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PublicationPersistent Identification for Conferences( 2022-04-05)
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PublicationOC π : Object-Centric Process Insights( 2022)Process mining uses event sequences recorded in information systems to discover and analyze the process models that generated them. Traditional process mining techniques make two assumptions that often do not find correspondence in real-life event data: First, each event sequence is assumed to be of the same type, i.e., all sequences describe an instantiation of the same process. Second, events are assumed to exclusively belong to one sequence, i.e., not being shared between different sequences. In reality, these assumptions often do not hold. Events may be shared between multiple event sequences identified by objects, and these objects may be of different types describing different subprocesses. Assuming “unshared” events and homogeneously typed objects leads to misleading insights and neglects the opportunity of discovering insights about the interplay between different objects and object types. Object-centric process mining is the term for techniques addressing this more general problem setting of deriving process insights for event data with multiple objects. In this paper, we introduce the tool OC π. OC π aims to make the process behind object-centric event data transparent to the user. It does so in two ways: First, we show frequent process executions, defined and visualized as a set of event sequences of different types that share events. The frequency is determined with respect to the activity attribute, i.e., these are object-centric variants. Second, we allow the user to filter infrequent executions and activities, discovering a mainstream process model in the form of an object-centric Petri net. Our tool is freely available for download (http://ocpi.ai/ ).
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PublicationAn Event Data Extraction Approach from SAP ERP for Process Mining( 2022)The extraction, transformation, and loading of event logs from information systems is the first and the most expensive step in process mining. In particular, extracting event logs from popular ERP systems such as SAP poses major challenges, given the size and the structure of the data. Open-source support for ETL is scarce, while commercial process mining vendors maintain connectors to ERP systems supporting ETL of a limited number of business processes in an ad-hoc manner. In this paper, we propose an approach to facilitate event data extraction from SAP ERP systems. In the proposed approach, we store event data in the format of object-centric event logs that efficiently describe executions of business processes supported by ERP systems. To evaluate the feasibility of the proposed approach, we have developed a tool implementing it and conducted case studies with a real-life SAP ERP system.
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PublicationComparing Micromobility with Public Transportation Trips in a Data-Driven Spatio-Temporal Analysis( 2022)Micromobility service systems have recently appeared in urban areas worldwide. Although e-bike and e-scooter services have been operating for some time now, their characteristics have only recently been analyzed in more detail. In particular, the influence on the existing transportation services is not well understood. This study proposes a framework to gather data, infer micromobility trips, deduce their characteristics, and assess their relation to a public transportation network. We validate our approach by comparing it to similar approaches in the literature and applying it to data of over a year from the city of Aachen. We find hints at the recreational role of e-scooters and a larger commuting role for e-bikes. We show that micromobility services in particular are used in situations where public transportation is not a viable alternative, hence often complementing the available services, and competing with public transportation in other areas. This ambivalent relationship between micromobility and public transportation emphasizes the need for appropriate regulations and policies to ensure the sustainability of micromobility services.
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PublicationOn Modeling Depths of Power Electronic Circuits for Real-Time Simulation( 2022)Investigations of the dynamic behaviour of power electronic components integrated into electric networks require suitable and established simulation methodologies. Real-time simulation represents a frequently applied methodology for analyzing the steady-state and transient behavior of electric power systems. This work introduces a guideline on how to model power electronics converters in digital real time simulators, taking into account the trade-off between model accuracy and the required computation time. Based on this concept, possible execution approaches with respect to the usage of central processing unit and field-programmable gate array components are highlighted. Simulation test scenario, such as primary frequency regulation and low voltage ride through, have been performed and accuracy indices are discussed for each implemented real-time model and each test scenario, respectively. Finally, a run-time analysis of presented real-time setups is given and real-time simulation results are compared. This manuscript demonstrates important differences in real-time simulation modelling, providing useful guidelines for the decision making of power engineers.
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PublicationImpact of Cyber-attacks on EV Charging Coordination: The Case of Single Point of Failure( 2022)Expanding adoption of electric vehicles (EVs) and broad deployment of charging stations push the limits of distribution grid infrastructure and increase the importance of effective charging coordination. Smart EV chargers with several functionalities and charging coordination solutions that can manage the charging sessions of hundreds of EVs are becoming common, with the increasing risk of triggering significant operational problems in case of cyberattacks. The information exchange between the charging coordinator, distribution network operator, and users is essential in the scheduling of a large number of charging sessions, relying on customer preferences, without violating operational grid constraints. Both the user mobile apps used for charging session reservations and DSO-charging coordinator interfaces are vulnerable to cyberattacks which may cause considerable technical and economic consequences. An important concern is the potential impacts of attacks when a single node or communication link is compromised. This study investigates the impacts of false data injection (FDI) and hijacking attacks on EV charging coordination in case of a single point of failure. Hijacking of one user's mobile app and FDI attack on the DSO-charging coordinator interface are investigated by simulating a 24-hour scenario with 12 chargers, 34 realistic charging sessions, and an EV charging coordination approach based on each session's tolerance to delays. The study highlighted considerable negative impacts that could be encountered in case of a single point of failure in EV charging coordination.
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PublicationMay I Take Your Order? On the Interplay Between Time and Order in Process Mining( 2022)Process mining starts from event data. The ordering of events is vital for the discovery of process models. However, the timestamps of events may be unreliable or imprecise. To further complicate matters, also causally unrelated events may be ordered in time. The fact that one event is followed by another does not imply that the former causes the latter. This paper explores the relationship between time and order. Moreover, it describes an approach to preprocess event data having timestamp-related problems. This approach avoids using accidental or unreliable orders and timestamps, creates partial orders to capture uncertainty, and allows for exploiting domain knowledge to (re)order events. Optionally, the approach also generates interleavings to be able to use existing process mining techniques that cannot handle partially ordered event data. The approach has been implemented using ProM and can be applied to any event log.
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PublicationA Framework for Extracting and Encoding Features from Object-Centric Event Data( 2022-01-01)
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PublicationMulti-Institutional Breast Cancer Detection Using a Secure On-Boarding Service for Distributed Analytics( 2022)The constant upward movement of data-driven medicine as a valuable option to enhance daily clinical practice has brought new challenges for data analysts to get access to valuable but sensitive data due to privacy considerations. One solution for most of these challenges are Distributed Analytics (DA) infrastructures, which are technologies fostering collaborations between healthcare institutions by establishing a privacy-preserving network for data sharing. However, in order to participate in such a network, a lot of technical and administrative prerequisites have to be made, which could pose bottlenecks and new obstacles for non-technical personnel during their deployment. We have identified three major problems in the current state-of-the-art. Namely, the missing compliance with FAIR data principles, the automation of processes, and the installation. In this work, we present a seamless on-boarding workflow based on a DA reference architecture for data sharing institutions to address these problems. The on-boarding service manages all technical configurations and necessities to reduce the deployment time. Our aim is to use well-established and conventional technologies to gain acceptance through enhanced ease of use. We evaluate our development with six institutions across Germany by conducting a DA study with open-source breast cancer data, which represents the second contribution of this work. We find that our on-boarding solution lowers technical barriers and efficiently deploys all necessary components and is, therefore, indeed an enabler for collaborative data sharing.
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PublicationA Service Oriented Architecture for the Digitalization and Automation of Distribution Grids( 2022)Modern distribution grids are complex systems that need advanced management for their secure and reliable operation. The Information and Communication Technology domain today offers unprecedented opportunities for the smart design of tools in support of grid operators. This paper presents a new philosophy for the digitalization and automation of distribution grids, based on a modular architecture of microservices implemented via container technology. This architecture enables a service-oriented deployment of the intelligence needed in the Distribution Management Systems, moving beyond the traditional view of monolithic software installations in the control rooms. The proposed architecture unlocks a broad set of possibilities, including cloud-based implementations, extension of legacy systems and fast integration of machine learning-based analytic tools. Moreover, it potentially opens a completely new market of turnkey services for distribution grid management, thus avoiding large upfront investments for grid operators. This paper presents the main concepts and benefits of the proposed philosophy, together with an example of field implementation based on open source components carried out in the context of the European project SOGNO.
