians-University Munich, Germany
Title: Group security and individual privacy
Abstract: The security of an organisation is affected by the privacy enjoyed by its member individuals. The large-scale change emergent from the gloabal proliferation of cloud computing, smart homes, the internet of things and machine learning requires a novel view on the flow of confidential information. The growing reciprocal influence between the knowledge about individuals and the knowledge about the groups or organisations they pertain to originates in the personal data about group members being processed outside of the domain where it originates. Thus, the degree of privacy of individuals affects the degree of confidentiality of the information pertaining to the groups the individual is a member of. This lecture discusses threats to individuals and organisations based on models of information flow and knowledge extraction.