Sprechaktbasiertes Adaptives Fallmanagement

With a focus on their pragmatic intention, speech acts have been proposed to improve the design of interactive systems for decades. Yet, early prototypes were isolated applications with limited inferencing capabilities. Alongside, the share of knowledge work in the workforce rapidly increased, and knowledge-intensive processes became customary. The objectives, interactions, intermediate results, and final outcomes of knowledge-intensive processes are typically scattered across many systems. Often, important information is not documented at all. Adaptive case management is intended for emerging, knowledge-intensive processes, where the course of action unfolds as more information becomes available. We apply speech act theory in adaptive case management, since the representation of interactions considers the context they are performed in, regardless of whether this context is a structured, semi-structured, or ad-hoc process. This common representation enables inference regardless of whether an interaction or activity is modeled a priori or documented ad hoc, and ultimately helps in resolving the issue of scattered process information that is loosely coupled by the knowledge workers performing the process. This thesis verifies that speech acts are prevalent and diverse in actual business processes. Moreover, it substantiates that a manageable set of common speech acts for modeling and ad-hoc documentation is applicable in representative knowledge work domains. It investigates the requirements and expectations of adaptive case management in a more fine-grained classification of the knowledge workers to be supported. This way, for complex work with high interdependence, it results in a speech-act-based approach of adaptive case management, that does not require a predefined process model for knowledge-intensive processes or cases. It initially expects activities to be ad hoc, and additional models to simplify or automate routine work can be introduced on demand. It establishes speech-act-based techniques for semantic annotation, modeling, and business rules for compliance monitoring as well as for integration. Thereby, the approach combines structured, semi-structured, and ad-hoc work, while providing guard rails, and line markings for one consolidated, knowledge-intensive process.Sprechakte werden schon seit Jahrzehnten als Möglichkeit gesehen, durch ihren Fokus auf die pragmatische Intention das Design von interaktiven Systemen zu verbessern. Frühe Prototypen waren jedoch isolierte Anwendungen mit begrenzten Inferenzfähigkeiten, was ihren praktischen Einsatz erschwerte. Mit der drastischen Zunahme von Wissensarbeit und wissensintensiven Prozessen hat sich auch die Arbeitswelt deutlich geändert. Allerdings werden die Ziele, Interaktionen, Zwischen- und Endergebnisse wissensintensiver Prozesse typischerweise auf viele beteiligte Systeme verstreut - und wichtige Informationen häufig nicht dokumentiert. Adaptives Fallmanagement soll wissensintensive Prozesse unterstützen, deren Ablauf erst während der Durchführung selbst klar wird. Wir setzen Sprechakttheorie im adaptiven Fallmanagement ein, da sie eine kontextgewahre Repräsentation von Interaktionen erlaubt, aber für diesen Kontext vernachlässigbar ist, ob dieser ein strukturierter, semi-strukturierter oder ein Ad-Hoc-Prozess ist. Durch eine allgemeine Repräsentation von Intentionen ermöglicht dies Inferenz unabhängig davon, ob eine Interaktion oder Aktivität modelliert oder ad hoc dokumentiert wurde. Verstreute Prozessinformationen, die heute verantwortliche Wissensarbeiter verbinden, können mit einem solchen Ansatz besser in einem Gesamtkontext verarbeitet werden. Diese Arbeit bekräftigt, dass Sprechakte in Geschäftsprozessen verbreitet und divers sind. Für repräsentative Domänen von Wissensarbeit überprüft sie, ob die Diversität eine für Modellierung und Dokumentation beherrschbare Menge an Sprechakten erlaubt. Sie untersucht auch Anforderungen und Erwartungen an adaptive Fallmanagementsysteme – mit einer genaueren Klassifizierung der unterstützten Wissensarbeiter. Dadurch leitet sie für komplexe Arbeit mit hoher Interdependenz einen sprechaktbasierten Ansatz für adaptives Fallmanagement ab. Dieser Ansatz unterstützt wissensintensive Prozesse auch, wenn vorab kein Prozessmodell vorhanden ist. Aktivitäten werden zunächst als ad hoc angenommen. Nach Bedarf können Routinearbeiten durch Prozessmodelle vereinfacht oder automatisiert werden. Weiterhin werden sprechaktbasierte Techniken für semantische Annotationen, Modellierung und Geschäftsregeln für Compliance-Überwachung sowie Integration eingeführt. Dies ermöglicht die Verbindung von strukturierten, semi-strukturierten und Ad-Hoc-Vorgängen in einem übergreifenden, wissensintensiven Prozess, der durch warnende sowie verhindernde Kontrollmöglichkeiten unterstützt wird

Predictive Business Process Deviation Monitoring

Business processes run at the core of an organisation\u27s value creation and are often the target of optimisation efforts. Organisations aim at adhering to their optimised processes. However, deviations from the optimised process still occur and may potentially impede efficiency in process executions. Conformance checking can provide valuable insights regarding past process deviations, but it cannot identify deviations before they occur. Outcome-oriented predictive business process monitoring (PBPM) provides a set of methods to predict process outcomes, e.g., key performance indicators. We propose an outcome-oriented PBPM method for predictive deviation monitoring using conformance checking and deep learning to draw the most out of the two domains. By leveraging early intervention, the method supports the proactive handling of deviations, i.e., inserted and missing events in process instances, to reduce their potential harm. Our evaluation shows that the method can predict business process deviations with high predictive quality, particularly for processes with fewer variants

Sequences, yet functions: The dual nature of data-stream processing

Data-stream processing has continuously risen in importance as the amount of available data has been steadily increasing over the last decade. Besides traditional domains such as data-center monitoring and click analytics, there is an increasing number of network-enabled production machines that generate continuous streams of data. Due to their continuous nature, queries on data-streams can be more complex, and distinctly harder to under- stand than database queries. As users have to consider operational details, maintenance and debugging become challenging. Current approaches model data-streams as sequences, because this is the way they are physically received. This forces query authors to often consider unnecessary details. We explore a different way of modeling data-streams by focusing on time-slicing semantics. This results in a model based on functions, which is better for abstract reasoning about query semantics. By adapting the given definitions of relevant concepts in stream processing to our model, we illustrate the practical use of our approach. To achieve this, we link data-streams and query primitives to concepts in functional programming and mathematics. Most noteworthy, we prove that data-streams are monads, and show how to derive monad definitions for current data-stream models. We provide an abstract, yet practical perspective on data-stream-related subjects based on a sound, consistent query model. Our work can serve as solid foundation for future data-stream query languages