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Über dieses Buch

This book constitutes the thoroughly refereed proceedings of the Fourth International Symposium on Data-Driven Process Discovery and Analysis held in Riva del Milan, Italy, in November 2014.

The five revised full papers were carefully selected from 21 submissions. Following the event, authors were given the opportunity to improve their papers with the insights they gained from the symposium. During this edition, the presentations and discussions frequently focused on the implementation of process mining algorithms in contexts where the analytical process is fed by data streams. The selected papers underline the most relevant challenges identified and propose novel solutions and approaches for their solution.



Discovery of Frequent Episodes in Event Logs

Lion’s share of process mining research focuses on the discovery of end-to-end process models describing the characteristic behavior of observed cases. The notion of a process instance (i.e., the case) plays an important role in process mining. Pattern mining techniques (such as traditional episode mining, i.e., mining collections of partially ordered events) do not consider process instances. In this paper, we present a new technique (and corresponding implementation) that discovers frequently occurring episodes in event logs, thereby exploiting the fact that events are associated with cases. Hence, the work can be positioned in-between process mining and pattern mining. Episode Discovery has its applications in, amongst others, discovering local patterns in complex processes and conformance checking based on partial orders. We also discover episode rules to predict behavior and discover correlated behaviors in processes, and apply our technique to other perspectives present in event logs. We have developed a ProM plug-in that exploits efficient algorithms for the discovery of frequent episodes and episode rules. Experimental results based on real-life event logs demonstrate the feasibility and usefulness of the approach.
Maikel Leemans, Wil M. P. van der Aalst

Finding Suitable Activity Clusters for Decomposed Process Discovery

Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle “big event data” adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel. Many decomposed process mining techniques have been proposed in literature. Analysis shows that even though the decomposition step takes a relatively small amount of time, it is of key importance in finding a high-quality process model and for the computation time required to discover the individual parts. Currently there is no way to assess the quality of a decomposition beforehand. We define three quality notions that can be used to assess a decomposition, before using it to discover a model or check conformance with. We then propose a decomposition approach that uses these notions and is able to find a high-quality decomposition in little time.
B. F. A. Hompes, H. M. W. Verbeek, W. M. P. van der Aalst

History-Based Construction of Alignments for Conformance Checking: Formalization and Implementation

Alignments provide a robust approach for conformance checking, which has been largely applied in various contexts such as auditing and performance analysis. Alignment-based conformance checking techniques pinpoint the deviations causing nonconformity based on a cost function. However, such a cost function is often manually defined on the basis of human judgment and thus error-prone, leading to alignments that do not provide accurate explanations of nonconformity. This paper proposes an approach to automatically define the cost function based on information extracted from the past process executions. The cost function only relies on objective factors and thus enables the construction of probable alignments, i.e. alignments that provide probable explanations of nonconformity. Our approach has been implemented in ProM and evaluated using both synthetic and real-life data.
Mahdi Alizadeh, Massimiliano de Leoni, Nicola Zannone

Dynamic Constructs Competition Miner - Occurrence- vs. Time-Based Ageing

Since the environment for businesses is becoming more competitive by the day, business organizations have to be more adaptive to environmental changes and are constantly in a process of optimization. Fundamental parts of these organizations are their business processes. Discovering and understanding the actual execution flow of the processes deployed in organizations is an important enabler for the management, analysis, and optimization of both, the processes and the business. This has become increasingly difficult since business processes are now often dynamically changing and may produce hundreds of events per second. The basis for this paper is the Constructs Competition Miner (CCM): A divide-and-conquer algorithm which discovers block-structured processes from event logs possibly consisting of exceptional behaviour. In this paper we propose a set of modifications for the CCM to enable dynamic business process discovery of a run-time process model from a stream of events. We describe the different modifications with a particular focus on the influence of individual events, i.e. ageing techniques. We furthermore investigate the behaviour and performance of the algorithm and the ageing techniques on event streams of dynamically changing processes.
David Redlich, Thomas Molka, Wasif Gilani, Gordon Blair, Awais Rashid

Trustworthy Cloud Certification: A Model-Based Approach

Cloud computing is introducing an architectural paradigm shift that involves a large part of the IT industry. The flexibility in allocating and releasing resources at runtime creates new business opportunities for service providers and their customers. However, despite its advantages, cloud computing is still not showing its full potential. Lack of mechanisms to formally assess the behavior of the cloud and its services/processes, in fact, negatively affects the trust relation between providers and potential customers, limiting customer movement to the cloud. Recently, cloud certification has been proposed as a means to support trustworthy services by providing formal evidence of service behavior to customers. One of the main limitations of existing approaches is the uncertainty introduced by the cloud on the validity and correctness of existing certificates. In this paper, we present a trustworthy cloud certification approach based on model verification. Our approach checks certificate validity at runtime, by continuously verifying the correctness of the service model at the basis of certification activities against real and synthetic service execution traces.
Marco Anisetti, Claudio A. Ardagna, Ernesto Damiani, Nabil El Ioini


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