Computing and Information Systems - Research Publications
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ItemNo Preview AvailableOverview of ChEMU 2022 Evaluation Campaign: Information Extraction in Chemical Patents(SPRINGER INTERNATIONAL PUBLISHING AG, 2022)
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ItemNo Preview AvailableHand Hygiene Quality Assessment Using Image-to-Image Translation(SPRINGER INTERNATIONAL PUBLISHING AG, 2022)Hand hygiene can reduce the transmission of pathogens and prevent healthcare-associated infections. Ultraviolet (UV) test is an effective tool for evaluating and visualizing hand hygiene quality during medical training. However, due to various hand shapes, sizes, and positions, systematic documentation of the UV test results to summarize frequently untreated areas and validate hand hygiene technique effectiveness is challenging. Previous studies often summarize errors within predefined hand regions, but this only provides low-resolution estimations of hand hygiene quality. Alternatively, previous studies manually translate errors to hand templates, but this lacks standardized observational practices. In this paper, we propose a novel automatic image-to-image translation framework to evaluate hand hygiene quality and document the results in a standardized manner. The framework consists of two models, including an Attention U-Net model to segment hands from the background and simultaneously classify skin surfaces covered with hand disinfectants, and a U-Net-based generator to translate the segmented hands to hand templates. Moreover, due to the lack of publicly available datasets, we conducted a lab study to collect 1218 valid UV test images containing different skin coverage with hand disinfectants. The proposed framework was then evaluated on the collected dataset through five-fold cross-validation. Experimental results show that the proposed framework can accurately assess hand hygiene quality and document UV test results in a standardized manner. The benefit of our work is that it enables systematic documentation of hand hygiene practices, which in turn enables clearer communication and comparisons.
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ItemLightweight Nontermination Inference with CHCs(SPRINGER INTERNATIONAL PUBLISHING AG, 2021)Non-termination is an unwanted program property (considered a bug) for some software systems, and a safety property for other systems. In either case, automated discovery of preconditions for non-termination is of interest. We introduce NtHorn, a fast lightweight non-termination analyser, able to deduce non-trivial sufficient conditions for non-termination. Using Constrained Horn Clauses (CHCs) as a vehicle, we show how established techniques for CHC program transformation and abstract interpretation can be exploited for the purpose of non-termination analysis. NtHorn is comparable in power to the state-of-the-art non-termination analysis tools, as measured on standard competition benchmark suites (consisting of integer manipulating programs), while typically solving problems an order of magnitude faster.
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ItemDisjunctive Interval Analysis(SPRINGER INTERNATIONAL PUBLISHING AG, 2021)We revisit disjunctive interval analysis based on the Boxes abstract domain. We propose the use of what we call range decision diagrams (RDDs) to implement Boxes, and we provide algorithms for the necessary RDD operations. RDDs tend to be more compact than the linear decision diagrams (LDDs) that have traditionally been used for Boxes. Representing information more directly, RDDs also allow for the implementation of more accurate abstract operations. This comes at no cost in terms of analysis efficiency, whether LDDs utilise dynamic variable ordering or not. RDD and LDD implementations are available in the Crab analyzer, and our experiments confirm that RDDs are well suited for disjunctive interval analysis.
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ItemAdvanced Process Discovery Techniques(Springer Cham, 2022-01-01)Given the challenges associated to the process discovery task, more than a hundred research studies addressed the problem over the past two decades. Despite the richness of proposals, many state-of-the-art automated process discovery techniques, especially the oldest ones, struggle to systematically discover accurate and simple process models. In general, when the behavior recorded in the input event log is simple (e.g., exhibiting little parallelism, repetitions, or inclusive choices) or noise free, some basic algorithms such as the alpha miner can output accurate and simple process models. However, as the complexity of the input data increases, the quality of the discovered process models can worsen quickly. Given that oftentimes real-life event logs record very complex and unstructured process behavior containing many repetitions, infrequent traces, and incomplete data, some state-of-the-art techniques turn unreliable and not purposeful. Specifically, they tend to discover process models that either have limited accuracy (i.e., low fitness and/or precision) or are syntactically incorrect. While currently there exists no perfect automated process discovery technique, some are better than others when discovering a process model from event logs recording complex process behavior. In this chapter, we introduce four of such techniques, discussing their underlying approach and algorithmic ideas, reporting their benefits and limitation, and comparing their performance with the algorithms introduced in the previous chapter.
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ItemRobotic Process Mining(Springer, Cham, 2022-06-27)User interaction logs allow us to analyze the execution of tasks in a business process at a finer level of granularity than event logs extracted from enterprise systems. The fine-grained nature of user interaction logs open up a number of use cases. For example, by analyzing such logs, we can identify best practices for executing a given task in a process, or we can elicit differences in performance between workers or between teams. Furthermore, user interaction logs allow us to discover repetitive and automatable routines that occur during the execution of one or more tasks in a process. Along this line, this chapter introduces a family of techniques, called Robotic Process Mining (RPM), which allow us to discover repetitive routines that can be automated using robotic process automation technology. The chapter presents a structured landscape of concepts and techniques for RPM, including techniques for user interaction log preprocessing, techniques for discovering frequent routines, notions of routine automatability, as well as techniques for synthesizing executable routine specifications for robotic process automation.
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ItemData and Process Resonance Identifier Soundness for Models of Information Systems(SPRINGER INTERNATIONAL PUBLISHING AG, 2022)A model of an information system describes its processes and how these processes manipulate data objects. Object-aware extensions of Petri nets focus on modeling the life-cycle of objects and their interactions. In this paper, we focus on Petri nets with identifiers, where identifiers are used to refer to objects. These objects should “behave” well in the system from inception to termination. We formalize this intuition in the notion of identifier soundness, and show that although this property is undecidable in general, useful subclasses exist that guarantee identifier soundness by construction.
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ItemBootstrapping Generalization of Process Models Discovered from Event Data(SPRINGER INTERNATIONAL PUBLISHING AG, 2022)Process mining extracts value from the traces recorded in the event logs of IT-systems, with process discovery the task of inferring a process model for a log emitted by some unknown system. Generalization is one of the quality criteria applied to process models to quantify how well the model describes future executions of the system. Generalization is also perhaps the least understood of those criteria, with that lack primarily a consequence of it measuring properties over the entire future behavior of the system when the only available sample of behavior is that provided by the log. In this paper, we apply a bootstrap approach from computational statistics, allowing us to define an estimator of the model’s generalization based on the log it was discovered from. We show that standard process mining assumptions lead to a consistent estimator that makes fewer errors as the quality of the log increases. Experiments confirm the ability of the approach to support industry-scale data-driven systems engineering.
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ItemProcess Querying: Methods, Techniques, and Applications(Springer International Publishing, 2022)Process querying studies concepts and methods from fields like Big data, process modeling and analysis, business process intelligence, and process analytics and applies them to retrieve and manipulate real-world and designed processes. This chapter reviews state-of-the-art methods for process querying, summarizes techniques used to implement process querying methods, discusses typical applications of process querying, and identifies research gaps and suggests directions for future research in process querying.
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ItemNo Preview AvailableProcess Query Language(Springer International Publishing, 2022)