Considering the presence of large amounts of data in organizations today, the need to transform this data into useful information and subsequently into knowledge, increasingly gains attention. Process discovery is a technique to automatically discover process models from data in event logs. Since process discovery is gaining attention among researchers as well as practitioners, the quality of the resulting process model must be assured.In this paper, the quality of the frequently used Heuristics Miner is improved as anomalies were found concerning the validity and completeness of the resulting process model. For this purpose, a new artifact called the Updated Heuristics Miner was constructed containing alterations to the tool and to the algorithm itself. Evaluations of this artifact resulted in the conclusion that the Updated Heuristics Miner indeed demonstrates higher validity and completeness. This study contributes to the body of knowledge first by improving the quality of the an often used research instrument and second by stating that there is a need for a systematic developing and evaluation method for process discovery techniques.
Customer loyalty programs are largely present in the private sector and have been elaborately studied. Applications from the private sector have found resonance in a public setting, however, simply extrapolating research results is not acceptable, as their rationale inherently differs. This study focuses on data from a loyalty program issued by the city of Antwerp (Belgium). The aim of the loyalty card entails large citizen participation, however, an active user base of only 20 % is reached. Predictive techniques are employed to increase this number. Using spatial behavioral user information, a Naive Bayes classifier and a Support Vector Machine are used which result in models capable of predicting whether a user will actively use its card, whether a user will defect in the near future and which locations a user will visit. Also, a projection of spatial behavioral data onto even more finegrained spatio-temporal data is performed. The results are promising: the best model achieves an AUC value of 92.5 %, 85.5 % and 88.12 % (averaged over five locations) for the predictions, respectively. Moreover, as behavior is modeled in more detail, better predictions are made. Two main contributions are made in this study. First, as a theoretical contribution, fine-grained behavioral data contributes to a more sound decision-making process. Second, as a practical contribution, the city of Antwerp can now make tailored strategic decisions to increase its active user base.
The predictive power in ubiquitous big, behavioral data has been emphasized by previous academic research. The ultra-high dimensional and sparse characteristics, however, pose significant challenges on state-of-the-art classification techniques. Moreover, no consensus exists regarding a feasible trade-off between classification performance and computational complexity. This work provides a contribution in this direction through a systematic benchmarking study. Forty-three fine-grained behavioral data sets are analyzed with 11 classification techniques. Statistical performance comparisons enriched with learning curve analyses demonstrate two important findings. Firstly, an inherent AUC-time trade-off becomes clear, making the choice for an appropriate classifier dependent on time restrictions and data set characteristics. Logistic regression achieves the best AUC, however in the worst amount of time. Also, L2 regularization proves better than sparse L1-regularization. An attractive trade-off is found in a similarity-based technique called PSN. Secondly, the results illustrate that significant value lies in collecting and analyzing even more data, both in the instance and in the feature dimension, contrasting findings on traditional data. The results of this study provide guidance for researchers and practitioners for the selection of appropriate classification techniques, sample sizes and data features, while also providing focus in scalable algorithm design in the face of large, behavioral data.
The outstanding performance of deep learning (DL) for computer vision and natural language processing has fueled increased interest in applying these algorithms more broadly in both research and practice. This study investigates the application of DL techniques to classification of large sparse behavioral data-which has become ubiquitous in the age of big data collection. We report on an extensive search through DL architecture variants and compare the predictive performance of DL with that of carefully regularized logistic regression (LR), which previously (and repeatedly) has been found to be the most accurate machine learning technique generally for sparse behavioral data. At a high level, we demonstrate that by following recommendations from the literature, researchers and practitioners who are not DL experts can achieve world-class performance using DL. More specifically, we report several findings. As a main result, applying DL on 39 big sparse behavioral classification tasks demonstrates a significant performance improvement compared with LR. A follow-up result suggests that if one were to choose the best shallow technique (rather than just LR), there still would often be an improvement from using DL, but that in this case the magnitude of the improvement might not justify the high cost. Investigating when DL performs better, we find that worse performance is obtained for data sets with low signal-from-noise separability-in line with prior results comparing linear and nonlinear classifiers. Exploring why the deep architectures work well, we show that using the first-layer features learned by DL yields better generalization performance for a linear model than do unsupervised feature-reduction methods (e.g., singular-value decomposition). However, to do well enough to beat well-regularized LR with the original sparse representation, more layers from the deep distributed architecture are needed. With respect to interpreting how deep models come to their decisions, we demonstrate how the neurons on the lowest layer of the deep architecture capture nuances from the raw fine-grained features and allow intuitive interpretation. Looking forward, we propose the use of instance-level counterfactual explanations to gain insight into why deep models classify individual data instances the way they do.
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