Benoît Depaire

Traffic accident segmentation by means of latent class clustering.

Traffic accident data are often heterogeneous, which can cause certain relationships to remain hidden. Therefore, traffic accident analysis is often performed on a small subset of traffic accidents or several models are built for various traffic accident types. In this paper, we examine the effectiveness of a clustering technique, i.e. latent class clustering, for identifying homogenous traffic accident types. Firstly, a heterogeneous traffic accident data set is segmented into seven clusters, which are translated into seven traffic accident types. Secondly, injury analysis is performed for each cluster. The results of these cluster-based analyses are compared with the results of a full-data analysis. This shows that applying latent class clustering as a preliminary analysis can reveal hidden relationships and can help the domain expert or traffic safety researcher to segment traffic accidents.

Family Firm Types Based on the Professionalization Construct: Exploratory Research

This article responds to the calls from the research field to find effective ways to distinguish between different categories of family firms. The authors contribute to this literature by extending and refining previous family firm typologies. To attain this objective, the authors introduce the professionalization construct as basis for distinguishing family firms. As this construct is often approached in an oversimplified, one-dimensional manner, they first conduct an exploratory factor analysis to reveal its multidimensional nature. Based on these results, drawn from a representative sample of 532 Belgian family businesses, a cluster analysis facilitates a distinction between different “types” of family firms based on a multidimensional conceptualization of firm professionalization.

The Effect of Family Business Professionalization as a Multidimensional Construct on Firm Performance

In family business literature, business professionalization is often simplified into a binary characteristic, that is, the presence of a nonfamily manager. We contend that other professionalization features, which may act simultaneously, can influence firm performance. This study addresses professionalization as a multidimensional construct, as intended by general management literature, and assesses the impact on business performance based on these underlying dimensions. Using a representative sample of 523 private elgian family businesses, we identify five different dimensions of the professionalization construct by means of an exploratory factor analysis. Further regression results revealed significant positive effects of increasing nonfamily involvement, implementing human resource control systems, and/or decentralizing authority on firm performance. However, nonfamily involvement only seems to improve firm performance if there is sufficient decentralization of authority and an average or even low amount of formal financial control systems.

A Process Deviation Analysis – A Case Study

Processes are not always executed as expected. Deviations assure the necessary flexibility within a company, but also increase possible internal control weaknesses. Since the number of cases following such a deviation can grow very large, it becomes difficult to analyze them case-by-case. This paper proposes a semi-automatic process deviation analysis method which combines process mining with association rule mining to simplify the analysis of deviating cases. Association rule mining is used to group deviating cases into business rules according to similar attribute values. Consequently, only the resulting business rules need to be examined on their acceptability which makes the analysis less complicated. Therefore, this method can be used to support the search for internal control weaknesses.

Features for art painting classification based on vector quantization of MPEG-7 descriptors

An approach for extracting higher-level visual features for art painting classification based on MPEG-7 descriptors is presented in this paper. The MPEG 7 descriptors give a good presentation of different types of visual features, but are complex structures. This prevents their direct use into standard classification algorithms and thus requires specific processing. Our approach consists of the following steps: (1) the images are tiled into non-overlapping rectangles to capture more detailed information; (2) the tiles of the images are clustered for each MPEG 7 descriptor; (3) vector quantization is used to assign a unique value to each tile, which corresponds to the number of the cluster where the tile belongs to, in order to reduce the dimensionality of the data. Finally, the significance of the attributes and the importance of the underlying MPEG 7 descriptors for class prediction in this domain are analyzed.

Does Process Mining Add to Internal Auditing? An Experience Report

In this paper we report on our experiences of applying business process mining in a real business context. The context for the application is using process mining for the purpose of internal auditing of a procurement cycle in a large multinational financial institution. One of the targeted outcomes of an internal audit is often the reporting on internal controls over financial reporting (ICFR), since this reporting is mandatory for Sarbanes-Oxley regulated organisations. Our process mining analyses resulted in more identified issues concerning ICFR than the traditional auditing approach. Issues that were identified using process mining analysis concerned violations of the segregation of duties principle, payments without approval, and violations of company specific internal procedures.

The Use of Process Mining in Business Process Simulation Model Construction

The paper focuses on the use of process mining (PM) to support the construction of business process simulation (BPS) models. Given the useful BPS insights that are available in event logs, further research on this topic is required. To provide a solid basis for future work, this paper presents a structured overview of BPS modeling tasks and how PM can support them. As directly related research efforts are scarce, a multitude of research challenges are identified. In an effort to provide suggestions on how these challenges can be tackled, an analysis of PM literature shows that few PM algorithms are directly applicable in a BPS context. Consequently, the results presented in this paper can encourage and guide future research to fundamentally bridge the gap between PM and BPS.

A Process Deviation Analysis Framework

Process deviation analysis is becoming increasingly important for companies. This paper presents a framework which structures the field of process deviation analysis and identifies new research opportunities. Application of the framework starts from managerial questions which relate to specific deviation categories and methodological steps. Finally a general outline to detect high-level process deviations is formulated.

Towards a Suitable Reconciliation of the Findings in Collaborative Fuzzy Clustering

This study is concerned with the application of multi-objective particle swarm optimization (MOPSO) approaches to the framework of collaborative fuzzy clustering. In particular, the emphasis lies in determining the collaboration matrix between the data repositories. By using fitness functions both at the level of data and information granules, we can provide a more effective way of reconciling the findings between the participating data sites. A practical application of the proposed methodology to marketing research is presented.

The use of process mining in a business process simulation context: Overview and challenges

This paper focuses on the potential of process mining to support the construction of business process simulation (BPS) models. To date, research efforts are scarce and have a rather conceptual nature. Moreover, publications fail to explicit the complex internal structure of a simulation model. The current paper outlines the general structure of a BPS model. Building on these foundations, modeling tasks for the main components of a BPS model are identified. Moreover, the potential value of process mining and the state of the art in literature are discussed. Consequently, a multitude of promising research challenges are identified. In this sense, the current paper can guide future research on the use of process mining in a BPS context.