Lampe, Ove Daae

Lampe, Ove Daae

Senior Scientist

T: +47 909 52 828

M: ove.daae.lampe@cmr.no

Expertise


  • Data Analysis and Big Data

    Big Data challenges existing algorithms, hardware and our way of thinking. CMR aims to help you find your way to the untapped secrets hidden in big data by introducing analysis and visualization tools scaled for big data.

Projects


Publications


  • Abstract Representation of Power System Networks as a Function of Regularity Properties

    Lampe, Ove Daae; Patel, Daniel; A.B. Svendsen,Goodtech Power, Bergen, Norway; T. Tollefsen, Goodtech Power, Bergen, Norway; R.F. Pedersen,Goodtech Power, Bergen, Norway; K.P. Petursson, Goodtech Power, Bergen, Norway

    Goodtech and Statnett SF have developed an online regularity calculator, with minimal delay between acquisition of process values and presentation of calculated regularity indices for the power grid. The simulation tool was put in operation in October 2013 for testing and bug fixing at Statnett’s operational central in Oslo.

  • Curve Density Estimates

    Lampe, Ove Daae; Helwig Hauser

    In this work, we present a technique based on kernel density estimation for rendering smooth curves. With this approach, we produce uncluttered and expressive pictures, revealing frequency information about one, or, multiple curves, independent of the level of detail in the data, the zoom level, and the screen resolution.

  • Interactive Visualization of Streaming Data with Kernel Density Estimation

    Lampe, Ove Daae; Helwig Hauser

    In this paper, we discuss the extension and integration of the statistical concept of Kernel Density Estimation (KDE) in a scatterplot-like visualization for dynamic data at interactive rates. We present a line kernel for representing streaming data, we discuss how the concept of KDE can be adapted to enable a continuous representation of the distribution of a dependent variable of a 2D domain.

  • Model Building in Visualization Space

    Lampe, Ove Daae; Helwig Hauser

    Researching formal models that explain selected natural phenomena of interest is a central aspect of most scientific work. A tested and confirmed model can be the key to classification, knowledge crystallization, and prediction.With this paper we propose a new approach to rapidly draft, fit and quantify model prototypes in visualization space.

  • Visual Analysis of Multivariate Movement Data Using Interactive Difference Views

    Lampe, Ove Daae; Johannes Kehrer, Helwig Hauser

    Movement data consisting of a large number of spatio-temporal agent trajectories is challenging to visualize, especially when all trajectories are attributed with multiple variates. In this paper, we demonstrate the visual exploration of such movement data through the concept of interactive difference views. By reconfiguring the difference views in a fast and flexible way, we enable temporal trend discovery.

  • Curve-Centric Volume Reformation for Comparative Visualization

    Lampe, Ove Daae; Carlos Correa, Kwan-Liu Ma, Helwig Hauser

    We present two visualization techniques for curve-centric volume reformation with the aim to create compelling comparative visualizations. A curve-centric volume reformation deforms a volume, with regards to a curve in space, to create a new space in which the curve evaluates to zero in two dimensions and spans its arc-length in the third. The volume surrounding the curve is deformed such that spatial neighborhood to the curve is preserved.

  • Two-Level Approach to Efficient Visualization of Protein Dynamics

    Lampe, Ove Daae; Ivan Viola Member, IEEE Computer Society , Nathalie Reuter and Helwig Hauser Member, IEEE

    Proteins are highly flexible and large amplitude deformations of their structure, also called slow dynamics, are often decisive to their function. We present a two-level rendering approach that enables visualization of slow dynamics of large protein assemblies.