Antonella Guzzo

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Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being(More)
Process mining techniques have recently received notable attention in the literature; for their ability to assist in the (re)design of complex processes by automatically discovering models that explain the events registered in some log traces provided as input. Following this line of research, the paper investigates an extension of such basic approaches,(More)
Process mining techniques attempt to extract non-trivial and useful information from event logs recorded by information systems. For example, there are many process mining techniques to automatically discover a process model based on some event log. Most of these algorithms perform well on structured processes with little disturbances. However, in reality(More)
Bioinformatics is as a bridge between life science and computer science: computer algorithms are needed to face complexity of biological processes. Bioinformatics applications manage complex biological data stored into distributed and often heterogeneous databases and require large computing power. We discuss requirements of such applications and present(More)
Process mining techniques have been receiving great attention in the literature for their ability to automatically support process (re)design. The output of these techniques is a concrete workflow schema that models all the possible execution scenarios registered in the logs, and that can be profitably used to support further-coming enactments. In this(More)
We propose a general framework for the process mining problem which encompasses the assumption of workflow schema with local constraints only, for it being applicable to more expressive specification languages, independently of the particular syntax adopted. In fact, we provide an effective technique for process mining based on the rather unexplored concept(More)
Bioinformatics applications manage complex biological data stored into distributed and often heterogeneous databases and require large computing power. Among these, protein structure comparison applications exhibit complex workflow structure, access different databases, require high computing power. Thus they could benefit of semantic modelling and Grid(More)
Article history: Received 11 January 2010 Received in revised form 20 December 2010 Accepted 5 July 2011 Available online 28 July 2011 A prominent goal of process mining is to build automatically a model explaining all the episodes recorded in the log of some transactional system. Whenever the process to be mined is complex and highly-flexible, however,(More)
Classical outlier detection approaches may hardly fit process mining applications, since in these settings anomalies emerge not only as deviations from the sequence of events most often registered in the log, but also as deviations from the behavior prescribed by some (possibly unknown) process model. These issues have been faced in the paper via an(More)
The products of the SOS-regulated umuDC operon are required for most UV and chemical mutagenesis in Escherichia coli, a process that results from a translesion synthesis mechanism. The UmuD protein is activated for its role in mutagenesis by a RecA-facilitated autodigestion that removes the N-terminal 24 amino acids. A previous genetic screen for nonmutable(More)