Titlebook: Data-Driven Process Discovery and Analysis; 5th IFIP WG 2.6 Inte Paolo Ceravolo,Stefanie Rinderle-Ma Conference proceedings 2017 IFIP Inter

COLON

疏远天际

知识

Visual Analytics Meets Process Mining: Challenges and Opportunities,algorithms makes complex information structures more comprehensible and facilitates new insights. In this position paper I portray various concepts of interactive visual support for process mining, focusing on the challenges, but also the great opportunities for analyzing process data with visual analytics methods.

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Anal-Canal

Introduction: Nonlocal or Unreal?-critical aspects. To create complete, valid and reliable planning and customization process data for a product deployment, an overarching engineering process is crucial for the successful completion of a project. In this paper, we introduce a framework for process management in complex engineering

LURE

有毛就脱毛

Introduction: Nonlocal or Unreal?ntinuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Case data (e.g. treating physician, patient age) also influence how processes are executed. Existing . techniques assume processes to be static and therefore

丰富

Conceptual Basis of Quantum Mechanicsithout allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. Firs

小卷发

William Jankowiak,Claudia Woodmane of a consistent terminology to label the activities of these process models. To support this task, prior research has proposed quality metrics to support the usage of consistent terms, mainly based on linguistic relations such as synonymy or homonymy. In this paper, we propose a new approach that

Abrade

Kinship: The Tie that Binds (Disciplines)al effects, delays and performance trends are well-known to exist for business processes, there is up until now no prediction model available that explicitly captures seasonality. In this paper, we introduce time series Petri net models. These models integrate the control flow perspective of Petri n