KeyValueSets : event logs revisited

In process mining, event logs have traditionally been considered as strictly hierarchical lists of events, where each event belongs to exactly one case, refers to exactly one activity and has a timestamp. Based on this assumption, the XES standard has been developed to describe event logs. In this paper, we reconsider the notion of an event log, by focussing on events as the primary entity. Furthermore, we do not assume the presence of traditional notions like cases and timestamps (or even ordering), but we introduce mappings for sorting and grouping our KeyValueSets. This allows us to provide a generic transformation from a wide variety of formats to standard XES logs

Translating labelled P/T nets into EPCs for sake of communication

Petri nets can be used to capture the behavior of a process in a formal and precise way. However, Petri nets are less suitable to communicate the process to its owner, as simple routing constructs in the process might require a large number of transitions. This paper in- troduces a translation from labelled P/T nets to EPCs in such a way that many transitions can be translated into one EPC connector. The algorithm even allows for translating a set of transitions into an OR connector, even though the concept of OR connectors (especially the OR join connector) has no real equal in Petri nets. Using this translation presented here, labelled P/T nets may be communicated to the process owner by means of the created EPC

Measuring similarity between business process models

Quality aspects become increasingly important when business process modeling is used in a large-scale enterprise setting. In order to facilitate a storage without redundancy and an efficient retrieval of relevant process models in model databases it is required to develop a theoretical understanding of how a degree of behavioral similarity can be defined. In this paper we address this challenge in a novel way. We use causal footprints as an abstract representation of the behavior captured by a process model, since they allow us to compare models defined in both formal modeling languages like Petri nets and informal ones like EPCs. Based on the causal footprint derived from two models we calculate their similarity based on the established vector space model from information retrieval.We validate this concept with an experiment using the SAP Reference Model and an implementation in the ProM framework

Automatic discovery of data-centric and artifact-centric processes

Process discovery is a technique that allows for automatically discovering a process model from recorded executions of a process as it happens in reality. This technique has successfully been applied for classical processes where one process execution is constituted by a single case with a unique case identifier. Data-centric and artifact-centric systems such as ERP systems violate this assumption. Here a process execution is driven by process data having various notions of interrelated identifiers that distinguish the various interrelated data objects of the process. Classical process mining techniques fail in this setting. This paper presents a fully automatic technique for discovering for each notion of data object in the process a separate process model that describes the evolution of this object, also known as artifact life-cycle model. Given a relational database that stores process execution information of a data-centric system, the technique extracts event information, case identifiers and their interrelations, discovers the central process data objects and their associated events, and decomposes the data source into multiple logs, each describing the cases of a separate data object. Then classical process discovery techniques can be applied to obtain a process model for each object. The technique is implemented and has been evaluated on the production ERP system of a large retailer

On the degree of behavioral similarity between business process models

Quality aspects become increasingly important while business process modeling is used in a large-scale enterprise setting. In order to facilitate a storage without redundancy and an efficient retrieval of relevant process models in model databases it is required to develop a theoretical understanding of how a degree of behavioral similarity can be defined. In this paper we address this challenge in a novel way. We use causal footprints as an abstract representation of the behavior captured by a process model, since they allow us to compare models defined in both formal modeling languages like Petri nets and informal ones like EPCs. Based on the causal footprint derived from two models we calculate their similarity based on the established vector space model from information retrieval. We illustrate this concept with an example from the SAP Reference Model and present a prototypical implementation as a plug-in to the ProM framework

Filter techniques for region-based process discovery

The goal of process discovery is to learn a process model based on example behavior recorded in an event log. Region-based process discovery techniques are able to uncover complex process structures (e.g., milestones) and, at the same time, provide formal guarantees w.r.t. the model discovered. For example, it is possible to ensure that the discovered model is able to replay the event log and that there are bounds on the amount of additional behavior allowed by the model that is not present in the event log. Unfortunately, region-based discovery techniques cannot handle exceptional behavior. The presence of a few exceptional traces may result in an incomprehensible model concealing the dominant behavior observed. Hence, despite their promise, region-based approaches cannot be applied in everyday process mining practice. This paper addresses the problem by proposing two filtering techniques tailored towards ILP-based process discovery (an approach based on integer linear programming and language-based region theory). Both techniques help to produce models that are less over-fitting w.r.t. the event log and have been implemented in ProM. One of the techniques is also feasible in real-life settings as it, in most cases, reduces computation time compared to conventional region-based techniques. Additionally the technique is able to produce understandable process models that better capture the dominant behavior present in the event log. Keywords: Process mining, process discovery, integer linear programming, filterin

Event stream-based process discovery using abstract representations

The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper, we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining toolkit ProM (http://promtools.org). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.</p

Translating message sequence charts to other process languages using process mining

Message Sequence Charts (MSCs) are a well known language for specifying scenarios that describe how di??erent actors (e.g., system components, people, or organizations) interact. MSCs are often used as a starting point for software analysts to discuss the behavior of a system with di??erent stakeholders. Often such discussions lead to more complete behavioral models described by e.g. Event-driven Process Chains (EPCs), UML activity diagrams, BPMN models, Petri nets, etc. The contribution of this paper is to present a method that uses process mining to translate a set of MSCs that represent example scenarios into a complete process model, e.g., represented in terms of EPCs or Petri nets. Our approach takes MSCs and translates them into a special kind event logs. Unlike all known process mining techniques, we use a new approach that uses event logs containing explicit causal dependencies. This allows us to discover high-quality process models. The approach has been implemented in the process mining framework ProM