Business Process Visualization - Use Cases, Challenges, Solutions

The proper visualization and monitoring of their (ongoing) business processes is crucial for any enterprise. Thus a broad spectrum of processes has to be visualized ranging from simple, short–running processes to complex long–running ones (consisting of up to hundreds of activities). In any case, users shall be able to quickly understand the logic behind a process and to get a quick overview of related tasks. One practical problem arises when different fragments of a business process are scattered over several systems where they are often modeled using different process meta models (e.g., High–Level Petri Nets). The challenge is to find an integrated and user–friendly visualization for these business processes. In this paper we discover use cases relevant in this context. Since existing graph layout approaches have focused on general graph drawing so far we further develop a specific approach for layouting business process graphs. The work presented in this paper is embedded within a larger project (Proviado) on the visualization of automotive processes

DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding

While deep neural networks have led to human-level performance on computer vision tasks, they have yet to demonstrate similar gains for holistic scene understanding. In particular, 3D context has been shown to be an extremely important cue for scene understanding - yet very little research has been done on integrating context information with deep models. This paper presents an approach to embed 3D context into the topology of a neural network trained to perform holistic scene understanding. Given a depth image depicting a 3D scene, our network aligns the observed scene with a predefined 3D scene template, and then reasons about the existence and location of each object within the scene template. In doing so, our model recognizes multiple objects in a single forward pass of a 3D convolutional neural network, capturing both global scene and local object information simultaneously. To create training data for this 3D network, we generate partly hallucinated depth images which are rendered by replacing real objects with a repository of CAD models of the same object category. Extensive experiments demonstrate the effectiveness of our algorithm compared to the state-of-the-arts. Source code and data are available at http://deepcontext.cs.princeton.edu.Comment: Accepted by ICCV201

Geometric guides for interactive evolutionary design

This thesis describes the addition of novel Geometric Guides to a generative Computer-Aided Design (CAD) application that supports early-stage concept generation. The application generates and evolves abstract 3D shapes, used to inspire the form of new product concepts. It was previously a conventional Interactive Evolutionary system where users selected shapes from evolving populations. However, design industry users wanted more control over the shapes, for example by allowing the system to influence the proportions of evolving forms. The solution researched, developed, integrated and tested is a more cooperative human-machine system combining classic user interaction with innovative geometric analysis. In the literature review, different types of Interactive Evolutionary Computation (IEC), Pose Normalisation (PN), Shape Comparison, and Minimum-Volume Bounding Box approaches are compared, with some of these technologies identified as applicable for this research. Using its Application Programming Interface, add-ins for the Siemens NX CAD system have been developed and integrated with an existing Interactive Evolutionary CAD system. These add-ins allow users to create a Geometric Guide (GG) at the start of a shape exploration session. Before evolving shapes can be compared with the GG, they must be aligned and scaled (known as Pose Normalisation in the literature). Computationally-efficient PN has been achieved using geometric functions such as Bounding Box for translation and scaling, and Principle Axes for the orientation. A shape comparison algorithm has been developed that is based on the principle of non-intersecting volumes. This algorithm is also implemented with standard, readily available geometric functions, is conceptually simple, accessible to other researchers and also offers appropriate efficacy. Objective geometric testing showed that the PN and Shape Comparison methods developed are suitable for this guiding application and can be efficiently adapted to enhance an Interactive Evolutionary Design system. System performance with different population sizes was examined to indicate how best to use the new guiding capabilities to assist users in evolutionary shape searching. This was backed up by participant testing research into two user interaction strategies. A Large Background Population (LBP) approach where the GG is used to select a sub-set of shapes to show to the user was shown to be the most effective. The inclusion of Geometric Guides has taken the research from the existing aesthetic focused tool to a system capable of application to a wider range of engineering design problems. This system supports earlier design processes and ideation in conceptual design and allows a designer to experiment with ideas freely to interactively explore populations of evolving solutions. The design approach has been further improved, and expanded beyond the previous quite limited scope of form exploration