Retrieval and clustering for supporting business process adjustment and analysis

Abstract

In this paper, we describe a framework able to support run-time adjustment and a posteriori analysis of business processes, which exploits the retrieval step of the Case-based Reasoning (CBR) methodology. In particular, our framework allows to retrieve traces of process execution similar to the current one. Moreover, it supports an automatic organization of the trace database content through the application of hierarchical clustering techniques. Results can provide help both to end users, in the process execution phase, and to process engineers, in (formal) process conformance evaluation and long term process schema redesign.

Retrieval and clustering rely on a distance definition able to take into account temporal information in traces. This metric has outperformed simpler distance definitions in our experiments, which were conducted in a real-world application domain.

Introduction

Business Process (BP) Management is a set of activities aimed at defining, executing and optimizing BP, with the objective of making the business of an enterprise as effective and efficient as possible, and of increasing its economic success. Such activities are highly automated, typically by means of workflow management systems, also called Business Process Management System (BPMS) [1], [2].

In normal conditions, a BPMS allows to automatically execute a BP according to its process schema, i.e., to a formalized model in which the actions to be performed and the control flow relations to be respected among them are specified. However, BP optimization may ask the enterprise to be able to flexibly change and adapt such a predefined process schema, in response to expected situations (e.g., new laws, reengineering efforts) as well as to unanticipated exceptions and problems in the operating environment (e.g., emergencies) [3].

The agile workflow technology [4], [5] is the technical solution which has been invoked to deal with such adaptation and overriding needs. It can support both ad hoc adjustments of individual process instances [6], [7], operated by end users, and redesign at the general process schema level, operated by process engineers—applicable even if the default process schema is already in use by some running instances [6], [8].

In order to provide an effective and quick adaptation support, many agile workflow systems share the idea of recalling and reusing concrete examples of changes adopted in the past. To this end, Case-based Reasoning (CBR) [9] has been proposed as a natural methodological solution. CBR is a reasoning paradigm that exploits the specific knowledge of previously experienced situations, called cases. It operates by retrieving and reusing similar cases in order to solve the problem at hand (after a possible revision of the retrieved solutions, if needed). Indeed CBR is particularly well suited for managing exceptional situations, even when they cannot be foreseen or preplanned. As a matter of fact, in the literature cases have often been resorted to in order to describe exceptions, in various domains (see e.g. [10]), and many examples of CBR-based process change support have been proposed (see e.g. [7], [11], [12], [13], [14], [15]).

The implementation of a proper CBR-based support for process adjustment and analysis obviously starts from a careful evaluation of past cases representation. In many applications, past examples of change are recorded as traces of execution [16] (stored in a database, also known as event log [17]), i.e., as the sequence of the process actions that were actually performed, often coupled with their starting and ending time. In the simplest form, a trace does not log any other feature about the executed actions (e.g., the actor, or the available resources). Moreover, usually it does not provide any contextual information, which could justify the reasons for possible deviations from the prescriptions of the default process schema.

In this paper, we propose a support to BP adjustment and analysis which adopts the retrieval step of the CBR methodology, specifically designed to work on cases in the form of traces of execution.

In our framework, retrieval is meant to help end users in the process execution phase, when dealing with an atypical situation. Indeed, suggestions on how to adjust the default process schema in the current situation may be obtained by analyzing the most similar retrieved examples of change, recorded as traces that share the starting sequence of actions with the current query (i.e., with the input problem).

Moreover, we support an automatic organization of the case base content (i.e., of all the available traces) through the application of hierarchical clustering techniques. Clustering can serve as a starting point for a set of a posteriori trace analyses. In particular, it can help process engineers in conformance evaluation (e.g., it can be an input to formal verification of the conformance of traces to proper semantic constraints [18]). Additionally, since changes can also be due to a weak or incomplete initial process schema definition, engineers can exploit (retrieval and) clustering results to draw some suggestions on how to redesign process schemata, in order to incorporate the most frequent and significant changes once and for all.

In our work retrieval and clustering rely on a distance definition able to take into account temporal information.

Interestingly, only a few metrics specifically designed to work on traces have been described in the literature. Moreover, most of them do not manage temporal information; in particular, a properly way of comparing qualitative temporal constraints is usually not provided (see Section 5).

On the other hand, neglecting time can be a significant flaw. In fact, time is really crucial in some applications. In medicine, for instance, the role of time is clearly central: it is mandatory to penalize the fact that the very same action had different durations in two traces, or was delayed, especially if referring to emergency procedures. And, generally speaking, temporal information is relevant in all domains, as it can be used e.g., to discover bottlenecks and to measure service levels [17].

The metric we introduce in this work allows us to explicit manage temporal information in traces, paying attention both to quantitative and to qualitative constraints.

In addition to this methodological contribution, in the paper we also describe our experimental work in the field of stroke care, in which we compared the new metric to a classical existing one (namely, the edit distance [19], [20]), and to simpler versions of our distance definition, able to manage only part of the overall information available on traces.

The paper is organized as follows. Section 2 presents technical details of the framework. Section 3 describes experimental results. Section 4 adds information about recent methodological improvements we are providing, in order to enhance the framework performance. Section 5 addresses some comparisons with related works. Finally, Section 6 is devoted to conclusions, discussion of limitations and future research directions.