Reducing Complexity Of Large EPCs

Automatically Improve Software Architecture Models for Performance, Reliability, and Costs using Evolutionary Algorithms

This document proposes a formal approach to constructing process views from non-well-structured BPMN processes. It defines key BPMN elements and how they relate in a process. Rules are defined to regulate view generation while ensuring structural and behavioral consistency. An algorithm is developed to find the minimal set of elements to aggregate for a given user-specified set. This approach allows selective aggregation of branches and considers events and exceptions in the aggregation.

This explores the matrix addition and multiplication as two problems to reduce the latency of these two operations during execution.

The document discusses resource optimization of workflow problems. It introduces the motivation to optimize flexible production systems using a single assembly line for multiple variants. It then describes workflow abstraction and terms, including attributed resources, partially ordered plans, flexible processes, continuous supply, and partitioning. The document outlines the basic model, including data structures, process logic representation as a partially ordered plan, partitioning workflows into blocks, and calculating infrastructure needs and cycle time. It also discusses the heuristic approach and implementation in Mozart/Oz constraint programming.

The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen the application of region theory in process mining for industrial scenarios.

The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen the application of region theory in process mining for industrial scenarios.

The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen application of region theory for industrial process mining scenarios.

The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen the application of region theory in process mining for industrial scenarios.

The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen application of the theory of regions for industrial Process Mining scenarios.