IC3K is a joint conference composed of three concurrent conferences: KDIR, KEOD and KMIS. These three conferences are always co-located and held in parallel. Keynote lectures are plenary sessions and can be attended by all IC3K participants.
Big Data Mining Services and Distributed Knowledge Discovery Applications on Clouds
University of Calabria and ICAR-CNR
Domenico Talia is a full professor of computer engineering at the University of Calabria and the director of ICAR-CNR. He is a partner of startups like Scalable Data Analytics, Exeura and Eco4Cloud. His research interests include parallel and distributed data mining algorithms, Cloud computing, Grid services, distributed knowledge discovery, mobile computing, green computing systems, peer-to-peer systems, and parallel programming.
Talia published ten books and about 300 papers in archival journals such as CACM, Computer, IEEE TKDE, IEEE TSE, IEEE TSMC-B, IEEE Micro, ACM Computing Surveys, FGCS, Parallel Computing, IEEE Internet Computing and international conference proceedings. He is a member of the editorial boards of IEEE Transactions on Computers, the Future Generation Computer Systems journal, the International Journal on Web and Grid Services, the Scalable Computing: Practice and Experience journal, MultiAgent and Grid Systems: An International Journal, International Journal of Web and Grid Services, and the Web Intelligence and Agent Systems International journal. Talia has been a project evaluator for several international institutions such as the European Commission, the Aeres in France, the Austrian Science Fund, the Croucher Foundation, and the Russian Federation Government. He served as a program chair, organizer, or program committee member of several international scientific conferences and gave many invited talks and seminars in international conferences and schools. Talia is a member of the ACM and the IEEE Computer Society.
Digital data repositories are more and more massive and distributed, therefore we need smart data analysis techniques and scalable architectures to extract useful information from them in reduced time. Cloud computing infrastructures offer an effective support for addressing both the computational and data storage needs of big data mining and parallel knowledge discovery applications. In fact, complex data mining tasks involve data- and compute-intensive algorithms that require large and efficient storage facilities together with high performance processors to get results in acceptable times. In this talk we introduce the topic and the main research issues, then we present a Data Mining Cloud Framework designed for developing and executing distributed data analytics applications as workflows of services. In this environment we use data sets, analysis tools, data mining algorithms and knowledge models that are implemented as single services that can be combined through a visual programming interface in distributed workflows to be executed on Clouds. The first implementation of the Data Mining Cloud Framework on Azure is presented and the main features of the graphical programming interface are described.