Process mining in case handling systems

Process mining has proven to be a valuable tool for tracking down problems or inefficiencies within a variety of business processes, using information from event logs. Compared to traditional process-aware information systems (e.g. workow management systems), case handling systems allow for much more exibility by adding all kinds of implicit controls. In this paper we reason about what information can be extracted from a case handling system and how this is accomplished. Further, we investigate how this data can be exploited, in order to achieve greater insight into how business processes are handled within such a flexible environment

Fuzzy mining - adaptive process simplification based on multi-perspective metrics

Process Mining is a technique for extracting process models from execution logs. This is particularly useful in situations where people have an idealized view of reality. Real-life processes turn out to be less structured than people tend to believe. Unfortunately, traditional process mining approaches have problems dealing with unstructured processes. The discovered models are often "spaghetti-like", showing all details without distinguishing what is important and what is not. This paper proposes a new process mining approach to overcome this problem. The approach is configurable and allows for different faithfully simplified views of a particular process. To do this, the concept of a roadmap is used as a metaphor. Just like different roadmaps provide suitable abstractions of reality, process models should provide meaningful abstractions of operational processes encountered in domains ranging from healthcare and logistics to web services and public administration

Process mining of test processes : a case study

Process mining techniques attempt to extract non-trivial and useful information from event logs. For example, there are many process mining techniques to automatically discover a process model describing the causal dependencies between activities. Moreover, using conformance checking it is possible to investigate and quantify deviations between the real process and the modeled process. Several successful case studies have been reported in literature, all demonstrating the applicability of process mining. However, these case studies refer to rather structured administrative processes. In this paper, we investigate the applicability of process mining to less structured processes. We report on a case study where the ProM framework has been applied to the test processes of ASML (the leading manufacturer of wafer scanners in the world). This case study provides many interesting insights. On the one hand, process mining is also applicable to the less structured processes of ASML. On the other hand, the case study also shows the need for alternative mining approaches able to better visualize processes and provide more insights

Supporting flexible processes through recommendations based on history

In today's fast changing business environment exible information systems are required to allow companies to rapidly adjust their business processes to changes in the environment. However, increasing exibility in large information system usually leads to less guidance for its users and consequently requires more experienced users. In order to allow for exible systems with a high degree of guidance, intelligent user assistance is required. In this paper we propose a recommendation service, which, when used in combination with exible information systems, can guide end users during process execution by giving recommendations on possible next steps. Recommendations are generated based on similar past process executions by considering the specific optimization goals. This paper also describes an implementation of the proposed recommendation service in the context of ProM and the declarative work ow management system DECLARE

XES, XESame, and ProM 6

Process mining has emerged as a new way to analyze business processes based on event logs. These events logs need to be extracted from operational systems and can subsequently be used to discover or check the conformance of processes. ProM is a widely used tool for process mining. In earlier versions of ProM, MXML was used as an input format. In future releases of ProM, a new logging format will be used: the eXtensible Event Stream (XES) format. This format has several advantages over MXML. The paper presents two tools that use this format - XESame and ProM 6 - and highlights the main innovations and the role of XES. XESame enables domain experts to specify how the event log should be extracted from existing systems and converted to XES. ProM 6 is a completely new process mining framework based on XES and enabling innovative process mining functionality

Towards an evaluation framework for process mining algorithms

Although there has been a lot of progress in developing process mining algorithms in recent years, no effort has been put in developing a common means of assessing the quality of the models discovered by these algorithms. In this paper, we outline elements of an evaluation framework that is intended to enable (a) process mining researchers to compare the performance of their algorithms, and (b) end users to evaluate the validity of their process mining results. Furthermore, we describe two possible approaches to evaluate a discovered model (i) using existing comparison metrics that have been developed by the process mining research community, and (ii) based on the so-called k-fold-cross validation known from the machine learning community. To illustrate the application of these two approaches, we compared a set of models discovered by different algorithms based on a simple example log

The need for a process mining evaluation framework in research and practice

Although there has been much progress in developing process mining algorithms in recent years, no effort has been put in developing a common means of assessing the quality of the models discovered by these algorithms. In this paper, we motivate the need for such an evaluation mechanism, and outline elements of an evaluation framework that is intended to enable (a) process mining researchers to compare the performance of their algorithms, and (b) end users to evaluate the validity of their process mining results

ProM : the process mining toolkit

Nowadays, all kinds of information systems store detailed information in logs. Process mining has emerged as a way to analyze these systems based on these detailed logs. Unlike classical data mining, the focus of process mining is on processes. First, process mining allows us to extract a process model from an event log. Second, it allows us to detect discrepancies between a modeled process (as it was envisioned to be) and an event log (as it actually is). Third, it can enrich an existing model with knowledge derived from an event log. This paper presents our tool ProM, which is the world-leading tool in the area of process mining

Efficient Doubling on Genus Two Curves over Binary Fields

In most algorithms involving elliptic and hyperelliptic curves, the costliest part consists in computing multiples of ideal classes. This paper investigates how to compute faster doubling over fields of characteristic two. We derive explicit doubling formulae making strong use of the defining equation of the curve. We analyze how many field operations are needed depending on the curve making clear how much generality one loses by the respective choices. Note, that none of the proposed types is known to be weak – one only could be suspicious because of the more special types. Our results allow to choose curves from a large enough variety which have extremely fast doubling needing only half the time of an addition. Combined with a sliding window method this leads to fast computation of scalar multiples. We also speed up the general case