Dr. rer. nat.

Hecker, Dirk

Filters

2
3 2
Reset filters

Settings
Now showing 1 - 5 of 5
No Thumbnail Available
Publication

Informed Machine Learning for Industry

2019 , Bauckhage, Christian , Schulz, Daniel , Hecker, Dirk

Deep neural networks have pushed the boundaries of artificial intelligence but their training requires vast amounts of data and high performance hardware. While truly digitised companies easily cope with these prerequisites, traditional industries still often lack the kind of data or infrastructures the current generation of end-to-end machine learning depends on. The Fraunhofer Center for Machine Learning therefore develops novel solutions which are informed by expert knowledge. These typically require less training data and are more transparent in their decision-making processes.

View
No Thumbnail Available
Publication

Spatial data mining in practice

2010 , Körner, Christine , Hecker, Dirk , Krause-Traudes, Maike , May, Michael , Scheider, Simon , Schulz, Daniel , Stange, Hendrik , Wrobel, Stefan

Almost any data can be referenced in geographic space. Such data permit advanced analyses that utilize the position and relationships of objects in space as well as geographic background information. Even though spatial data mining is still a young research discipline, in the past years research advances have shown that the particular challenges of spatial data can be mastered and that the technology is ready for practical application when spatial aspects are treated as an integrated part of data mining and model building. In this chapter in particular, we give a detailed description of several customer projects that we have carried out and which all involve customized data mining solutions for business relevant tasks. The applications range from customer segmentation to the prediction of traffic frequencies and the analysis of GPS trajectories. They have been selected to demonstrate key challenges, to provide advanced solutions and to arouse further research questions.

View
No Thumbnail Available
Publication

Detecting Mobility Patterns with Stationary Bluetooth Sensors: A real-world Case Study

2015 , Müller, Marc , Schulz, Daniel , Mock, Michael , Hecker, Dirk

A Bluetooth sensor network was built up in the city of Bonn to measure Bluetooth MAC-addresses. The results of the acquired data are separated on a macro level and mobility patterns. We have collected nearly 5 million data points from 14 distinct stationary sensors over a period of 1 month and recognized over 85.000 unique devices. We show that the data is sufficiently dense to detect commuter patterns based on a Fourier analysis. In addition, we discuss limitations found in the dataset and present lessons learned.

View
No Thumbnail Available
Publication

Pedestrian flow prediction in extensive road networks using biased observational data

2008 , Scheider, Simon , May, Michael , Rösler, Roberto , Schulz, Daniel , Hecker, Dirk

In this paper, we discuss an application of spatial data mining to predict pedestrian flow in extensive road networks using a large biased sample. Existing out-of-the-box techniques are not able to appropriately deal with its challenges and constraints, in particular with sample selection bias. For this purpose, we introduce s-knn-apriori, an efficient nearest neighbor based spatial mining algorithm that allows prior knowledge and deductive models to be included in a straightforward and easy way.

View
No Thumbnail Available
Publication

Modeling micro-movement variability in mobility studies

2011 , Hecker, Dirk , Körner, Christine , Stange, Hendrik , Schulz, Daniel , May, Michael

During the past years the interest in the exploitation of mobility information has increased significantly. Along with these interests, new demands on mobility data sets have been posed. One particular demand is the evaluation of movement data on a high level of spatial detail. The high dimensionality of geographic space, however, makes this requirement hard to fulfill. Even large mobility studies cannot guarantee to comprise all movement variation on a high level of detail. In this paper we present an approach to increase the variability of movement data on microscopic scale in order to achieve a better representation of population movement. Our approach consists of two steps. First, we perform a spatial aggregation of trajectory data in order to counteract sparseness and to preserve movement on macroscopic scale. Second, we disaggregate the data in geographic space based on traffic distribution knowledge using repeated simulation. Our approach is applied in a real-world business application for the Ger-man outdoor advertising industry to measure the performance of poster sites.

View