Filip Caron

Comprehensive rule-based compliance checking and risk management with process mining

Process mining researchers have primarily focused on developing and improving process discovery techniques, while attention for the applicability of process mining has been below par. As a result, there only exists a partial fit with the traditional requirements for compliance checking and risk management. This paper proposes a comprehensive rule-based process mining approach for a timely investigation of a complete set of enriched process event data. Additionally, the contribution elaborates a two-dimensional business rule taxonomy that serves as a source of business rules for the comprehensive rule-based compliance checking approach. Finally, the study provides a formal grounding for and an evaluation of the comprehensive rule-based compliance checking approach.

Declarative business process modelling: principles and modelling languages

The business process literature has proposed a multitude of business process modelling approaches or paradigms, each in response to a different business process type with a unique set of requirements. Two polar paradigms, i.e. the imperative and the declarative paradigm, appear to define the extreme positions on the paradigm spectrum. While imperative approaches focus on explicitly defining how an organisational goal should be reached, the declarative approaches focus on the directives, policies and regulations restricting the potential ways to achieve the organisational goal. In between, a variety of hybrid-paradigms can be distinguished, e.g. the advanced and adaptive case management. This article focuses on the less-exposed declarative approach on process modelling. An outline of the declarative process modelling and the modelling approaches is presented, followed by an overview of the observed declarative process modelling principles and an evaluation of the declarative process modelling approaches.

Acquiring logistics process intelligence: Methodology and an application for a Chinese bulk port

The processes of logistics service providers are considered as highly human-centric, flexible and complex. Deviations from the standard operating procedures as described in the designed process models, are not uncommon and may result in significant uncertainties. Acquiring insight in the dynamics of the actual logistics processes can effectively assist in mitigating the uncovered risks and creating strategic advantages, which are the result of uncertainties with respectively a negative and a positive impact on the organizational objectives. In this paper a comprehensive methodology for applying process mining in logistics is presented, covering the event log extraction and preprocessing as well as the execution of exploratory, performance and conformance analyses. The applicability of the presented methodology and roadmap is demonstrated with a case study at an important Chinese port that specializes in bulk cargo.

Advanced Care-Flow Mining and Analysis

Health-care processes are typically human-centric processes characterized by heterogeneity and a multi-disciplinary nature. This contribution gives an executive summary of our submission for the 2011 Business Process Intelligence Challenge. We proposed both the department-based sub processes and specific treatement/drug focus as new process mining techniques that result in useful information.

Monitoring care processes in the gynecologic oncology department

The care processes of healthcare providers are typically considered as human-centric, flexible, evolving, complex and multi-disciplinary. Consequently, acquiring an insight in the dynamics of these care processes can be an arduous task. A novel event log based approach for extracting valuable medical and organizational information on past executions of the care processes is presented in this study. Care processes are analyzed with the help of a preferential set of process mining techniques in order to discover recurring patterns, analyze and characterize process variants and identify adverse medical events.

A comprehensive investigation of the applicability of process mining techniques for enterprise risk management

Process mining techniques and tools perfectly complement the existing set of enterprise risk management approaches. Enterprise risk management aims at minimizing the negative effects of uncertainty on the objectives, while at the same time promoting the potential positive effects. Process mining research has proposed a broad range of techniques and tools that could be used to effectively support the activities related to the different phases of risk management. This paper contributes to the process mining and risk management research by providing a full exploration of the applicability of process mining in the context of the eight components of the COSO Enterprise Risk Management Framework. The identified applications will be illustrated based on the risks involved in insurance claim handling processes.

Getting a grasp on clinical pathway data: an approach based on process mining

Since healthcare processes are pre-eminently heterogeneous and multi-disciplinary, information systems supporting these processes face important challenges in terms of design, implementation and diagnosis. Nonetheless, streamlining clinical pathways with the purpose of delivering high quality care while at the same time reducing costs is a promising goal. In this paper, we propose a methodology founded on process mining for intelligent analysis of clinical pathway data. Process mining can be considered a valuable approach to obtain a better understanding about the actual way of working in human-centric processes such as clinical pathways by investigating the event data as recorded in healthcare information systems. However, capturing tangible knowledge from clinical processes with their ad hoc and complex nature proves difficult. Accordingly, this paper proposes a data analysis methodology focussing on the extraction of tangible insights from clinical pathway data by adopting both a drill up and a drill down perspective.

A process mining-based investigation of adverse events in care processes.

This paper proposes the Clinical Pathway Analysis Method (CPAM) approach that enables the extraction of valuable organisational and medical information on past clinical pathway executions from the event logs of healthcare information systems. The method deals with the complexity of real-World clinical pathways by introducing a perspective-based segmentation of the date-stamped event log. CPAM enables the clinical pathway analyst to effectively and efficiently acquire a profound insight into the clinical pathways. By comparing the specific medical conditions of patients with the factors used for characterising the different clinical pathway variants, the medical expert can identify the best therapeutic option. Process mining-based analytics enables the acquisition of valuable insights into clinical pathways, based on the complete audit traces of previous clinical pathway instances. Additionally, the methodology is suited to assess guideline compliance and analyse adverse events. Finally, the methodology provides support for eliciting tacit knowledge and providing treatment selection assistance.

A Prototype Tool for the Event-Driven Enforcement of SBVR Business Rules

Business rules define and constrain various aspects of the business, such as vocabulary, control-flow and organizational issues. Although the presence of many languages for expressing business rules that differ in expressivenes, knowledge representation mechanism and execution model, none of these cover all the necessary business aspects. In this paper, we show how business rules, not only vocabulary rules, but also control-flow rules and organizational rules can be expressed in SBVR and translated using patterns into a more uniform event mechanism, such that the event handling could provide an integrated enforcement of business rules of many kinds. As a proof of concept a prototype tool integrates this pattern mechanism and provides an execution environment in which these rules are enforced.

Modeling Decision Structures and Dependencies

Decisions are often not adequately modeled. They are hardcoded in process models or other representations, but not always modeled in a systematic way. Because of this hardcoding or inclusion in other models, organizations often lack the necessary flexibility, maintainability and traceability in their business operations.