Computing and Information Systems - Research Publications

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    ProLift: Automated Discovery of Causal Treatment Rules From Event Logs (Extended Abstract)
    Bozorgi, ZD ; Kopõlov, A ; Dumas, M ; Rosa, ML ; Polyvyanyy, A ; Hassani, M ; Koschmider, A ; Comuzzi, M ; Maria Maggi, F ; Pufahl, L (CEUR, 2022-01-01)
    ProLift is a Web-based tool that uses causal machine learning, specifically uplift trees, to discover rules for optimizing business processes based on execution data (event logs). ProLift allows users to upload an event log, to specify case treatments and case outcomes, and to visualize treatment rules that increase the probability of positive case outcomes. The target audience of ProLift includes researchers and practitioners interested in leveraging causal machine learning for process improvement.
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    GRACE: A Simulator for Continuous Goal Recognition over Changing Environments
    Su, Z ; Polyvyanyy, A ; Lipovetzky, N ; Sardina, S ; van Beest, N ; De Giacomo, G ; Guzzo, A ; Montali, M ; Limonad, L ; Fournier, F ; Chakraborti, T (IJCAI, 2022)
    Goal Recognition (GR) is a research problem that studies ways to infer the goal of an intelligent agent based on its observed behavior and knowledge of the environment. A common assumption of GR is that the underlying environment is stationary. However, in many real-world scenarios, it is necessary to recognize agents’ goals over extended periods. Therefore, it is reasonable to assume that the environment will change throughout a series of goal recognition tasks. This paper introduces the problem of continuous GR over a changing environment. The solution to this problem is a GR system capable of recognizing agents’ goals over an extended period where the environment in which the agents operate changes. To support the evaluation of candidate solutions to this new GR problem, in this paper, we present the Goal Recognition Amidst Changing Environments (GRACE) tool for generating instances of the new problem. Specifically, the tool can be configured to generate GR problems that account for different environmental changes and drifts. GRACE can generate a series of modified environments over discrete time steps and the data induced by agents operating in the environment while completing different goals.
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    Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction
    Dasht Bozorgi, Z ; Teinemaa, I ; Dumas, M ; La Rosa, M ; Polyvyanyy, A ; DiCiccio, C ; DiFrancescomarino, C ; Soffer, P (IEEE, 2021)
    Reducing cycle time is a recurrent concern in the field of business process management. Depending on the process, various interventions may be triggered to reduce the cycle time of a case, for example, using a faster shipping service in an order-to-delivery process or calling a customer to obtain missing information rather than waiting passively. However, each of these interventions comes with a cost. This paper tackles the problem of determining if and when to trigger a time-reducing intervention in a way that maximizes a net gain function. The paper proposes a prescriptive monitoring method that uses orthogonal random forests to estimate the causal effect of triggering a time-reducing intervention for each ongoing case of a process. Based on this estimate, the method triggers interventions according to a user defined policy. The method is evaluated on two real-life datasets.
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    Process Model Forecasting Using Time Series Analysis of Event Sequence Data
    De Smedt, J ; Yeshchenko, A ; Polyvyanyy, A ; De Weerdt, J ; Mendling, J ; Ghose, A ; Horkoff, J ; Souza, VES ; Parsons, J ; Evermann, J (Springer, 2021)
    Process analytics is an umbrella of data-driven techniques which includes making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling next activity, remaining time, and outcome prediction. At the model level, there is a notable void. It is the ambition of this paper to fill this gap. To this end, we develop a technique to forecast the entire process model from historical event data. A forecasted model is a will-be process model representing a probable future state of the overall process. Such a forecast helps to investigate the consequences of drift and emerging bottlenecks. Our technique builds on a representation of event data as multiple time series, each capturing the evolution of a behavioural aspect of the process model, such that corresponding forecasting techniques can be applied. Our implementation demonstrates the accuracy of our technique on real-world event log data.
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    Structural and Behavioral Biases in Process Comparison Using Models and Logs
    Kalenkova, A ; Polyvyanyy, A ; La Rosa, M ; Ghose, A ; Horkoff, J ; Souza, VES ; Parsons, J ; Evermann, J (Springer, 2021)
    Process models automatically discovered from event logs represent business process behavior in a compact graphical way. To compare process variants, e.g., to explore how the system’s behavior changes over time or between customer segments, analysts tend to visually compare conceptual process models discovered from different “slices” of the event log, solely relying on the structure of these models. However, the structural distance between two process models does not always reflect the behavioral distance between the underlying event logs and thus structural comparison should be applied with care. This paper aims to investigate relations between structural and behavioral process distances and explain when structural distance between two discovered process models can be used to assess the behavioral distance between the corresponding event logs. Keywords: Process mining · Variant analysis · Structural distance · BPMN
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    All That Glitters Is Not Gold: Towards Process Discovery Techniques with Guarantees
    van der Werf, JM ; Polyvyanyy, A ; van Wensveen, B ; Brinkhuis, M ; Reijers, H ; LaRosa, M ; Sadiq, S ; Teniente, E (Springer, 2021-07-01)
    The aim of a process discovery algorithm is to construct from event data a process model that describes the underlying, real-world process well. intuitively, the better the quality of the input event data, the better the quality of the resulting discovered model should be. However, existing process discovery algorithms do not guarantee this relationship. We demonstrate this by using a range of quality measures for both event data and discovered process models. This paper is a call to the community of IS engineers to complement their process discovery algorithms with properties that relate qualities of their inputs to those of their outputs. To this end, we distinguish four incremental stages for the development of such algorithms, along with concrete guidelines for the formulation of relevant properties and experimental validation. We use these stages to reflect on the state of the art, which shows the need to move forward in our thinking about algorithmic process discovery.
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    Microservice Remodularisation of Monolithic Enterprise Systems for Embedding in Industrial IoT Networks
    Adambarage Anuruddha Chathuranga, DA ; Barros, A ; Fidge, C ; Polyvyanyy, A ; LaRosa, M ; Sadiq, S ; Teniente, E (Springer, 2021)
    This paper addresses the challenge of decoupling "back-office" enterprise system functions in order to integrate them with the Industrial Internet-of-Things (IIoT). IIoT is a widely anticipated strategy, combining IoT technologies managing physical object movements, interactions and contexts, with business contexts. However, enterprise systems, supporting these contexts, are notoriously large and monolithic, and coordinate centralised business processes through software components dedicated to managing business objects (BOs). Such objects and their associated operations are difficult to manually decouple because of the asynchronous and user-driven nature of the business processes and complex BO dependencies, such as many-to-many and aggregation relationships. Here we present a software remodularisation technique for enterprise systems, to support the discovery of fine-grained microservices, which can be extracted and embedded to run on IIoT network nodes. It combines the semantic knowledge of enterprise systems, i.e., the BO structure, with syntactic knowledge of the code, i.e., various dependencies at the level of classes and methods. Using extracted feature sets based on both semantic and syntactic dependencies, K-Means clustering and optimisation is then used to recommend microservices, i.e., redistributions of BO operations through microservices from BO-centric components of enterprise systems. The approach is validated using the Dolibarr open source ERP system, in which we identify processes comprising both "edge" operations and request-response calls to the Cloud-based enterprise system. Through experimentation using Amazon GreenGrass deployments, simulating IIoT nodes, we show that the recommended microservices demonstrate key non-functional characteristics, of high execution efficiency, scalability and availability.
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    Remodularization Analysis for Microservice Discovery Using Syntactic and Semantic Clustering
    De Alwis, AAC ; Barros, A ; Fidge, C ; Polyvyanyy, A ; Dustdar, S ; Yu, E ; Salinesi, C ; Rieu, D ; Pant, V (Springer, 2020-06-03)
    This paper addresses the challenge of automated remodularization of large systems as microservices. It focuses on the analysis of enterprise systems, which are widely used in corporate sectors and are notoriously large, monolithic and challenging to manually decouple because they manage asynchronous, user-driven business processes and business objects (BOs) having complex structural relationships. The technique presented leverages semantic knowledge of enterprise systems, i.e., BO structure, together with syntactic knowledge of the code, i.e., classes and interactions as part of static profiling and clustering. On a semantic level, BOs derived from databases form the basis for prospective clustering of classes as modules, while on a syntactic level, structural and interaction details of classes provide further insights for module dependencies and clustering, based on K-Means clustering and optimization. Our integrated techniques are validated using two open source enterprise customer relationship management systems, SugarCRM and ChurchCRM. The results demonstrate improved feasibility of remodularizing enterprise systems (inclusive of coded BOs and classes) as microservices. Furthermore, the recommended microservices, integrated with ‘backend’ enterprise systems, demonstrate improvements in key non-functional characteristics, namely high execution efficiency, scalability and availability.
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    Stochastic-Aware Conformance Checking: An Entropy-Based Approach
    Leemans, SJJ ; Polyvyanyy, A ; Dustdar, S ; Yu, E ; Salinesi, C ; Rieu, D ; Pant, V (Springer, 2020-06-03)
    Business process management (BPM) aims to support changes and innovations in organizations’ processes. Process mining complements BPM with methods, techniques, and tools that provide insights based on observed executions of business processes recorded in event logs of information systems. State-of-the-art discovery and conformance techniques completely ignore or only implicitly consider the information about the likelihood of processes, which is readily available in event logs, even though such stochastic information is necessary for simulation, prediction and recommendation in models. Furthermore, stochastic information can provide business analysts with further actionable insights on frequent and rare conformance issues. In this paper, we propose precision and recall conformance measures based on the notion of entropy of stochastic automata that are capable of quantifying, and thus differentiating, frequent and rare deviations between an event log and a process model. The feasibility of using the proposed precision and recall measures in industrial settings is demonstrated by an evaluation over several real-world datasets supported by our open-source implementation.
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    Supporting process model validation through natural language generation
    Leopold, H ; Mendling, J ; Polyvyanyy, A (Gesellschaft fur Informatik (GI), 2016-01-01)