bluuebunny/crossref_metadata_2025_split huggingface dataset:
Food Systems, Resilience, and Their Implications for Public Action
Authors: Hoddinott John 
Published: March 2023

AbstractLinking the concepts of food systems and resilience offers the opportunity to strengthen our understanding of these concepts, the potential they hold for more informed policy discussions, and the design and implementation of interventions that will better deliver on food security outcomes. This chapter outlines how these twin concepts can be linked conceptually and empirically. It argues that while we know much about certain elements of the food system, specifically production and consumption, our understanding of the processing and distribution components of the food system are weak. For example, market structure in the processing sector and market integration can contribute to food system resilience, but these are rarely measured at a country level. This makes efforts to measure resilience at the system-level challenging. Understanding what can make a resilient food system has important implications for policy and intervention design. Building resilient food systems requires that policymakers grapple with trade-offs and tensions such as those between the benefits of diversification versus gains from specialization; and how openness to trade reduces vulnerability to domestic shocks to the food system while exposing it to external shocks. How best to manage these will be an important challenge to address.
Food Security Under a Changing Climate: Exploring the Integration of Resilience in Research and Practice
Authors: De Pinto Alessandro , Islam Md Mofakkarul , Katic Pamela 
Published: March 2023

AbstractClimate change poses significant risks to our food systems, thus jeopardising the food security of millions of people worldwide. The concept of resilience is increasingly being proposed as a framework to find solutions to these challenges. In this chapter, we assess how resilience has been integrated in discussions about climate change and food security by both academics and practitioners. We performed a targeted review of the academic literature on climate change, food security, and resilience and found that despite a growing body of literature on the subject, the pathways through which actions translate into resilience and then into food security remain unclear. An examination of a sample of projects implemented through the Adaptation Fund revealed that many good practices with potential for resilience-building are used but also that suitable indicators and methods to monitor and evaluate resilience and its outcomes are lacking. Based on our findings, we conclude that while the concept of resilience has accompanied and may have favoured a transition towards more integrated approaches and interventions in work related to climate change and food security, further efforts are needed to identify an efficient and rational sequence of interventions to improve food security in response to climate threats.
Gender, Resilience, and Food Systems
Authors: Bryan Elizabeth , Ringler Claudia , Meinzen-Dick Ruth 
Published: March 2023

AbstractResearch on the gender dimensions of resilience highlights differences in the ways that men and women experience disturbances, their resilience capacities, and their preferred responses. This chapter incorporates a food systems lens into a gender and resilience framework to identify key entry points to strengthen women’s and men’s food security and nutrition in the face of multiple, reoccurring shocks and stressors. Drawing on systematic reviews and case studies from the literature, this chapter finds that exposure and sensitivity to disturbances depend largely on gendered roles in food systems, including along agricultural value chains, and the food environments in which men and women live. Increasing women’s resilience capacities—which tend to be lower than men’s—through investments in education, information and financial services, employment opportunities, and women’s agency, can improve food security and nutrition outcomes and increase their contribution to food system resilience. Considering gender differences in needs and preferences in policy and intervention design is, therefore, essential to ensure that investments reach, benefit, and empower women as agents of change for greater resilience.
COVID-19, Household Resilience, and Rural Food Systems: Evidence from Southern and Eastern Africa
Authors: Upton Joanna , Tennant Elizabeth , Fiorella Kathryn J. , Barrett Christopher B. 
Published: March 2023

AbstractResilience offers a useful lens for studying how human well-being and agri-food systems absorb and recover from a range of shocks and stressors, including the COVID-19 pandemic. Looking beyond the direct effects of observable shocks to the mechanisms that shape their impacts can guide our understanding of COVID-19 and leverage findings from the pandemic to better understand resilience to future shocks. We develop a conceptual framework for the multiple paths through which observed shocks interact with systemic mechanisms to influence resilience. We illustrate this framework with reference to the pandemic and policy responses as they unfolded in three rural areas in Malawi, Madagascar, and Kenya. Consistent with this framework, we find multiple pathways through which the pandemic affected household food security and resilience. Our findings highlight that, in some settings, the direct effects—in this case severe illness and mortality from SARS-CoV-2—may impact fewer people than the indirect impacts that arise as behaviors, markets, and policies adjust. We illustrate that although COVID-19 is a new shock, its massive, broad-reaching impacts manifest through familiar stressors and uncertainties that frequently burden poor rural populations in much of the low- and middle-income world.
Do You Behave Always the Same?
Authors: Di Federico Gemma , Burattin Andrea 
Published: March 2023

AbstractHuman behavior could be represented in the form of a process. Existing process modeling notations, however, are not able to faithfully represent these very flexible and unstructured processes. Additional non-process aware perspectives should be considered in the representation. Control-flow and data dimensions should be combined to build a robust model which can be used for analysis purposes. The work in this paper proposes a new hybrid model in which these dimensions are combined. An enriched conformance checking approach is described, based on the alignment of imperative and declarative process models, which also supports data dimensions from a statistical viewpoint.
BERMUDA: Participatory Mapping of Domain Activities to Event Data via System Interfaces
Authors: Cosma Vlad P. , Hildebrandt Thomas T. , Gyldenkærne Christopher H. , Slaats Tijs 
Published: March 2023

AbstractWe present a method and prototype tool supporting participatory mapping of domain activities to event data recorded in information systems via the system interfaces. The aim is to facilitate responsible secondary use of event data recorded in information systems, such as process mining and the construction of predictive AI models. Another identified possible benefit is the support for increasing the quality of data by using the mapping to support educating new users in how to register data, thereby increasing the consistency in how domain activities are recorded. We illustrate the method on two cases, one from a job center in a danish municipality and another from a danish hospital using the healthcare platform from Epic.
Predicting Activities of Interest in the Remainder of Customer Journeys Under Online Settings
Authors: Wolters Lisan , Hassani Marwan 
Published: March 2023

AbstractCustomer journey analysis is important for organizations to get to know as much as possible about the main behavior of their customers. This provides the basis to improve the customer experience within their organization. This paper addresses the problem of predicting the occurrence of a certain activity of interest in the remainder of the customer journey that follows the occurrence of another specific activity. For this, we propose the HIAP framework which uses process mining techniques to analyze customer journeys. Different prediction models are researched to investigate which model is most suitable for high importance activity prediction. Furthermore the effect of using a sliding window or landmark model for (re)training a model is investigated. The framework is evaluated using a health insurance real dataset and a benchmark data set. The efficiency and prediction quality results highlight the usefulness of the framework under various realistic online business settings.
On the Potential of Textual Data for Explainable Predictive Process Monitoring
Authors: Warmuth Christian , Leopold Henrik 
Published: March 2023

AbstractPredictive process monitoring techniques leverage machine learning (ML) to predict future characteristics of a case, such as the process outcome or the remaining run time. Available techniques employ various models and different types of input data to produce accurate predictions. However, from a practical perspective, explainability is another important requirement besides accuracy since predictive process monitoring techniques frequently support decision-making in critical domains. Techniques from the area of explainable artificial intelligence (XAI) aim to provide this capability and create transparency and interpretability for black-box ML models. While several explainable predictive process monitoring techniques exist, none of them leverages textual data. This is surprising since textual data can provide a rich context to a process that numerical features cannot capture. Recognizing this, we use this paper to investigate how the combination of textual and non-textual data can be used for explainable predictive process monitoring and analyze how the incorporation of textual data affects both the predictions and the explainability. Our experiments show that using textual data requires more computation time but can lead to a notable improvement in prediction quality with comparable results for explainability.
Attribute-Based Conformance Diagnosis: Correlating Trace Attributes with Process Conformance
Authors: Grohs Michael , Rehse Jana-Rebecca 
Published: March 2023

AbstractAn important practical capability of conformance checking is that organizations can use it to alleviate potential deviations from the intended process behavior. However, existing techniques only identify these deviations, but do not provide insights on potential explanations, which could help to improve the process. In this paper, we present attribute-based conformance diagnosis (ABCD), a novel approach for correlating process conformance with trace attributes. ABCD builds on existing conformance checking techniques and uses machine learning techniques to find trace attribute values that potentially impact the process conformance. It creates a regression tree to identify those attribute combinations that correlate with higher or lower trace fitness. We evaluate the explanatory power, computational efficiency, and generated insights of ABCD based on publicly available event logs. The evaluation shows that ABCD can find correlations of trace attribute combinations with higher or lower fitness in a sufficiently efficient way, although computation time increases for larger log sizes.
Detecting Surprising Situations in Event Data
Authors: Kohlschmidt Christian , Qafari Mahnaz Sadat , van der Aalst Wil M. P. 
Published: March 2023

AbstractProcess mining is a set of techniques that are used by organizations to understand and improve their operational processes. The first essential step in designing any process reengineering procedure is to find process improvement opportunities. In existing work, it is usually assumed that the set of problematic process instances in which an undesirable outcome occurs is known prior or is easily detectable. So the process enhancement procedure involves finding the root causes and the treatments for the problem in those process instances. For example, the set of problematic instances is considered as those with outlier values or with values smaller/bigger than a given threshold in one of the process features. However, on various occasions, using this approach, many process enhancement opportunities, not captured by these problematic process instances, are missed. To overcome this issue, we formulate finding the process enhancement areas as a context-sensitive anomaly/outlier detection problem. We define a process enhancement area as a set of situations (process instances or prefixes of process instances) where the process performance is surprising. We aim to characterize those situations where process performance is significantly different from what was expected considering its performance in similar situations. To evaluate the validity and relevance of the proposed approach, we have implemented and evaluated it on a real-life event log.
Weighted Violations in Alignment-Based Conformance Checking
Authors: Grüger Joscha , Geyer Tobias , Kuhn Martin , Braun Stephan A. , Bergmann Ralph 
Published: March 2023

AbstractConformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Many conformance checking algorithms provide quantitative information about the conformance of a process instance through metrics such as fitness. Fitness measures to what degree the model allows the behavior observed in the event log. Conventional fitness does not consider the individual severity of deviations. In cases where there are rules that are more important to comply with than others, fitness consequently does not take all factors into account. In the field of medicine, for example, there are guideline recommendations for clinical treatment that have information about their importance and soundness, making it essential to distinguish between them. Therefore, we introduce an alignment-based conformance checking approach that considers the importance of individual specifications and weights violations. The approach is evaluated with real patient data and evidence-based guideline recommendations. Using this approach, it was possible to integrate guideline recommendation metadata into the conformance checking process and to weight violations individually.
Event Log Generation in MIMIC-IV Research Paper
Authors: Cremerius Jonas , Pufahl Luise , Klessascheck Finn , Weske Mathias 
Published: March 2023

AbstractPublic event logs are valuable for process mining research to evaluate process mining artifacts and identify new and promising research directions. Initiatives like the BPI Challenges have provided a series of real-world event logs, including healthcare processes, and have significantly stimulated process mining research. However, the healthcare related logs provide only excerpts of patient visits in hospitals. The Medical Information Mart for Intensive Care (MIMIC)-IV database is a public available relational database that includes data on patient treatment in a tertiary academic medical center in Boston, USA. It provides complex care processes in a hospital from end-to-end. To facilitate the use of MIMIC-IV in process mining and to increase the reproducibility of research with MIMIC, this paper provides a framework consisting of a method, an event hierarchy, and a log extraction tool for extracting useful event logs from the MIMIC-IV database. We demonstrate the framework on a heart failure treatment process, show how logs on different abstraction levels can be generated, and provide configuration files to generate event logs of previous process mining works with MIMIC.
SAMPLE: A Semantic Approach for Multi-perspective Event Log Generation
Authors: Grüger Joscha , Geyer Tobias , Jilg David , Bergmann Ralph 
Published: March 2023

AbstractData and process mining techniques can be applied in many areas to gain valuable insights. For many reasons, accessibility to real-world business and medical data is severely limited. However, research, but especially the development of new methods, depends on a sufficient basis of realistic data. Due to the lack of data, this progress is hindered. This applies in particular to domains that use personal data, such as healthcare. With adequate quality, synthetic data can be a solution to this problem. In the procedural field, some approaches have already been presented that generate synthetic data based on a process model. However, only a few have included the data perspective so far. Data semantics, which is crucial for the quality of the generated data, has not yet been considered. Therefore, in this paper we present the multi-perspective event log generation approach SAMPLE that considers the data perspective and, in particular, its semantics. The evaluation of the approach is based on a process model for the treatment of malignant melanoma. As a result, we were able to integrate the semantic of data into the log generation process and identify new challenges.
Discovering Break Behaviours in Process Mining: An Application to Discover Treatment Pathways in ICU of Patients with Acute Coronary Syndrome
Authors: Chen Qifan , Lu Yang , Tam Charmaine S. , Poon Simon K. 
Published: March 2023

AbstractThe inductive miner (IM) can guarantee to return structured process models, but the process behaviours that process trees can represent are limited. Loops in process trees can only be exited after the execution of the “body” part. However, in some cases, it is possible to break a loop structure in the “redo” part. This paper proposes an extension to the process tree notation and the IM to discover and represent break behaviours. We present a case study using a healthcare event log to explore Acute Coronary Syndrome (ACS) patients’ treatment pathways, especially discharge behaviours from ICU, to demonstrate the usability of the proposed approach in real-life. We find that treatment pathways in ICU are routine behaviour, while discharges from ICU are break behaviours. The results show that we can successfully discover break behaviours and obtain the structured and understandable process model with satisfactory fitness, precision and simplicity.
Multi-perspective Identification of Event Groups for Event Abstraction
Authors: Rebmann Adrian , Pfeiffer Peter , Fettke Peter , Aa Han van der 
Published: March 2023

AbstractIn process mining settings, events are often recorded on a low level and cannot be used for meaningful analysis directly. Moreover, the resulting variability in the recorded event sequences leads to complex process models that provide limited insights. To overcome these issues, event abstraction techniques pre-process the event sequences by grouping the recorded low-level events into higher-level activities. However, existing abstraction techniques require elaborate input about high-level activities upfront to achieve acceptable abstraction results. This input is often not available or needs to be constructed, which requires considerable manual effort and domain knowledge. We overcome this by proposing an approach that suggests groups of low-level events for event abstraction. It does not require the user to provide elaborate input upfront, but still allows them to inspect and select groups of events that are related based on their common multi-perspective contexts. To achieve this, our approach learns representations of events that capture their context and automatically identifies and suggests interesting groups of related events. The user can inspect group descriptions and select meaningful groups to abstract the low-level event log.
Deriving Event Logs from Legacy Software Systems
Authors: Breitmayer Marius , Arnold Lisa , Rocca Stephan La , Reichert Manfred 
Published: March 2023

AbstractThe modernization of legacy software systems is one of the key challenges in software industry, which requires comprehensive system analysis. In this context, process mining has proven to be useful for understanding the (business) processes implemented by the legacy software system. However, process mining algorithms are highly dependent on both the quality and existence of suitable event logs. In many scenarios, existing software systems (e.g., legacy applications) do not leverage process engines capable of producing such high-quality event logs, which hampers the application of process mining algorithms. Deriving suitable event log data from legacy software systems, therefore, constitutes a relevant task that fosters data-driven analysis approaches, including process mining, data-based process documentation, and process-centric software migration. This paper presents an approach for deriving event logs from legacy software systems by combining knowledge from source code and corresponding database operations. The goal is to identify relevant business objects as well as to document user and software interactions with them in an event log suitable for process mining.
SAP Signavio Academic Models: A Large Process Model Dataset
Authors: Sola Diana , Warmuth Christian , Schäfer Bernhard , Badakhshan Peyman , Rehse Jana-Rebecca , Kampik Timotheus 
Published: March 2023

AbstractIn this paper, we introduce the SAP Signavio Academic Models (SAP-SAM) dataset, a collection of hundreds of thousands of business models, mainly process models in BPMN notation. The model collection is a subset of the models that were created over the course of roughly a decade on academic.signavio.com, a free-of-charge software-as-a-service platform that researchers, teachers, and students can use to create business (process) models. We provide a preliminary analysis of the model collection, as well as recommendations on how to work with it. In addition, we discuss potential use cases and limitations of the model collection from academic and industry perspectives.
A Virtual Knowledge Graph Based Approach for Object-Centric Event Logs Extraction
Authors: Xiong Jing , Xiao Guohui , Kalayci Tahir Emre , Montali Marco , Gu Zhenzhen , Calvanese Diego 
Published: March 2023

AbstractProcess mining is a family of techniques that support the analysis of operational processes based on event logs. Among the existing event log formats, the IEEE standard eXtensible Event Stream () is the most widely adopted. In , each event must be related to a single case object, which may lead to convergence and divergence problems. To solve such issues, object-centric approaches become promising, where objects are the central notion and one event may refer to multiple objects. In particular, the Object-Centric Event Logs () standard has been proposed recently. However, the crucial problem of extracting logs from external sources is still largely unexplored. In this paper, we try to fill this gap by leveraging the Virtual Knowledge Graph () approach to access data in relational databases. We have implemented this approach in the system, extending it to support both and standards. We have carried out an experiment with over the Dolibarr system. The evaluation results confirm that can effectively extract logs and the performance is scalable.