@phdthesis{Ladleif2021,
  author    = {Ladleif, Jan},
  title     = {Enforceability aspects of smart contracts on blockchain networks},
  doi       = {10.25932/publishup-51908},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-519088},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xix, 152},
  year      = {2021},
  abstract  = {Smart contracts promise to reform the legal domain by automating clerical and procedural work, and minimizing the risk of fraud and manipulation. Their core idea is to draft contract documents in a way which allows machines to process them, to grasp the operational and non-operational parts of the underlying legal agreements, and to use tamper-proof code execution alongside established judicial systems to enforce their terms. The implementation of smart contracts has been largely limited by the lack of an adequate technological foundation which does not place an undue amount of trust in any contract party or external entity. Only recently did the emergence of Decentralized Applications (DApps) change this: Stored and executed via transactions on novel distributed ledger and blockchain networks, powered by complex integrity and consensus protocols, DApps grant secure computation and immutable data storage while at the same time eliminating virtually all assumptions of trust. However, research on how to effectively capture, deploy, and most of all enforce smart contracts with DApps in mind is still in its infancy. Starting from the initial expression of a smart contract's intent and logic, to the operation of concrete instances in practical environments, to the limits of automatic enforcement---many challenges remain to be solved before a widespread use and acceptance of smart contracts can be achieved. This thesis proposes a model-driven smart contract management approach to tackle some of these issues. A metamodel and semantics of smart contracts are presented, containing concepts such as legal relations, autonomous and non-autonomous actions, and their interplay. Guided by the metamodel, the notion and a system architecture of a Smart Contract Management System (SCMS) is introduced, which facilitates smart contracts in all phases of their lifecycle. Relying on DApps in heterogeneous multi-chain environments, the SCMS approach is evaluated by a proof-of-concept implementation showing both its feasibility and its limitations. Further, two specific enforceability issues are explored in detail: The performance of fully autonomous tamper-proof behavior with external off-chain dependencies and the evaluation of temporal constraints within DApps, both of which are essential for smart contracts but challenging to support in the restricted transaction-driven and closed environment of blockchain networks. Various strategies of implementing or emulating these capabilities, which are ultimately applicable to all kinds of DApp projects independent of smart contracts, are presented and evaluated.},
  language  = {en}
}
@phdthesis{Bazhenova2018,
  author    = {Bazhenova, Ekaterina},
  title     = {Discovery of Decision Models Complementary to Process Models},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-410020},
  school      = {Universit{\"a}t Potsdam},
  year      = {2018},
  abstract  = {Business process management is an acknowledged asset for running an organization in a productive and sustainable way. One of the most important aspects of business process management, occurring on a daily basis at all levels, is decision making. In recent years, a number of decision management frameworks have appeared in addition to existing business process management systems. More recently, Decision Model and Notation (DMN) was developed by the OMG consortium with the aim of complementing the widely used Business Process Model and Notation (BPMN). One of the reasons for the emergence of DMN is the increasing interest in the evolving paradigm known as the separation of concerns. This paradigm states that modeling decisions complementary to processes reduces process complexity by externalizing decision logic from process models and importing it into a dedicated decision model. Such an approach increases the agility of model design and execution. This provides organizations with the flexibility to adapt to the ever increasing rapid and dynamic changes in the business ecosystem. The research gap, identified by us, is that the separation of concerns, recommended by DMN, prescribes the externalization of the decision logic of process models in one or more separate decision models, but it does not specify this can be achieved. The goal of this thesis is to overcome the presented gap by developing a framework for discovering decision models in a semi-automated way from information about existing process decision making. Thus, in this thesis we develop methodologies to extract decision models from: (1) control flow and data of process models that exist in enterprises; and (2) from event logs recorded by enterprise information systems, encapsulating day-to-day operations. Furthermore, we provide an extension of the methodologies to discover decision models from event logs enriched with fuzziness, a tool dealing with partial knowledge of the process execution information. All the proposed techniques are implemented and evaluated in case studies using real-life and synthetic process models and event logs. The evaluation of these case studies shows that the proposed methodologies provide valid and accurate output decision models that can serve as blueprints for executing decisions complementary to process models. Thus, these methodologies have applicability in the real world and they can be used, for example, for compliance checks, among other uses, which could improve the organization's decision making and hence it's overall performance.},
  language  = {en}
}
@phdthesis{Meyer2015,
  author    = {Meyer, Andreas},
  title     = {Data perspective in business process management},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-84806},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxi, 362},
  year      = {2015},
  abstract  = {Gesch{\"a}ftsprozessmanagement ist ein strukturierter Ansatz zur Modellierung, Analyse, Steuerung und Ausf{\"u}hrung von Gesch{\"a}ftsprozessen, um Gesch{\"a}ftsziele zu erreichen. Es st{\"u}tzt sich dabei auf konzeptionelle Modelle, von denen Prozessmodelle am weitesten verbreitet sind. Prozessmodelle beschreiben wer welche Aufgabe auszuf{\"u}hren hat, um das Gesch{\"a}ftsziel zu erreichen, und welche Informationen daf{\"u}r ben{\"o}tigt werden. Damit beinhalten Prozessmodelle Informationen {\"u}ber den Kontrollfluss, die Zuweisung von Verantwortlichkeiten, den Datenfluss und Informationssysteme. Die Automatisierung von Gesch{\"a}ftsprozessen erh{\"o}ht die Effizienz der Arbeitserledigung und wird durch Process Engines unterst{\"u}tzt. Daf{\"u}r werden jedoch Informationen {\"u}ber den Kontrollfluss, die Zuweisung von Verantwortlichkeiten f{\"u}r Aufgaben und den Datenfluss ben{\"o}tigt. W{\"a}hrend aktuelle Process Engines die ersten beiden Informationen weitgehend automatisiert verarbeiten k{\"o}nnen, m{\"u}ssen Daten manuell implementiert und gewartet werden. Dem entgegen verspricht ein modell-getriebenes Behandeln von Daten eine vereinfachte Implementation in der Process Engine und verringert gleichzeitig die Fehleranf{\"a}lligkeit dank einer graphischen Visualisierung und reduziert den Entwicklungsaufwand durch Codegenerierung. Die vorliegende Dissertation besch{\"a}ftigt sich mit der Modellierung, der Analyse und der Ausf{\"u}hrung von Daten in Gesch{\"a}ftsprozessen. Als formale Basis f{\"u}r die Prozessausf{\"u}hrung wird ein konzeptuelles Framework f{\"u}r die Integration von Prozessen und Daten eingef{\"u}hrt. Dieses Framework wird durch operationelle Semantik erg{\"a}nzt, die mittels einem um Daten erweiterten Petrinetz-Mapping vorgestellt wird. Die modellgetriebene Ausf{\"u}hrung von Daten muss komplexe Datenabh{\"a}ngigkeiten, Prozessdaten und den Datenaustausch ber{\"u}cksichtigen. Letzterer tritt bei der Kommunikation zwischen mehreren Prozessteilnehmern auf. Diese Arbeit nutzt Konzepte aus dem Bereich der Datenbanken und {\"u}berf{\"u}hrt diese ins Gesch{\"a}ftsprozessmanagement, um Datenoperationen zu unterscheiden, um Abh{\"a}ngigkeiten zwischen Datenobjekten des gleichen und verschiedenen Typs zu spezifizieren, um modellierte Datenknoten sowie empfangene Nachrichten zur richtigen laufenden Prozessinstanz zu korrelieren und um Nachrichten f{\"u}r die Prozess{\"u}bergreifende Kommunikation zu generieren. Der entsprechende Ansatz ist nicht auf eine bestimmte Prozessbeschreibungssprache begrenzt und wurde prototypisch implementiert. Die Automatisierung der Datenbehandlung in Gesch{\"a}ftsprozessen erfordert entsprechend annotierte und korrekte Prozessmodelle. Als Unterst{\"u}tzung zur Datenannotierung f{\"u}hrt diese Arbeit einen Algorithmus ein, welcher Informationen {\"u}ber Datenknoten, deren Zust{\"a}nde und Datenabh{\"a}ngigkeiten aus Kontrollflussinformationen extrahiert und die Prozessmodelle entsprechend annotiert. Allerdings k{\"o}nnen gew{\"o}hnlich nicht alle erforderlichen Informationen aus Kontrollflussinformationen extrahiert werden, da detaillierte Angaben {\"u}ber m{\"o}gliche Datenmanipulationen fehlen. Deshalb sind weitere Prozessmodellverfeinerungen notwendig. Basierend auf einer Menge von Objektlebenszyklen kann ein Prozessmodell derart verfeinert werden, dass die in den Objektlebenszyklen spezifizierten Datenmanipulationen automatisiert in ein Prozessmodell {\"u}berf{\"u}hrt werden k{\"o}nnen. Prozessmodelle stellen eine Abstraktion dar. Somit fokussieren sie auf verschiedene Teilbereiche und stellen diese im Detail dar. Solche Detailbereiche sind beispielsweise die Kontrollflusssicht und die Datenflusssicht, welche oft durch Aktivit{\"a}ts-zentrierte beziehungsweise Objekt-zentrierte Prozessmodelle abgebildet werden. In der vorliegenden Arbeit werden Algorithmen zur Transformation zwischen diesen Sichten beschrieben. Zur Sicherstellung der Modellkorrektheit wird das Konzept der „weak conformance" zur {\"U}berpr{\"u}fung der Konsistenz zwischen Objektlebenszyklen und dem Prozessmodell eingef{\"u}hrt. Dabei darf das Prozessmodell nur Datenmanipulationen enthalten, die auch in einem Objektlebenszyklus spezifiziert sind. Die Korrektheit wird mittels Soundness-{\"U}berpr{\"u}fung einer hybriden Darstellung ermittelt, so dass Kontrollfluss- und Datenkorrektheit integriert {\"u}berpr{\"u}ft werden. Um eine korrekte Ausf{\"u}hrung des Prozessmodells zu gew{\"a}hrleisten, m{\"u}ssen gefundene Inkonsistenzen korrigiert werden. Daf{\"u}r werden f{\"u}r jede Inkonsistenz alternative Vorschl{\"a}ge zur Modelladaption identifiziert und vorgeschlagen. Zusammengefasst, unter Einsatz der Ergebnisse dieser Dissertation k{\"o}nnen Gesch{\"a}ftsprozesse modellgetrieben ausgef{\"u}hrt werden unter Ber{\"u}cksichtigung sowohl von Daten als auch den zuvor bereits unterst{\"u}tzten Perspektiven bez{\"u}glich Kontrollfluss und Verantwortlichkeiten. Dabei wird die Modellerstellung teilweise mit automatisierten Algorithmen unterst{\"u}tzt und die Modellkonsistenz durch Datenkorrektheits{\"u}berpr{\"u}fungen gew{\"a}hrleistet.},
  language  = {en}
}
@phdthesis{RoggeSolti2014,
  author    = {Rogge-Solti, Andreas},
  title     = {Probabilistic Estimation of Unobserved Process Events},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-70426},
  school      = {Universit{\"a}t Potsdam},
  year      = {2014},
  abstract  = {Organizations try to gain competitive advantages, and to increase customer satisfaction. To ensure the quality and efficiency of their business processes, they perform business process management. An important part of process management that happens on the daily operational level is process controlling. A prerequisite of controlling is process monitoring, i.e., keeping track of the performed activities in running process instances. Only by process monitoring can business analysts detect delays and react to deviations from the expected or guaranteed performance of a process instance. To enable monitoring, process events need to be collected from the process environment. When a business process is orchestrated by a process execution engine, monitoring is available for all orchestrated process activities. Many business processes, however, do not lend themselves to automatic orchestration, e.g., because of required freedom of action. This situation is often encountered in hospitals, where most business processes are manually enacted. Hence, in practice it is often inefficient or infeasible to document and monitor every process activity. Additionally, manual process execution and documentation is prone to errors, e.g., documentation of activities can be forgotten. Thus, organizations face the challenge of process events that occur, but are not observed by the monitoring environment. These unobserved process events can serve as basis for operational process decisions, even without exact knowledge of when they happened or when they will happen. An exemplary decision is whether to invest more resources to manage timely completion of a case, anticipating that the process end event will occur too late. This thesis offers means to reason about unobserved process events in a probabilistic way. We address decisive questions of process managers (e.g., "when will the case be finished?", or "when did we perform the activity that we forgot to document?") in this thesis. As main contribution, we introduce an advanced probabilistic model to business process management that is based on a stochastic variant of Petri nets. We present a holistic approach to use the model effectively along the business process lifecycle. Therefore, we provide techniques to discover such models from historical observations, to predict the termination time of processes, and to ensure quality by missing data management. We propose mechanisms to optimize configuration for monitoring and prediction, i.e., to offer guidance in selecting important activities to monitor. An implementation is provided as a proof of concept. For evaluation, we compare the accuracy of the approach with that of state-of-the-art approaches using real process data of a hospital. Additionally, we show its more general applicability in other domains by applying the approach on process data from logistics and finance.},
  language  = {en}
}