Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Process discovery methods automatically infer process models from event logs. Often, event logs contain so-called noise, e.g., infrequent outliers or recording errors, which obscure the main behavior of the process. Existing methods filter this noise based on the frequency of event labels: infrequent paths and activities are excluded. However, infrequent behavior may reveal important insights into the process. Thus, not all infrequent behavior should be considered as noise. This paper proposes the Data-aware Heuristic Miner (DHM), a process discovery method that uses the data attributes to distinguish infrequent paths from random noise by using classification techniques. Data- and control-flow of the process are discovered together. We show that the DHM is, to some degree, robust against random noise and reveals data-driven decisions, which are filtered by other discovery methods. The DHM has been successfully tested on several real-life event logs, two of which we present in this paper

Heuristic mining revamped : an interactive, data-aware, and conformance-aware miner

Process discovery methods automatically infer process models based on events logs that are recorded by information systems. Several heuristic process discovery methods have been proposed to cope with less structured processes and the presence of noise in the event log. However, (1) a large parameter space needs to be explored, (2) several of the many available heuristics can be chosen from, (3) data attributes are not used for discovery, (4) discovered models are not visualized as described in literature, and (5) existing tools do not give reliable quality diagnostics for discovered models. We present the interactive Data-aware Heuristics Miner (iDHM), a modular tool that attempts to address those five issues. The iDHM enables quick interactive exploration of the parameter space and several heuristics. It uses data attributes to improve the discovery procedure and provides built-in conformance checking to get direct feedback on the quality of the model. It is the first tool that visualizes models using the concise Causal Net (C-Net) notation. We provide a walk-through of the iDHM by applying it to a large event log with hospital billing information

Exploring Customer Behavior Patterns: A Process-based Perspective

With the increasingly fierce competition among enterprises, it is important for enterprises to understand customer behaviors accurately in a dynamic environment. While data mining methods have been applied to investigate customer behavior patterns with high-quality objective data, the process perspective has been largely neglected. Given that customer behaviors can be reflected in process event logs, it is possible to mine the real behavior patterns from a process-based perspective. To this aim, this paper presents a method for exploring customer behavior patterns using process mining techniques. The method consists of five steps: data collection and preprocessing, customer service process modeling, identifying deviant behaviors, clustering analysis and discovering customer behavior patterns. This method provides a viable way to understand the customer behavior patterns from a process-based perspective

Process Mining to Discover and Preserve Infrequent Relations in Event Logs: An Application to Understand the Laboratory Test Ordering Process Using the MIMIC-III Dataset

Process mining techniques can provide insights into the healthcare domain with the rapid growth of electrical health records. Process mining is about understanding the sequence of activities in event logs, where directly-follows relations identify pairs of activities that follow each other directly. Existing research explores frequent relations, while infrequent relations are often seen as noises and filtered out during discovery. However, important insights may be revealed through these infrequent relations, especially in healthcare processes. This paper aims to use process mining techniques to discover and preserve value-based conditional infrequent relations. We adopt the L* life-cycle methodology and Data-aware Heuristic Miner (DHM) as tools to provide a worded example based on extracted data from the MIMIC-III dataset, which is a publicly available database containing a large amount of electrical health records (EHR), to show how process mining can be used to analyse infrequent relations in a laboratory test’s ordering process

Leveraging Anomaly Detection in Business Process with Data Stream Mining

Identifying fraudulent or anomalous business procedures is today a key challenge for organisations of any dimension. Nonetheless, the continuous nature of business activities conveys to the continuous acquisition of data in support of business process monitoring. In light of this, we propose a method for online anomaly detection in business processes. From a stream of events, our approach extract cases descriptors and applies a density-based clustering technique to detect outliers. We applied our method to a real-life dataset, and we used streaming clustering measures for evaluating performances. Exploring different combinations of parameters, we obtained promising performance metrics, showing that our method is capable of finding anomalous process instances in a vast complexity of scenarios

Data-driven process discovery: revealing conditional infrequent behavior from event logs

Process discovery methods automatically infer process models from event logs. Often, event logs contain so-called noise, e.g., infrequent outliers or recording errors, which obscure the main behavior of the process. Existing methods filter this noise based on the frequency of event labels: infrequent paths and activities are excluded. However, infrequent behavior may reveal important insights into the process. Thus, not all infrequent behavior should be considered as noise. This paper proposes the Data-aware Heuristic Miner (DHM), a process discovery method that uses the data attributes to distinguish infrequent paths from random noise by using classification techniques. Data- and control-flow of the process are discovered together. We show that the DHM is, to some degree, robust against random noise and reveals data-driven decisions, which are filtered by other discovery methods. The DHM has been successfully tested on several real-life event logs, two of which we present in this paper