Ton Weijters

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.

IGTree: using trees for compression and classification in lazy learning algorithms

We describe the IGTree learning algorithm, which compresses an instance base into a tree structure. The concept of information gain is used as a heuristic function for performing this compression. IGTree produces trees that, compared to other lazy learning approaches, reduce storage requirements and the time required to compute classifications. Furthermore, we obtained similar or better generalization accuracy with IGTree when trained on two complex linguistic tasks, viz. letter–phoneme transliteration and part-of-speech-tagging, when compared to alternative lazy learning and decision tree approaches (viz., IB1, information-gain-weighted IB1, and C4.5). A third experiment, with the task of word hyphenation, demonstrates that when the mutual differences in information gain of features is too small, IGTree as well as information-gain-weighted IB1 perform worse than IB1. These results indicate that IGTree is a useful algorithm for problems characterized by the availability of a large number of training instances described by symbolic features with sufficiently differing information gain values.

Logistic-based patient grouping for multi-disciplinary treatment

Present-day healthcare witnesses a growing demand for coordination of patient care. Coordination is needed especially in those cases in which hospitals have structured healthcare into specialty-oriented units, while a substantial portion of patient care is not limited to single units. From a logistic point of view, this multi-disciplinary patient care creates a tension between controlling the hospitals units, and the need for a control of the patient flow between units. A possible solution is the creation of new units in which different specialties work together for specific groups of patients. A first step in this solution is to identify the salient patient groups in need of multi-disciplinary care. Grouping techniques seem to offer a solution. However, most grouping approaches in medicine are driven by a search for pathophysiological homogeneity. In this paper, we present an alternative logistic-driven grouping approach. The starting point of our approach is a database with medical cases for 3,603 patients with peripheral arterial vascular (PAV) diseases. For these medical cases, six basic logistic variables (such as the number of visits to different specialist) are selected. Using these logistic variables, clustering techniques are used to group the medical cases in logistically homogeneous groups. In our approach, the quality of the resulting grouping is not measured by statistical significance, but by (i) the usefulness of the grouping for the creation of new multi-disciplinary units; (ii) how well patients can be selected for treatment in the new units. Given a priori knowledge of a patient (e.g. age, diagnosis), machine learning techniques are employed to induce rules that can be used for the selection of the patients eligible for treatment in the new units. In the paper, we describe the results of the above-proposed methodology for patients with PAV diseases. Two groupings and the accompanied classification rule sets are presented. One grouping is based on all the logistic variables, and another grouping is based on two latent factors found by applying factor analysis. On the basis of the experimental results, we can conclude that it is possible to search for medical logistic homogenous groups (i) that can be characterized by rules based on the aggregated logistic variables; (ii) for which we can formulate rules to predict to which cluster new patients belong.

Empirical Learning of Natural Language Processing Task

Language learning has thus far not been a hot application for machine-learning (ML) research. This limited attention for work on empirical learning of language knowledge and behaviour from text and speech data seems unjustified. After all, it is becoming apparent that empirical learning of Natural Language Processing (NLP) can alleviate NLPs all-time main problem, viz. the knowledge acquisition bottleneck: empirical ML methods such as rule induction, top down induction of decision trees, lazy learning, inductive logic programming, and some types of neural network learning, seem to be excellently suited to automatically induce exactly that knowledge that is hard to gather by hand. In this paper we address the question why NLP is an interesting application for empirical ML, and provide a brief overview of current work in this area.

Interpretable Neural Networks with BP-SOM

Interpretation of models induced by artificial neural networks is often a difficult task. In this paper we focus on a relatively novel neural network architecture and learning algorithm, bp-som that offers possibilities to overcome this difficulty. It is shown that networks trained with BP-SOM show interesting regularities, in that hidden-unit activations become restricted to discrete values, and that the som part can be exploited for automatic rule extraction.

Improving Product Quality and Reliability with Customer Experience Data

Advance technology development and wide use of the World Wide Web have made it possible for new product development organizations to access multi-sources of data-related customer complaints. However, the number of customer plaints of highly innovative consumer electronic products is still increasing; that is, product quality and reliability is at risk. This article aims to understand why existing solutions from literature as well as from industry to deal with these increasingly complex multiple data sources are not able to manage product quality and reliability. Three case studies in industry are discussed. On the basis of the case study results, this article also identifies a new research agenda that is needed to improve product quality and reliability under this circumstance. Copyright (c) 2011 John Wiley & Sons, Ltd.

Genetic rule induction at an intermediate level

Abstract Lists of if–then rules (i.e. ordered rule sets) are among the most expressive and intelligible representations for inductive learning algorithms. Two extreme strategies searching for such a list of rules can be distinguished: (i) local strategies primarily based on a step-by-step search for the optimal list of rules, and (ii) global strategies primarily based on a one-strike search for the optimal list of rules. Both approaches have their disadvantages. In this paper we present an intermediate strategy. A sequential covering strategy is combined with a one-strike genetic search for the next most promising rule. To achieve this, a new rule-fitness function is introduced. Experimental results on benchmark problems are presented and the performance of our intermediate approach is compared with other rule learning algorithms. Finally, GeSeCos performance is compared to a more local strategy on a set of tasks in which the information value of individual attributes is varied.

Preface (BPI 2005)

Business process intelligence (BPI) is an emerging area that has been increasing in importance during the last few years as a result of the pressing need for companies to improve the business processes underlying their business operations so as to better meet their business goals. A number of groups in different research areas are working on technologies to support different aspects of BPI, even if they do not call it this.

Modularity in inductively-learned word pronunciation systems

In leading morpho-phonological theories and state-of-the-art text-to-speech systems it is assumed that word pronunciation cannot be learned or performed without in-between analyses at several abstraction levels (e.g., morphological, graphemic, phonemic, syllabic, and stress levels). We challenge this assumption for the case of English word pronunciation. Using igtree, an inductive-learning decision-tree algorithms, we train and test three word-pronunciation systems in which the number of abstraction levels (implemented as sequenced modules) is reduced from five, via three, to one. The latter system, classifying letter strings directly as mapping to phonemes with stress markers, yields significantly better generalisation accuracies than the two multi-module systems. Analyses of empirical results indicate that positive utility effects of sequencing modules are outweighed by cascading errors passed on between modules.

Introduction to the third workshop on business process intelligence (BPI 2007)

Business process intelligence (BPI) is quickly gaining interest and importance in research industry. BPI refers to the application of various measurement and analysis techniques in the area of business process management to provide a better understanding and a more appropriate support of a company’s processes at design time and the way they are handled at runtime. The Call for Papers for this workshop attracted 16 international submissions. Each paper was reviewed by at least three members of the Program Committee and the eight best papers were selected for presentation at the workshop. In addition, the workshop included of a keynote and a roundtable. In his keynote talk “DataMining: Practical Challenges in Analyzing Performance” M. Genrich addressed challenges which arise when applying process performance analysis in practices. Genrich pointed out that events logs are often not sufficient for process analysis, and that the business context has to be considered carefully before drawing conclusions from the data.