Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining "vague" when there is not enough "evidence" in the data or standard modeling constructs do not "fit". Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.Comment: 25 pages, 12 figure

Partial-order-based process mining: a survey and outlook

The field of process mining focuses on distilling knowledge of the (historical) execution of a process based on the operational event data generated and stored during its execution. Most existing process mining techniques assume that the event data describe activity executions as degenerate time intervals, i.e., intervals of the form [t, t], yielding a strict total order on the observed activity instances. However, for various practical use cases, e.g., the logging of activity executions with a nonzero duration and uncertainty on the correctness of the recorded timestamps of the activity executions, assuming a partial order on the observed activity instances is more appropriate. Using partial orders to represent process executions, i.e., based on recorded event data, allows for new classes of process mining algorithms, i.e., aware of parallelism and robust to uncertainty. Yet, interestingly, only a limited number of studies consider using intermediate data abstractions that explicitly assume a partial order over a collection of observed activity instances. Considering recent developments in process mining, e.g., the prevalence of high-quality event data and techniques for event data abstraction, the need for algorithms designed to handle partially ordered event data is expected to grow in the upcoming years. Therefore, this paper presents a survey of process mining techniques that explicitly use partial orders to represent recorded process behavior. We performed a keyword search, followed by a snowball sampling strategy, yielding 68 relevant articles in the field. We observe a recent uptake in works covering partial-order-based process mining, e.g., due to the current trend of process mining based on uncertain event data. Furthermore, we outline promising novel research directions for the use of partial orders in the context of process mining algorithms

Detection and localization of early- and late-stage cancers using platelet RNA

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I–IV cancer patients and in half of 352 stage I–III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening

Stochastic-Aware Conformance Checking: An Entropy-Based Approach

Business process management (BPM) aims to support changes and innovations in organizations’ processes. Process mining complements BPM with methods, techniques, and tools that provide insights based on observed executions of business processes recorded in event logs of information systems. State-of-the-art discovery and conformance techniques completely ignore or only implicitly consider the information about the likelihood of processes, which is readily available in event logs, even though such stochastic information is necessary for simulation, prediction and recommendation in models. Furthermore, stochastic information can provide business analysts with further actionable insights on frequent and rare conformance issues. In this paper, we propose precision and recall conformance measures based on the notion of entropy of stochastic automata that are capable of quantifying, and thus differentiating, frequent and rare deviations between an event log and a process model. The feasibility of using the proposed precision and recall measures in industrial settings is demonstrated by an evaluation over several real-world datasets supported by our open-source implementation

Translating Workflow Nets to Process Trees : An Algorithmic Approach

Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a sound workflow net is trivial. However, the reverse is not the case. Simultaneously, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to reason on its behavior more easily. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, if the algorithm finds a process tree, the language of the process tree is equal to the language of the original WF-net. The experiments conducted show that the algorithm’s corresponding implementation has a quadratic time complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree rediscoverability.</p

Using Multi-Level Information in Hierarchical Process Mining: Balancing Behavioural Quality and Model Complexity

Process mining techniques aim to derive knowledge of the execution of processes, by means of automated analysis of behaviour recorded in event logs. A well-known challenge in process mining is to strike an adequate balance between the behavioural quality of a discovered model compared to the event log and the model’s complexity as perceived by stakeholders. At the same time, events typically contain multiple attributes related to parts of the process at different levels of abstraction, which are often ignored by existing process mining techniques, resulting in either highly complex and/or incomprehensible process mining results. This paper addresses this problem by extending process mining to use event-level attributes readily available in event logs. We introduce (1) the concept of multi-level logs and generalise existing hierarchical process models, which support multiple modelling formalisms and notions of activities in a single model, (2) a framework, instantiation and implementation for process discovery of hierarchical models, and (3) a corresponding conformance checking technique. The resulting framework has been implemented as a plug-in of the open-source process mining framework ProM, and has been evaluated qualitatively and quantitatively using multiple real-life event logs

A process mining impacts framework

PurposeProcess mining (PM) specialises in extracting insights from event logs to facilitate the improvement of an organisation’s business processes. Industry trends show the proliferation and continued growth of PM techniques. To address the minimal attention given to developing empirically supported frameworks to assess the nature of impact in the PM domain, this study proposes a framework that identifies the key categories of PM impacts and their interrelationships. Design/methodology/approachThe qualitatively derived framework is built, re-specified and validated from a diverse collection of 62 PM case reports. With multiple rounds of coding supported by coder corroborations, inductively extracted concepts relating to impact from a first set of 12 case reports were grouped into themes and sub-themes to derive an a-priori framework by adopting the Balanced Scorecard as a theoretical lens. Concepts from the remaining 50 case reports were deductively grouped to re-specify and validate the proposed PM Impacts Framework. Further analysis identified interrelationships between impacts, which extends our understanding of the identified PM impacts. FindingsThe proposed framework captures PM impacts in four main categories: (a) impact on the process, (b) customer impact, (c) financial impact, and (d) impact on innovation and learning. We extended our analysis to identify the interrelationships between these categories, which vividly demonstrates how impact on the process mediates the attainment of the other three impact types.Originality/valueThe need for a deeper understanding of PM impacts within the context of contemporary PM practice is addressed by this work. Our PM Impacts Framework provides a classification of PM impacts into four categories with 19 subcategories. It also identifies direct, moderating and mediating relationships between categories and subcategories whilst highlighting the role of impact on the process as a precursor to the other types of PM impact

State Snapshot Process Discovery on Career Paths of Qing Dynasty Civil Servants

In process mining, computational processing of sequential data allows the discovery and analysis of processes followed by organisations. These can be either explicitly understood processes, captured in documents or rules, or implicit process paths known in more informal or emergent ways. This paper examines a long-lived institution of historical interest, the Qing (1644-1911) Chinese civil service, using data assembled by historians on civil officials during the 19th century. Mapping the promotion process by following paths of officials through civil service postings helps illuminate the everyday operation of the institution and the society around it. Two distinctive features of this data set are that it records states, not events, and careers often include holding multiple concurrent roles. The combination is a poor match for existing process discovery techniques. We describe this structure as a state snapshot log, and present a new discovery technique, the State Snapshot miner, for constructing stochastic Petri net models from such logs. A case study shows its use in analysing promotion paths for elite graduates in the Qing civil service

Automated process discovery

An event log contains a historical record of the steps taken in a business process. An event log consists of traces, one for each case, customer, order, etc. in the process. A trace contains events, which represent the steps (activities) that were taken for a particular case, customer, order, etc.An example of an event log derived from an insurance claim handling process is [〈receive claim, check difficulty, decide claim, notify customer〉10, 〈receive claim, check difficulty, check fraud, decide claim, notify customer〉5]. This event log consists of 15 traces, corresponding to 15 claims made in the process. In 10 of these traces, the claim was received, its difficulty assessed, the claim was decided and the customer was notified..