Barbara Pernici

Politecnico di Milano

Barbara Pernici

Politecnico di Milano

Biography

Prof. Barbara Pernici is full professor in Computer Engineering at the Politecnico di Milano. Her research interests include adaptive information systems, service oriented computing, data quality, and energy efficiency in information systems. She has lead the POLIMI team in the European FP7 ECO2Clouds (Experimental Awareness of CO2 in Federated Cloud Sourcing) project and scientific leader in the FP7 European project GAMES (Green Active Management of Energy in Service Centers). She is member of the Management Committee of the COST 1304 Action ACROSS (Autonomous Control for a Reliable Internet of Services). She has been elected chair of TC8 Information Systems of the International Federation for Information Processing (IFIP), of IFIP WG 8.1 on Information Systems Design, and vice-chair of the IFIP WG on Services-Oriented Software.

Keynote Title: Processes and quality of data

While a great emphasis has been given in the literature on modeling and analyzing the structure of processes, data being processed and managed within processes are often considered with less attention. In the talk, the importance of data in processes will be analyzed mainly from the point of view of its quality. The data being considered include both the ones directly managed by the process and also the ones that are available in the process execution environment, providing information about its context of execution. In particular the presentation will discuss the issues and possible techniques that can be adopted for evaluating the impact of poor data quality on processes, for assessing the importance of different data quality dimensions, such as, for instance, accuracy, consistency, and completeness, for improving processes adding data quality controls, for repairing processes when failures due to poor data quality occur during execution. Finally, future directions for research considering the opportunities and issues arising from the larger and larger amounts of data available in process environments from different sources will analyzed and discussed.