bluuebunny/crossref_metadata_2025 huggingface dataset:
Phishing uniform resource locator detection using machine learning: A step towards secure system
Authors: Mahajan Shilpa
Published: March 2023

The advancement in technology has led to increase in cyber‐attacks. Hackers have become more skilled at finding the loopholes in the system and can penetrate easily on to host network. The rate of cybercrimes is increasing exponentially with the growth of digital era. Phishing is considered as one of the top cybercrimes that has impacted the society at large. As per Kaspersky reports 2021, around 22% attacks were phishing attacks. This paper explores methods for detecting phishing uniform resource locator (URLs) by analyzing various features using Machine Learning techniques. Various data mining algorithms are used to learn data patterns that can identify and differentiate between benign and phishing websites using phishing website data set. The best results are shown by an XGBoost Model that provides more than 90% accuracy on the balanced class dataset.
MyEasyHealthcare: An efficient and secure three‐tier blockchain‐based healthcare system
Authors: Agrawal Kanika , Aggarwal Mayank , Tanwar Sudeep
Published: March 2023

Blockchain systems have seen vast growth due to the immense potential in developing secure applications for education, healthcare, and so forth. The healthcare system is extensively researched to provide convenience to human life. With the exponential growth in healthcare systems and devices, patient data security and privacy issues are becoming primary concerns. Blockchain is emerging as a solution to secure healthcare records, but it faces certain shortcomings like transaction time, execution time, gas cost consumption, bandwidth utilization, and so forth. The current article designed an extensive blockchain‐based healthcare system (MyEasyHealthcare) with reduced gas consumption, transaction cost, execution cost, and bandwidth utilization, along with enhanced security at three levels. At the first level, the professionals and patients get registered, which provides identity access management. Secondly, authorization is required for each registered entity by the owners. Lastly, the third level includes a doctor‐patient relationship where the hospital's owner assigns a patient to a particular doctor. The data is protected from the outer world and is preserved only between the doctor and the patient. Moreover, to include the majority of tasks for hospital management, the developed system incorporates a smart contract to record seven different parameters for patient diagnosis by a physician and 15 different parameters by a pathologist. The designed system is evaluated for the amount of gas consumed, transaction cost, execution cost, and bandwidth utilization by simulating/executing the written smart contract on InterPlanetary File System (IPFS) and Remix to check the feasibility of the developed system (MyEasyHealthcare) for the real world; the results testify the proposed system is useful in the real world.
Synthesis and Characterization of Innovative Fe–Cr–Mn Ferritic Stainless Steel by Laser Powder Bed Fusion
Authors: Li Xing Chen , Ma Tie Jun , Du Wen Xiang , Cao Li Ce , Shen Fa Lei , Zhang Yin Ling , Fang Xiao Ying
Published: March 2023

Crack development for laser‐fused metallic materials is one major challenge for laser‐based powder bed fusion (LPBF). The cracks are manifested in two forms, i.e., macrocracks along building layers due to spalling and microcracks along the high‐angle grain boundaries. To mitigate the crack formation, a new type of ferritic stainless steel Fe–Cr–Mn are synthesized by LPBF. The well‐developed columnar ferrite grains with an enhanced fine acicular austenite precipitation, which possess Nishiyama–Wassermann (N–W) orientation relationship with the ferrite matrix, can be readily produced when the laser energy density is beyond 100 J mm−3. Porosity can be further reduced below 0.5% by optimizing the laser power and scanning speed. It is found that laser rescanning may eliminate the cracks without modifying the grain morphology, crystallographic texture, and phase distribution.
Classroom as Neo‐National Microcosm: Teachers Learning to Disrupt Linguistic Microaggressions
Authors: Chang‐Bacon Chris K. , Salerno April S.
Published: March 2023

Neo‐national ideologies are often associated with despots and political extremists. Yet, we argue that neo‐nationalist discourses also manifest in everyday TESOL classroom interactions. In English‐dominant contexts, teachers send potent messages about who does or does not “belong” in classrooms, largely based on racialized notions of English proficiency. These dynamics create a classroom microcosm where students are apprenticed into linguistic normativity and anti‐immigrant sentiments, ultimately sustaining future generations of neo‐nationalist rhetoric. In light of these dynamics, we believe that teachers can play a key role in disrupting normative, monolingual language ideologies in classrooms, but that they often feel underprepared to do so. In this brief research report, we present findings from our ongoing examination of TESOL licensure endorsement candidates responding to deficit‐oriented microaggressions about multilingual learners in a simulated environment. Our results suggest teacher candidates have the desire to promote asset‐based orientations around multilingualism, but are often unable to do so in real time. These findings have important implications for teacher education and the underutilized potential of teachers for disrupting deficit‐oriented monolingual discourses that too easily turn into neo‐nationalist ideologies.
A comprehensive review on how ionic liquids enhance the pyrolysis of cellulose, lignin, and lignocellulose toward a circular economy
Authors: Eqbalpour Mohammad , Andooz Amirhossein , Kowsari Elaheh , Ramakrishna Seeram , Cheshmeh Zahra Ansari , Chinnappan Amutha
Published: March 2023

The sustainable use of plant biomass (PB) to produce new valuable compounds helps alleviate the world's excessive reliance on fossil fuels. Among the different processes available, pyrolysis has drawn significant attention for its efficiency in converting PB (including lignin, hemicellulose, and cellulose) into solid, liquid, and gas products by thermal degradation. Moreover, the participation of ionic liquids (ILs) in the pyrolysis process can further facilitate this process, improve the quality of pyrolysis products, and enhance the operational parameters. This review article presents an in‐depth examination of how ILs enhance the pyrolysis of lignin, cellulose, and lignocellulose toward sustainability and circular economy (CE). The structural chemistry of the components of PB, namely cellulose, lignin, and hemicellulose, is first discussed. Furthermore, the role of ILs in the pyrolysis of cellulose, lignin, and lignocellulose is thoroughly investigated. These roles include pre‐treating agent before pyrolysis, catalyst after or during pyrolysis, template during pyrolysis, and extractant after pyrolysis. In the following, the sustainability of PB pyrolysis with the participation of ILs is examined from three aspects: environmental, social, and economical. Finally, the PB pyrolysis was investigated from the CE aspect. There is no doubt that the participation of ILs in the pyrolysis process positively affects the operating conditions and product quality, so the whole process is only one step away from complete sustainability.

This article is categorized under:

Sustainable Development > Sustainable Development Goals

Sustainable Energy > Bioenergy

Climate and Environment > Circular Economy
Physico‐chemical characteristics and biodegradability of primary effluent and particulate matter removed by microscreens
Authors: Alizadeh Sanaz , Chowdhury Pankaj , Ghodsi Vahid , Giaccherini Francesca , Sarathy Siva , Santoro Domenico , Comeau Yves
Published: March 2023

In this study, the physical, chemical, and biological characteristics of raw wastewater were compared with the liquid and solid streams generated by a primary clarifier (PC), a rotating belt filter (RBF, 350 μm), and a drum filter (DF, 60 μm) and series (SER) combination of an RBF with a PC or a DF using pilot‐scale primary treatment units. The RBF removed about 36% of the influent total suspended solids. The DF and PC yielded an influent total suspended solid removal of 47% to 55% in both individual (parallel) and SER configurations. The size fractionation and chemical characterizations of the liquid fractions indicated a significant change in the wastewater composition in both filter configurations with no variation in the biodegradability of liquid fractions. The solids recovered by RBF had a higher total solids (TS) concentration and a higher volatile solids (VS) content (0.92 g VS/g TS) than that of DF and PC treatments (0.58 to 0.84 g VS/g TS). DF and PC sludge demonstrated a higher biodegradability rate (k1; 0.11 d−1 < k1 < 0.20 d−1) than solids recovered by RBF (0.09 d−1). The retained solids in the SER configuration demonstrated a significantly lower theoretical biochemical methane potential than the parallel configuration, likely due to the presence of smaller particles with a significantly higher ratio of particulate chemical oxygen demand over volatile suspended solids (1.86 to 2.40 g chemical oxygen demand/g volatile suspended solids). These results indicated that the physical, chemical, and biological characteristics of liquid and solids from different filter configurations are required to determine design criteria to upgrade or retrofit water resource recovery facilities using an RBF or a DF.

Practitioner Points

A rotating belt filter (RBF) removed less solids than a drum filter (DF) or a primary clarifier (PC).

A series configuration of an RBF with either a DF or PC resulted in an effluent with a lower proportion of slowly biodegradable organic matter than in a parallel configuration.

Solids from an RBF, a DF, or a PC had similar theoretical biochemical methane potential.
Introduction: Maritime Im/Mobilities
Authors: Ganser Alexandra , Lavery Charne
Published: March 2023

In a time of refugee boats and rising seas, maritime mobilities and immobilities have regained critical status for the development of viable futures—social, economic, environmental, political, as well as imaginative. This introduction describes advances in maritime studies, alongside “blue cultural studies” or “oceanic humanities,” and covers a selection of recent critical perspectives in the maritime humanities that have extended, but also criticized, earlier assumptions in the field: Eurocentric, anthropocentric, and class-, race-, and gender-based. The focus is on some of the ways in which maritime studies has been informed by mobility studies, an interdisciplinary field within the humanities. Taking up cultural geography’s revision of the field to acknowledge the centrality of movement in history and society and to move beyond logistics-centered transportation studies, mobility studies investigates the production of mobilities and immobilities historically, socially, politically, and culturally. The present volume sets out to explore how oceanic im/mobilities have been framed and articulated in the literary and cultural imagination.
“Ocean People”: Maritime (Im)Mobilities in the Chinese American Imaginary
Authors: Poppenhagen Nicole
Published: March 2023

Understanding the ocean in both metaphorical and material terms, this essay approaches Chinese American texts from the intersection of oceanic, mobility, and Chinese American studies. While mobility has often been perceived in highly romanticized terms, in Chinese American literature the ocean is characterized by ambiguity as it becomes a site of negotiation for the role of immigrants during the era of Chinese exclusion. In Maxine Hong Kingston’s auto/biographical textChina Men, Pam Chun’s family narrativeThe Money Dragon, poetry from Ellis Island and Angel Island, and Genny Lim’s playPaper Angels, the complex relationships between time and space, sea and land, inform the immigrants’ journey across the ocean. Their American beginnings are defined by a liberating, though often disorienting, mobility and at the same time a longed for, but mostly paralyzing immobility on and off ships and islands. To use Maxine Hong Kingston words, these circumstances of migration turn Chinese Americans into “ocean people.” Ultimately, a focus on the ship voyage and the island as oceanic tropes in Chinese American texts reveals the Chinese American experience as shaped by maritime (im)mobilities.
Storied Waves: Maritime Connections and Subaltern Knowledge in Arctic and Mediterranean Literary Contact Zones
Authors: Mackenthun Gesa
Published: March 2023

Woven around Mary Shelley’s Frankenstein, Olaudah Equiano’s Interesting Narrative, Peter Høeg’s Miss Smilla’s Feeling for Snow, and Merle Kröger’s Havarie (Collision), this chapter discusses the concurrence between the search for knowledge and the topic of human disposability in texts and events relating to the contact zones of the Arctic and the Mediterranean. It views them as combining macrogeographical considerations of oceanic contact with reflections on the micro-level of human experience, resonating with Edward Said’s and Kirsten Gruesz’s pleas for investigating “overlapping geographies,” “intertwined histories,” and maritime geopolitical regions as “engines” of empire. Straddling the period between the beginning of globalization and the recent past, these literary texts are harnessed to critical reflections on the complexity of these maritime, social, and epistemic connections and contact zones beyond the landed history of nations and mainlands.
Birds of the Plastic Pacific: Moving (the) Masses
Authors: Crane Kylie
Published: March 2023

The “Great Pacific Garbage Patch”is a misnomer. It suggests an easily visualizable mass of plastic, clearly defined and floating atop the Pacific Ocean. The reality is that the masses of plastic move not singularly as a mass, nor are they necessarily clearly visible. The plastic moves, certainly; it shifts, floats, and sinks. It entangles and mangles. And it degrades, exuding lethal toxins into and through the oceans. The “Plastic Pacific” is more a shifting accretion of material than a static accumulation of objects.

In lieu of a “patch,” then, what images and symbols can be mobilized to “move the masses” to respond to this phenomenon? Images of two kinds of maritime birds have emerged: the Layson albatross, lying prostate with plastic (Chris Jordan’s “Midway Islands” series, Susan Middleton and David Liittschwager’s Archipelago volume), and the rubber duck, rendered both homely and unheimlich in several books (Slow Death by Rubber Duck by Rick Smith and Bruce Lourie, Moby-Duck by Donovan Hohn).

Drawing on theories of entanglement, archipelagoes, and toxicity, this article stresses the relational as a crucial component of political and ethical critique. The “moving” of the subtitle thus not only references the mobility of the material itself, but also the different aesthetic measures employed to effect responses and responsibilities.
Maritime Mobility and the Work of Susanna Rowson: Transatlantic Perspectives
Authors: Lippert Leopold
Published: March 2023

The chapter explores how the work of Susanna Haswell Rowson (1762–1824) employs ships and sea travel as sites of the cultural articulation and negotiation of mobility in the early modern Atlantic world. Through her writings on seafaring, the chapter suggests, Rowson examines the tensions between imperial fantasies of seamless connectivity across the Atlantic world and the typically flawed material and infrastructural conditions that enabled/foreclosed that connectivity. Critically reading the playSlaves in Algiers(1794), the didactic treatise “Rise and Progress of Navigation” (1811), as well as the novelsReuben and Rachel(1798) andRebecca(1814 edition), the chapter analyzes the ways in which Rowson functionalized maritime mobility in order to deliberate the materiality of transatlantic travel and the ways such travel impacted on the cultural imaginaries of correspondence, circulation, and exchange. Moreover, Rowson used ships and seafaring as narrative devices to relate early U.S. national identities (such as her own) to larger transatlantic contexts. Ultimately, the chapter shows that Rowson’s writing establishes the Atlantic world as a contested and contradictory cultural space that could not always reconcile imperial fantasies of connectivity with the imperfect material conditions of late eighteenth- and early nineteenth-century maritime mobility.
Reading and Writing the Ship in “Benito Cereno” and “The Heroic Slave”
Authors: Massnick Thomas
Published: March 2023

Herman Melville’s “Benito Cereno” and Frederick Douglass’s “The Heroic Slave” both explore the ways in which power is inscribed into maritime technologies. A focus on the role of ships within these narratives reveals the way systems of power operate, and invites inquiry into the way those systems can be disrupted. The hapless qualities of Melville’s central figure, Amasa Delano, and the hypermasculine heroism of Douglass’s protagonist, Madison Washington, have received much scholarly attention. These aspects of the characters come into sharper relief when we consider the ship itself to be a legible, authoritative text. Delano’s notoriously poor reading of his situation stands in contrast to his accurate and detailed reading of the ship in its disarray. Relatedly, Washington’s nautical literacy parallels Douglass’s own well-known history with the printed word. The two stories present opposing looks at the nature of power as inscribed in the ship. For Douglass, power is there for the taking, systems can be overturned, and their technologies subverted. Melville’s text offers a dimmer view; his story and its concluding legal documentation suggests that systems of power are supported by a self-sustaining and self-justifying logic. These two visions imply different prospects for Black agency.
From HI-SEAS to Outer Space: Discourses of Water and Territory in U.S. Pacific Imperialism and Representations of U.S. Mars Colonization
Authors: Temmen Jens
Published: March 2023

This chapter analyzes how imperial discourses of water and territory serve to claim such seemingly disparate spaces as the Hawaiian Islands and the planet Mars for U.S. empire. Through a comparative analysis of the so-called HI-SEAS Mars simulation project located on the slopes of Mauna Loa mountain (Hawai‘i) as well as of recent representations of Mars habitation and colonization, such as Andy Weir’sThe Martian, it argues that current conceptualizations of Mars in the U.S. context tend to rely on discourses of territorial management groomed in nineteenth-century U.S. Pacific imperialism.
Resilience, Food Security and Food Systems: Setting the Scene
Authors: Béné Christophe , Devereux Stephen
Published: March 2023

The aim of this introduction chapter is twofold. First it will set the scene, frame the overarching problem and present the central question of this volume: How does the concept of resilience help in improving our general understanding of the development process, in particular around the issue of food (in)security, and how does it influence the way development interventions around this question of food security are now programmed and implemented? To address this ambitious question, the entire series of chapters will adopt a food system approach. The second part of the introduction chapter will then ‘kick-start’ the discussion, first by providing some initial element of definition for the three concepts under consideration and then by highlighting some of the main discussions, debates or even contradictions that emerge in the literature around the definition, interpretations and application of those concepts.
Urban Food Security and Resilience
Authors: Haysom Gareth , Battersby Jane
Published: March 2023

The concept of resilience within urban food systems has gained significant academic and policy focus in recent years. This aligns with the increased global awareness of the problem of urban food insecurity, and increased focus on sub-national policies for sustainable development. COVID-19 demonstrated a series of vulnerabilities in the food system and the urban system. Academic work on urban food system resilience is wide ranging, however particular areas of focus dominate, focusing on urban agriculture, localized food systems, resilient city region food systems and the water-energy-food nexus. Renewed interest in resilience policy at the local government level has been amplified by global networks, whose framing of urban food systems resilience is embedded within the SDGs and the New Urban Agenda. Using findings from cities in five African countries, we argue for a re-framing of urban food system resilience that is inclusive of a wider set of factors shaping the form and function of the food system; that the urban system, specifically infrastructure, shapes the functioning of the food system and the ability of consumers to use the food system; and that the agency of urban food system users needs inclusion in understandings of, and efforts to increase, food systems resilience.
Reflections and Conclusions
Authors: Devereux Stephen , Béné Christophe
Published: March 2023

The concluding chapter reflects on the conceptual, empirical, and policy-related contributions that this book has made and identifies remaining unresolved issues. ‘Resilience’, ‘food system’, and even ‘food security’ were seen to be elusive and contested concepts. A food systems’ lens is more holistic than conventional preoccupations with agriculture and food prices, by highlighting for instance the relatively neglected role of food processors in food security analysis. Global and national food systems are increasingly resilient, even against shocks like COVID-19 lockdowns, but climate change raises fundamental challenges. Moreover, paradoxes remain, such as the coexistence of high levels of undernutrition and overnutrition in countries where food systems appear to be performing efficiently. Governments, international agencies, civil society, the private sector, and local communities all have important complementary roles to play. But there are difficult policy trade-offs to negotiate: whether to promote higher crop yields or lower crop volatility, for instance, or whether to intervene in food systems by taxing unhealthy foods (e.g. sugar), which could benefit consumers but would harm affected farmers. Ultimately, at global, national, and local levels, well-functioning, sustainable and equitable food systems need to deliver not only enough food, but affordable access for all to sustainable healthy diets.
Achieving Food Security Through a Food Systems Lens
Authors: Fanzo Jessica
Published: March 2023

Achieving food security for the global population of 8 billion will be a challenge without functional, equitable, and resilient food systems. This chapter examines the history of how food security has been framed and addressed in international development, and the importance of a food systems approach and mindset in tackling food security. While this new food systems framing is important in bringing together the myriad of actors and components that food touches upon, food security has become more complex in the modern, challenged world, and functional food systems do not necessarily equate to improved food security. As international goals and commitments are made, policymakers must consider how food systems engage with other systems, and the failures and successes that history has taught us in efforts to achieve food security for all.
The Global Food System is Not Broken but Its Resilience is Threatened
Authors: Caron Patrick , Daguet Ellie , Dury Sandrine
Published: March 2023

The global food system is not broken. Apart from specific contexts, access to food has never been so easy as it is today. The global food system has been resilient and able to adapt during the twentieth century to many shocks and stressors such as an unprecedented population growth. The huge increase in production has exceeded the demographic growth. Together with the expansion of trade, they have been key in ensuring food system resilience of most countries, including those with limited resources. Yet, a profound transformation is needed for the following reasons: (i) food is today the major problem in public health, (ii) the economics of food chains fuel inequalities, and (iii) food systems are responsible for major environmental and climatic damage. Numerous calls for engaging in such a transformation highlight the need for a paradigm shift. However, despite such shared observations and alerts, no “great transformation” is taking place, for different reasons, including conflicts of interest. Resilience of food systems in the twenty-first century can thus be considered a property that is constrained by past transformation. Resilience depends on the ability to change the very drivers that made food systems resilient in the twentieth century.
Food Security and Resilience: The Potential for Coherence and the Reality of Fragmented Applications in Policy and Research
Authors: Constas Mark A.
Published: March 2023

While the topic of food security has long been a focal point for research and policy concerned with development, resilience represents a newer area of work. Given the complexity of each topic on its own, the task of integrating the inherently multidimensional concepts of resilience and food security is an ambitious undertaking. Against this background, the present chapter was motivated by two questions: How can the integration of food security and resilience be conceptualized? What trends can be observed when food security and resilience are linked? To answer the first question, the chapter offers a new conceptual model that articulates possibilities for connecting food security and resilience. In response to the second question, a case study of selected policy documents and a sample of research was conducted. From a methodological perspective, the case study combined lexical analysis methods with a scoping review protocol. The findings from the policy review and from the research literature revealed that attempts to integrate food security and resilience were limited, inconsistent, and largely superficial. The conceptual model and the findings from the case study highlight the need for more coherent integration of work situated at the intersection of food security and resilience.
Uncovering Change: A Streaming Approach for Declarative Processes
Authors: Burattin Andrea , López Hugo A. , Starklit Lasse
Published: March 2023

Process discovery is a family of techniques that helps to comprehend processes from their data footprints. Yet, as processes change over time so should their corresponding models, and failure to do so will lead to models that under- or over-approximate behaviour. We present a discovery algorithm that extracts declarative processes as Dynamic Condition Response (DCR) graphs from event streams. Streams are monitored to generate temporal representations of the process, later processed to create declarative models. We validated the technique by identifying drifts in a publicly available dataset of event streams. The metrics extend the Jaccard similarity measure to account for process change in a declarative setting. The technique and the data used for testing are available online.
Deep Reinforcement Learning for Resource Allocation in Business Processes
Authors: Żbikowski Kamil , Ostapowicz Michał , Gawrysiak Piotr
Published: March 2023

Assigning resources in business processes execution is a repetitive task that can be effectively automated. However, different automation methods may give varying results that may not be optimal. Proper resource allocation is crucial as it may lead to significant cost reductions or increased effectiveness that results in increased revenues.

In this work, we first propose a novel representation that allows the modeling of a multi-process environment with different process-based rewards. These processes can share resources that differ in their eligibility. Then, we use double deep reinforcement learning to look for an optimal resource allocation policy. We compare those results with two popular strategies that are widely used in the industry. Learning optimal policy through reinforcement learning requires frequent interactions with the environment, so we also designed and developed a simulation engine that can mimic real-world processes.

The results obtained are promising. Deep reinforcement learning based resource allocation achieved significantly better results compared to two commonly used techniques.
LSTM-Based Anomaly Detection of Process Instances: Benchmark and Tweaks
Authors: Lahann Johannes , Pfeiffer Peter , Fettke Peter
Published: March 2023

Anomaly detection can identify deviations in event logs and allows businesses to infer inconsistencies, bottlenecks, and optimization opportunities in their business processes. In recent years, various anomaly detection algorithms for business processes have been proposed based on either process discovery or machine learning algorithms. While there are apparent differences between machine learning and process discovery approaches, it is often unclear how they perform in comparison. Furthermore, deep learning research in other domains has shown that advancements did not solely come from improved model architecture but were often due to minor pre-processing and training procedure refinements. For this reason, this paper aims to set up a broad benchmark and establish a baseline for deep learning-based anomaly detection of process instances. To this end, we introduce a simple LSTM-based anomaly detector utilizing a collection of minor refinements and compare it with existing approaches. The results suggest that the proposed method can significantly outperform the existing approaches on a large number of event logs consistently.
Genetic Algorithms for AutoML in Process Predictive Monitoring
Authors: Kwon Nahyun , Comuzzi Marco
Published: March 2023

In recent years, AutoML has emerged as a promising technique for reducing computational and time cost by automating the development of machine learning models. Existing AutoML tools cannot be applied directly to process predictive monitoring (PPM), because they do not support several configuration parameters that are PPM-specific, such as trace bucketing or encoding. In other words, they are only specialized in finding the best configuration of machine learning model hyperparameters. In this paper, we present a simple yet extensible framework for AutoML in PPM. The framework uses genetic algorithms to explore a configuration space containing both PPM-specific parameters and the traditional machine learning model hyperparameters. We design four different types of experiments to verify the effectiveness of the proposed approach, comparing its performance in respect of random search of the configuration space, using two publicly available event logs. The results demonstrate that the proposed approach outperforms consistently the random search.
Outcome-Oriented Predictive Process Monitoring on Positive and Unlabelled Event Logs
Authors: Peeperkorn Jari , Ortega Vázquez Carlos , Stevens Alexander , De Smedt Johannes , vanden Broucke Seppe , De Weerdt Jochen
Published: March 2023

A lot of recent literature on outcome-oriented predictive process monitoring focuses on using models from machine and deep learning. In this literature, it is assumed the outcome labels of the historical cases are all known. However, in some cases, the labelling of cases is incomplete or inaccurate. For instance, you might only observe negative customer feedback, fraudulent cases might remain unnoticed. These cases are typically present in the so-called positive and unlabelled (PU) setting, where your data set consists of a couple of positively labelled examples and examples which do not have a positive label, but might still be examples of a positive outcome. In this work, we show, using a selection of event logs from the literature, the negative impact of mislabelling cases as negative, more specifically when using XGBoost and LSTM neural networks. Furthermore, we show promising results on real-life datasets mitigating this effect, by changing the loss function used by a set of models during training to those of unbiased Positive-Unlabelled (uPU) or non-negative Positive-Unlabelled (nnPU) learning.
Enhancing Data-Awareness of Object-Centric Event Logs
Authors: Goossens Alexandre , De Smedt Johannes , Vanthienen Jan , van der Aalst Wil M. P.
Published: March 2023

When multiple objects are involved in a process, there is an opportunity for processes to be discovered from different angles with new information that previously might not have been analyzed from a single object point of view. This does require that all the information of event/object attributes and their values are stored within logs including attributes that have a list of values or attributes with values that change over time. It also requires that attributes can unambiguously be linked to an object, an event or both. As such, object-centric event logs are an interesting development in process mining as they support the presence of multiple types of objects. First, this paper shows that the current object-centric event log formats do not support the aforementioned aspects to their full potential since the possibility to support dynamic object attributes (attributes with changing values) is not supported by existing formats. Next, this paper introduces a novel enriched object-centric event log format tackling the aforementioned issues alongside an algorithm that automatically translates XES logs to this Data-aware OCEL (DOCEL) format.
Clustering Analysis and Frequent Pattern Mining for Process Profile Analysis: An Exploratory Study for Object-Centric Event Logs
Authors: Faria Junior Elio Ribeiro , Neubauer Thais Rodrigues , Fantinato Marcelo , Peres Sarajane Marques
Published: March 2023

Object-centric event log is a format for properly organizing information from different views of a business process into an event log. The novelty in such a format is the association of events with objects, which allows different notions of cases to be analyzed. The addition of new features has brought an increase in complexity. Clustering analysis can ease this complexity by enabling the analysis to be guided by process behaviour profiles. However, identifying which features describe the singularity of each profile is a challenge. In this paper, we present an exploratory study in which we mine frequent patterns on top of clustering analysis as a mechanism for profile characterization. In our study, clustering analysis is applied in a trace clustering fashion over a vector representation for a flattened event log extracted from an object-centric event log, using a unique case notion. Then, frequent patterns are discovered in the event sublogs associated with clusters and organized according to that original object-centric event log. The results obtained in preliminary experiments show association rules reveal more evident behaviours in certain profiles. Despite the process underlying each cluster may contain the same elements (activities and transitions), the behaviour trends show the relationships between such elements are supposed to be different. The observations depicted in our analysis make room to search for subtler knowledge about the business process under scrutiny.
Process Modeling and Conformance Checking in Healthcare: A COVID-19 Case Study
Authors: Benevento Elisabetta , Pegoraro Marco , Antoniazzi Mattia , Beyel Harry H. , Peeva Viki , Balfanz Paul , van der Aalst Wil M. P. , Martin Lukas , Marx Gernot
Published: March 2023

The discipline of process mining has a solid track record of successful applications to the healthcare domain. Within such research space, we conducted a case study related to the Intensive Care Unit (ICU) ward of the Uniklinik Aachen hospital in Germany. The aim of this work is twofold: developing a normative model representing the clinical guidelines for the treatment of COVID-19 patients, and analyzing the adherence of the observed behavior (recorded in the information system of the hospital) to such guidelines. We show that, through conformance checking techniques, it is possible to analyze the care process for COVID-19 patients, highlighting the main deviations from the clinical guidelines. The results provide physicians with useful indications for improving the process and ensuring service quality and patient satisfaction. We share the resulting model as an open-source BPMN file.
Research Paper: Process Mining and Synthetic Health Data: Reflections and Lessons Learnt
Authors: Bullward Alistair , Aljebreen Abdulaziz , Coles Alexander , McInerney Ciarán , Johnson Owen
Published: March 2023

Analysing the treatment pathways in real-world health data can provide valuable insight for clinicians and decision-makers. However, the procedures for acquiring real-world data for research can be restrictive, time-consuming and risks disclosing identifiable information. Synthetic data might enable representative analysis without direct access to sensitive data. In the first part of our paper, we propose an approach for grading synthetic data for process analysis based on its fidelity to relationships found in real-world data. In the second part, we apply our grading approach by assessing cancer patient pathways in a synthetic healthcare dataset (The Simulacrum provided by the English National Cancer Registration and Analysis Service) using process mining. Visualisations of the patient pathways within the synthetic data appear plausible, showing relationships between events confirmed in the underlying non-synthetic data. Data quality issues are also present within the synthetic data which reflect real-world problems and artefacts from the synthetic dataset’s creation. Process mining of synthetic data in healthcare is an emerging field with novel challenges. We conclude that researchers should be aware of the risks when extrapolating results produced from research on synthetic data to real-world scenarios and assess findings with analysts who are able to view the underlying data.
Predicting Patient Care Acuity: An LSTM Approach for Days-to-day Prediction
Authors: Bekelaar Jorg W. R. , Luime Jolanda J. , de Carvalho Renata M.
Published: March 2023

In recent years, hospitals and other care providers in the Netherlands are coping with a widespread nursing shortage and a directly related increase in nursing workload. This nursing shortage combined with the high nursing workload is associated with higher levels of burnout and reduced job satisfaction among nurses. However, not only the nurses, but also the patients are affected as an increasing nursing workload adversely affects patient safety and satisfaction. Therefore, the aim of this research is to predict the care acuity corresponding to an individual patient for the next admission day, by using the available structured hospital data of the previous admission days. For this purpose, we make use of an LSTM model that is able to predict the care acuity of the next day, based on the hospital data of all previous days of an admission. In this paper, we elaborate on the architecture of the LSTM model and we show that the prediction accuracy of the LSTM model increases with the increase of the available amount of historical event data. We also show that the model is able to identify care acuity differences in terms of the amount of support needed by the patient. Moreover, we discuss how the predictions can be used to identify which patient care related characteristics and different types of nursing activities potentially contribute to the care acuity of a patient.
Measuring the Impact of COVID-19 on Hospital Care Pathways
Authors: Puthur Christin , Aljebreen Abdulaziz , McInerney Ciarán , Mebrahtu Teumzghi , Lawton Tom , Johnson Owen
Published: March 2023

Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted.
Defining Data Quality Issues in Process Mining with IoT Data
Authors: Bertrand Yannis , Van Belle Rafaël , De Weerdt Jochen , Serral Estefanía
Published: March 2023

IoT devices supporting business processes (BPs) in sectors like manufacturing, logistics or healthcare collect data on the execution of the processes. In the last years, there has been a growing awareness of the opportunity to use the data these devices generate for process mining (PM) by deriving an event log from a sensor log via event abstraction techniques. However, IoT data are often affected by data quality issues (e.g., noise, outliers) which, if not addressed at the preprocessing stage, will be amplified by event abstraction and result in quality issues in the event log (e.g., incorrect events), greatly hampering PM results. In this paper, we review the literature on PM with IoT data to find the most frequent data quality issues mentioned in the literature. Based on this, we then derive six patterns of poor sensor data quality that cause event log quality issues and propose solutions to avoid or solve them.
Creating Translucent Event Logs to Improve Process Discovery
Authors: Beyel Harry H. , van der Aalst Wil M. P.
Published: March 2023

Event logs capture information about executed activities. However, they do not capture information about activities that could have been performed, i.e., activities that were enabled during a process. Event logs containing information on enabled activities are called translucent event logs. Although it is possible to extract translucent event logs from a running information system, such logs are rarely stored. To increase the availability of translucent event logs, we propose two techniques. The first technique records the system’s states as snapshots. These snapshots are stored and linked to events. A user labels patterns that describe parts of the system’s state. By matching patterns with snapshots, we can add information about enabled activities. We apply our technique in a small setting to demonstrate its applicability. The second technique uses a process model to add information concerning enabled activities to an existing traditional event log. Data containing enabled activities are valuable for process discovery. Using the information on enabled activities, we can discover more correct models.
Aggregating Event Knowledge Graphs for Task Analysis
Authors: Klijn Eva L. , Mannhardt Felix , Fahland Dirk
Published: March 2023

Aggregation of event data is a key operation in process mining for revealing behavioral features of processes for analysis. It has primarily been studied over sequences of events in event logs. The data model of event knowledge graphs enables new analysis questions requiring new forms of aggregation. We focus on analyzing task executions in event knowledge graphs. We show that existing aggregation operations are inadequate and propose new aggregation operations, formulated as query operators over labeled property graphs. We show on the BPIC’17 dataset that the new aggregation operations allow gaining new insights into differences in task executions, actor behavior, and work division.
A Combined Approach of Process Mining and Rule-Based AI for Study Planning and Monitoring in Higher Education
Authors: Wagner Miriam , Helal Hayyan , Roepke Rene , Judel Sven , Doveren Jens , Goerzen Sergej , Soudmand Pouya , Lakemeyer Gerhard , Schroeder Ulrik , van der Aalst Wil M. P.
Published: March 2023

This paper presents an approach of using methods of process mining and rule-based artificial intelligence to analyze and understand study paths of students based on campus management system data and study program models. Process mining techniques are used to characterize successful study paths, as well as to detect and visualize deviations from expected plans. These insights are combined with recommendations and requirements of the corresponding study programs extracted from examination regulations. Here, event calculus and answer set programming are used to provide models of the study programs which support planning and conformance checking while providing feedback on possible study plan violations. In its combination, process mining and rule-based artificial intelligence are used to support study planning and monitoring by deriving rules and recommendations for guiding students to more suitable study paths with higher success rates. Two applications will be implemented, one for students and one for study program designers.
Domain-Driven Event Abstraction Framework for Learning Dynamics in MOOCs Sessions
Authors: Hidalgo Luciano , Munoz-Gama Jorge
Published: March 2023

In conjunction with the rapid expansion of Massive Open Online Courses (MOOCs), academic interest has grown in the analysis of MOOC student study sessions. Education researchers have increasingly regarded process mining as a promising tool with which to answer simple questions, including the order in which resources are completed. However, its application to more complex questions about learning dynamics remains a challenge. For example, do MOOC students genuinely study from a resource or merely skim content to understand what will come next? One common practice is to use the resources directly as activities, resulting in spaghetti process models that subsequently undergo filtering. However, this leads to over-simplified and difficult-to-interpret conclusions. Consequently, an event abstraction becomes necessary, whereby low-level events are combined with high-level activities. A wide range of event abstraction techniques has been presented in process mining literature, primarily in relation to data-driven bottom-up strategies, where patterns are discovered from the data and later mapped to education concepts. Accordingly, this paper proposes a domain-driven top-down framework that allows educators who are less familiar with data and process analytics to more easily search for a set of predefined high-level concepts from their own MOOC data. The framework outlined herein has been successfully tested in a Coursera MOOC, with the objective of understanding the in-session behavioral dynamics of learners who successfully complete their respective courses.