Antonella Guzzo

Process Mining Manifesto

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 recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

Process mining based on clustering: a quest for precision

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 it is difficult to determine the scope of a process and typically there are all kinds of disturbances. As a result, process mining techniques produce spaghetti-like models that are difficult to read and that attempt to merge unrelated cases. To address these problems, we use an approach where the event log is clustered iteratively such that each of the resulting clusters corresponds to a coherent set of cases that can be adequately represented by a process model. The approach allows for different clustering and process discovery algorithms. In this paper, we provide a particular clustering algorithm that avoids over-generalization and a process discovery algorithm that is much more robust than the algorithms described in literature [1]. The whole approach has been implemented in ProM.

Mining hierarchies of models: from abstract views to concrete specifications

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 paper, we face process mining in a slightly different perspective. Indeed, we propose an approach to process mining that combines novel discovery strategies with abstraction methods, with the aim of producing hierarchical views of the process that satisfactorily capture its behavior at different level of details. Therefore, at the highest level of detail, the mined model can support the design of concrete workflows; at lower levels of detail, the views can be used in advanced business process platforms to support monitoring and analysis. Our approach consists of several algorithms which have been integrated into a systems architecture whose description is accounted for in the paper as well.

Mining Expressive Process Models by Clustering Workflow Traces

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 of clustering workflow executions, in which clusters of executions sharing the same structure and the same unexpected behavior (w.r.t. the local properties) are seen as a witness of the existence of global constraints.

Mining taxonomies of process models

Process mining techniques have been receiving great attention in the literature for their ability to automatically support process (re)design. Typically, these techniques discover a concrete workflow schema modelling all possible execution patterns registered in a given log, which can be exploited subsequently to support further-coming enactments. In this paper, an approach to process mining is introduced that extends classical discovery mechanisms by means of an abstraction method aimed at producing a taxonomy of workflow models. The taxonomy is built to capture the process behavior at different levels of detail. Indeed, the most-detailed mined models, i.e., the leafs of the taxonomy, are meant to support the design of concrete workflows, as it happens with existing techniques in the literature. The other models, i.e., non-leaf nodes of the taxonomy, represent instead abstract views over the process behavior that can be used to support advanced monitoring and analysis tasks. All the techniques discussed in the paper have been implemented, tested, and made available as a plugin for a popular process mining framework (ProM). A series of tests, performed on different synthesized and real datasets, evidenced the capability of the approach to characterize the behavior encoded in input logs in a precise and complete way, achieving compelling conformance results even in the presence of complex behavior and noisy data. Moreover, encouraging results have been obtained in a real-life application scenario, where it is shown how the taxonomical view of the process can effectively support an explorative ex-post analysis, hinged on the different kinds of process execution discovered from the logs.

An Ontology-Driven Process Modeling Framework

Designing, analyzing and managing complex processes are recently become crucial issues in most application contexts, such as e-commerce, business process (re-)engineering, Web/grid computing. In this paper, we propose a framework that supports the designer in the definition and in the analysis of complex processes by means of several facilities for reusing, customizing and generalizing existent process components. To this aim we tightly integrate process models with a domain ontology and an activity ontology, so providing a semantic vision of the application context and of the processes themselves. Moreover, the framework is equipped with a set of techniques providing for advanced functionalities, which can be very useful when building and analyzing process models, such as consistency checking, interactive ontology navigation, automatic (re)discovering of process models. A software architecture fully supporting our framework is also presented and discussed.

Outlier detection techniques for process mining applications

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 approach for singling out anomalous evolutions within a set of process traces, which takes into account both statistical properties of the log and the constraints associated with the process model. The approach combines the discovery of frequent execution patterns with a cluster-based anomaly detection procedure; notably, this procedure is suited to deal with categorical data and is, hence, interesting in its own, given that outlier detection has mainly been studied on numerical domains in the literature. All the algorithms presented in the paper have been implemented and integrated into a system prototype that has been thoroughly tested to assess its scalability and effectiveness.

Integrating ontology and workflow in PROTEUS, a grid-based problem solving environment for bioinformatics

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 the architecture of PROTEUS, a grid-based problem solving environment that integrates ontology and workflow approaches to enhance composition and execution of bioinformatics applications on the grid.

Coclustering Multiple Heterogeneous Domains: Linear Combinations and Agreements

The high-order coclustering problem, i.e., the problem of simultaneously clustering heterogeneous types of domain, has become an active research area in the last few years, due to the notable impact it has on several application scenarios. This problem is generally faced by optimizing a weighted combination of functions measuring the quality of coclustering over each pair of domains, where weights are chosen based on the supposed reliability/relevance of their correlation. However, little knowledge is likely to be available, in practice, in order to set these weights in a definite and precise manner. And, more importantly, it might even be conceptually unclear whether to prefer a weighing scheme over others, in those cases where functions encode contrasting goals so that improving the quality for a pair of domains leads to a deterioration for other pairs. The aim of this paper is precisely to shed light on the impact of weighting schemes on techniques based on linear combinations of pairwise objective functions, and to define an approach that overcomes the above problems by looking for an agreement-intuitively, a kind of compromise-among the various domains, thereby getting rid of the need to define an appropriate weighting scheme. Two algorithms performing coclustering on star-structured” domains, based on linear combinations and agreements, respectively, have been designed within an information-theoretic framework. Results from a thorough experimentation, on both synthetic and real data, are discussed, in order to assess the effectiveness of the approaches and to get more insight into their actual behavior.

Mining unconnected patterns in workflows

General patterns of execution that have been frequently scheduled by a workflow management system provide the administrator with previously unknown, and potentially useful information, e.g., about the existence of unexpected causalities between subprocesses of a given workflow. This paper investigates the problem of mining unconnected patterns on the basis of some execution traces, i.e., of detecting sets of activities exhibiting no explicit dependency relationships that are frequently executed together. The problem is faced in the paper by proposing and analyzing two algorithms. One algorithm takes into account information about the structure of the control-flow graph only, while the other is a smart refinement where the knowledge of the frequencies of edges and activities in the traces at hand is also accounted for, by means of a sophisticated graphical analysis. Both algorithms have been implemented and integrated into a system prototype, which may profitably support the enactment phase of the workflow. The correctness of the two algorithms is formally proven, and several experiments are reported to evidence the ability of the graphical analysis to significantly improve the performances, by dramatically pruning the search space of candidate patterns.