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Process discovery is an important area in the field of process mining. To help advance this area, a process discovery contest (PDC) has been set up, which allows us to compare different approaches. At the moment of writing, there have been three instances of the PDC: in 2016, in 2017, and in 2019. This paper introduces the winning contribution to the PDC 2019, called the Log Skeleton Visualizer. This visualizer uses a novel type of process models called log skeletons. In contrast with many workflow net-based discovery techniques, these log skeletons do not rely on the directly follows relation. As a result, log skeletons offer circumstantial information on the event log at hand rather than only sequential information. Using this visualizer, we were able to classify 898 out of 900 traces correctly for the PDC 2019 and to win this contest.

The problem of automated discovery of process models from event logs has been intensively researched in the past two decades. Despite a rich field of proposals, state-of-the-art automated process discovery methods suffer from two recurrent deficiencies when applied to real-life logs: (i) they produce large and spaghetti-like models; and (ii) they produce models that either poorly fit the event log (low fitness) or over-generalize it (low precision). Striking a trade-off between these quality dimensions in a robust and scalable manner has proved elusive. This paper presents an automated process discovery method, namely Split Miner, which produces simple process models with low branching complexity and consistently high and balanced fitness and precision, while achieving considerably faster execution times than state-of-the-art methods, measured on a benchmark covering twelve real-life event logs. Split Miner combines a novel approach to filter the directly-follows graph induced by an event log, with an approach to identify combinations of split gateways that accurately capture the concurrency, conflict and causal relations between neighbors in the directly-follows graph. Split Miner is also the first automated process discovery method that is guaranteed to produce deadlock-free process models with concurrency, while not being restricted to producing block-structured process models.

Traditional approaches for process modeling and process analysis tend to focus on one type of object (also referred to as cases or instances), and each event refers to precisely one such object. This simplifies modeling and analysis, e.g., a process model merely describes the lifecycle of one object (e.g., a production order or an insurance claim) in terms of its activities (i.e., event types). However, in reality, there are often multiple objects of different types involved in an event. Think about filling out an electronic form referring to one order, one customer, ten items, three shipments, and one invoice. Object-centric process mining (OCPM) takes a more holistic and more comprehensive approach to process analysis and improvement by considering multiple object types and events that involve any number of objects. This paper introduces object-centric event data (OCED) and shows how these can be used to discover, analyze, and improve the fabric of real-life, highly intertwined processes. This tutorial-style paper presents the basic concepts, object-centric process-mining techniques, examples, and formalizes OCED. Fully embracing object centricity provides organizations with a “three-dimensional” view of their processes, showing how they interact with each other, and where the root causes of performance and compliance problems lie.

Adolescents' ethnic-racial identity (ERI) formation represents an important developmental process that is associated with adjustment. The Identity Project intervention, grounded in developmental theory, was designed to engage adolescents in the ERI processes of exploration and resolution. The current small-scale efficacy trial involved an ethnic-racially diverse sample of adolescents (N = 215; Mage = 15.02, SD = 68) from eight classrooms that were randomly assigned by classroom to the intervention or attention control group. Differences between conditions in ERI exploration at Time 2 were consistent with desired intervention effects; furthermore, higher levels of ERI exploration at Time 2 predicted increases in ERI resolution at Time 3 only for youth in the treatment condition. Findings provide preliminary evidence of program efficacy.

Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important steps in process mining. Many discovery techniques have been proposed, but none of them combines scalability with strong quality guarantees. We would like such techniques to handle billions of events or thousands of activities, to produce sound models (without deadlocks and other anomalies), and to guarantee that the underlying process can be rediscovered when sufficient information is available. In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framework. To measure the quality of discovered models for such large logs, we introduce a model–model and model–log comparison framework that applies a divide-and-conquer strategy to measure recall, fitness, and precision. We experimentally show that these discovery and measuring techniques sacrifice little compared to other algorithms, while gaining the ability to cope with event logs of 100,000,000 traces and processes of 10,000 activities on a standard computer.

Epistemic curiosity is a desire for knowledge accompanied by positive emotions, increased arousal, and exploratory behavior (Grossnickle, Educational Psychology Review, 28(1), 23–60, 2016). Although curiosity has typically been characterized as a domain-general construct, domain-general conceptualizations do not acknowledge systematic changes in an individuals' development (e.g., domain knowledge) as they advance within a domain. Moreover, a domaingeneral conceptualization of curiosity stands in direct contrast to research on interest, given that interest is typically described as domain-specific (e.g., interest for mathematics). Without a domain-specific conceptualization of curiosity as it relates to development within academic domains, comparisons between curiosity and interest will remain muddled. In the present theoretical review, we put forward a conceptualization of curiosity as domain-specific and examine how the components of curiosity develop within one academic domain: mathematics. In doing so, we juxtapose conceptualizations of epistemic curiosity with literature related to the development of other epistemic factors (i.e., knowledge, epistemic beliefs) in mathematics. Specifically, we build on the knowledge gap theories of epistemic curiosity (Litman, Personality and Individual Differences, 48(4), 397-410, 2010; Loewenstein, Psychological Bulletin, 116(1), 75-98, 1994) to consider developmental shifts in (a) knowledge gaps, (b) heightened arousal, and (c) exploratory behaviors within the domain of mathematics. Understanding the domain-specific and developmental nature of curiosity is critical for distinguishing curiosity from interest and for supporting motivation within mathematics classrooms.

In the business process lifecycle, models can be approached from two perspectives: on the one hand, models are used to create systems in the design phase, and on the other hand, systems in use produce (event) logs that are used to discover the models representing the structure of the systems. These discovered models can be the starting point of a new cycle of analysis, redesign, implementation, etc. Therefore, proper logging of implemented processes in line with system design is a critical element for process discovery. Recently, the consideration of the integration of data and process aspects has seen a surge in interest in both the model-for-design domain as in the automated-model-discovery domain. However, it seems that these domains use different conceptualizations of data/object-aware systems. A definition of how the captured event logs are related to the structure of the global system they are extracted from or are trying to discover is still missing. Especially the concept of an event needs to be aligned, as this is the main concept that the domains have in common. This paper investigates the concepts and terminology used in the different phases of the business process lifecycle: the design phase, the implementation phase (including the implementation of logging) and the discovery phase. The paper contains an extensive running example that is used to illustrate five misalignment issues. The main contribution of this paper is a meta-model that presents a unified terminology for modelling both domains and is demonstrated using the running example. The paper also shows how the concepts of iDOCEM relate to the concepts of a conceptual modelling approach and several event logging formats. iDOCEM is validated with the implementation of a log generator for the running case, demonstrating the feasibility of generating DOCEL-compliant logs from an application.

When freely viewing a scene, the eyes often return to previously visited locations. By tracking eye movements and coregistering eye movements and EEG, such refixations are shown to have multiple roles: repairing insufficient encoding from precursor fixations, supporting ongoing viewing by resampling relevant locations prioritized by precursor fixations, and aiding the construction of memory representations. All these functions of refixation behavior are understood to be underpinned by three oculomotor and cognitive systems and their associated brain structures. First, immediate saccade planning prior to refixations involves attentional selection of candidate locations to revisit. This process is likely supported by the dorsal attentional network. Second, visual working memory, involved in maintaining task-related information, is likely supported by the visual cortex. Third, higher-order relevance of scene locations, which depends on general knowledge and understanding of scene meaning, is likely supported by the hippocampal memory system. Working together, these structures bring about viewing behavior that balances exploring previously unvisited areas of a scene with exploiting visited areas through refixations.

Here or There (HOT) instruction is a blended synchronous approach that enables students from on-campus (“here”) or a remote location (“there”) to participate together in class activities in real time. The purpose of this article is to share three different cases at two universities that illustrate different implementations of HOT instruction, explain the affordances of these varied approaches, provide best practices that are common to each, and share lessons learned along the way. Readers will gain a better understanding of how to implement a range of innovative HOT approaches, and in what context(s) they might choose one approach over another. The authors’ experience indicates that sound pedagogical principles along with pragmatic considerations, such as class size, available technology, and instructor’s skills, should guide decisions regarding use of these blended synchronous approaches. Future research should look towards what impact blended synchronous environments have on student outcomes.

Community-based participatory research (CBPR) is uniquely suited to engage immigrants in all aspects of research, from research question development to data collection to interpretation and dissemination of results. An increasing number of research studies have utilized the methodology for exploring complex health issues for immigrants. In the current manuscript, we present a review of peer-reviewed articles in health-related research where CBPR was conducted in partnership with immigrants. We examined the role of immigrants in the CBPR process and how immigrant involvement improved/enhanced the research rigor. A total of 161 articles met the inclusion criteria. The results of this literature review enhance our understanding of how CBPR can be used in direct collaboration with immigrants and highlights the many potential benefits for both researchers and immigrant communities.