Research Center for Information Technology
Research Center for Information Technology
Ranft B.,Research Center for Information Technology |
Strauss T.,Karlsruhe Institute of Technology
2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014 | Year: 2014
Stereo cameras enable a 3D reconstruction of viewed scenes and are therefore well-suited sensors for many advanced driver assistance systems and autonomous driving. Modern algorithms for estimating distances for every image pixel achieve high-quality results, but their real-time capability is very limited. In contrast, window-based local methods can be implemented very efficiently but are more prone to errors. This is particularly true for spatial changes of distance within the matching window, most prominently on surfaces such as the road which are not parallel to but rather slanted towards the image plane. In this paper we present a method to compensate the impact of this effect for arbitrarily oriented sets of planes. It does not depend on any modifications to the actual distance estimation. Instead, it only applies specific transformations to input images and intermediate results. By combining this approach with existing implementations which efficiently use either multi-core or graphics processors, we were able to significantly increase quality while maintaining real-time throughputs on a compact target system. © 2014 IEEE.
Anicic D.,Research Center for Information Technology |
Rudolph S.,Karlsruhe Institute of Technology |
Fodor P.,State University of New York at Stony Brook |
Stojanovic N.,Research Center for Information Technology
Semantic Web | Year: 2012
Addressing dynamics and notifications in the SemanticWeb realm has recently become an important area of research. Run time data is continuously generated by multiple social networks, sensor networks, various on-line services and so forth. How to get advantage of this continuously arriving data (events) remains a challenge - that is, how to integrate heterogeneous event streams, combine them with background knowledge (e.g., an ontology), and perform event processing and stream reasoning. In this paper we describe ETALIS - a system which enables specification and monitoring of changes in near real time. Changes can be specified as complex event patterns, and ETALIS can detect them in real time. Moreover the system can perform reasoning over streaming events with respect to background knowledge. ETALIS implements two languages for specification of event patterns: ETALIS Language for Events, and Event Processing SPARQL. ETALIS has various applicabilities in capturing changes in semantic networks, broadcasting notifications to interested parties, and creating further changes (based on processing of the temporal, static, or slowly evolving knowledge). © 2009 - IOS Press and the authors. All rights reserved.
Schreiber M.,Research Center for Information Technology |
Knoppel C.,Daimler AG |
Franke U.,Daimler AG
IEEE Intelligent Vehicles Symposium, Proceedings | Year: 2013
Precise and robust localization in real-world traffic scenarios is a new challenge arising in the context of autonomous driving and future driver assistance systems. The required precision is in the range of a few centimeters. In urban areas this precision cannot be achieved by standard global navigation satellite systems (GNSS). Our novel approach achieves this requirement using a stereo camera system and a highly accurate map containing curbs and road markings. The maps are created beforehand using an extended sensor setup. GNSS position is used for initialization only and is not required during the localization process. In the paper we present the localization process and provide an evaluation on a test track under known conditions as well as a long term evaluation on approximately 50 km of rural roads, where a precision in centimeter-range is achieved. © 2013 IEEE.
Levina O.,Research Center for Information Technology
Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOCW | Year: 2016
Being a complex topic Internet of Things (IoT) involves multiple disciplines and approaches. In this paper an initial overview of the modeling techniques within the IoT by the Information Systems researchers is provided. These descriptive results offer a basis for discussion about the research topics that are important in IoT for the Information Systems Research community as well as the first overview of the keywords that the authors use to describe their work in IoT-related context. Publications from the IoT context, including some of the topic areas in smart environment from the AIS electronic library were analyzed towards their application of models within their result presentation. The focus of this preliminary analysis is the description of the application and dissemination of modeling in IoT research. Additionally, the results offer insights into the purpose of the model usage by the topic areas under analysis. The findings indicate that the publications that put themselves directly into the IoT context by mentioning it in the paper title, abstract or keywords frequently provide a general overview on the area and mostly do not involve formal modeling, while a more specific specialization requires the usage of formal modeling tools. © 2016 IEEE.
Traverso-Ribon I.,Research Center for Information Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015
Precisely determining semantic similarity between entities becomes a building block for data mining tasks, and existing approaches tackle this problem by mainly considering ontology-based annotations to decide relatedness. Nevertheless, because semantic similarity measures usually rely on the ontology class hierarchy and blindly treat ontology facts, they may erroneously assign high values of similarity to dissimilar entities. We propose ColorSim, a similarity measure that considers semantics of OWL2 annotations, e.g., relationship types, and implicit facts and their inferring processes, to accurately compute the relatedness of two ontology annotated entities. We compare ColorSim with state-of the- art approaches and report on preliminary experimental results that suggest the benefits of exploiting knowledge encoded in the ontologies to measure similarity. © Springer International Publishing Switzerland 2015.
Grimm S.,Research Center for Information Technology |
Wissmann J.,Research Center for Information Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011
Ontologies may contain redundancy in terms of axioms that logically follow from other axioms and that could be removed for the sake of consolidation and conciseness without changing the overall meaning. In this paper, we investigate methods for removing such redundancy from ontologies. We define notions around redundancy and discuss typical cases of redundancy and their relation to ontology engineering and evolution. We provide methods to compute irredundant ontologies both indirectly by calculating justifications, and directly by utilising a hitting set tree algorithm and module extraction techniques for optimization. Moreover, we report on experimental results on removing redundancy from existing ontologies available on the Web. © 2011 Springer-Verlag Berlin Heidelberg.
Muller L.,Research Center for Information Technology
UbiComp 2013 Adjunct - Adjunct Publication of the 2013 ACM Conference on Ubiquitous Computing | Year: 2013
Reflection on daily work practices can support informal learning and continuous improvement of work practices. This dissertation aims at supporting reflection by employing sensors and corresponding data visualizations to make employees ask the right questions about their work. Two tools have been developed and initial studies have been conducted to evaluate the impact of psychophysiological sensors and proximity sensing for employees in the healthcare domain. The main contribution of this work is the connection of reflective learning and wearable sensors with the goal to persuade employees to reflect. The resulting tools will be evaluated in real work settings.
Haak S.,Research Center for Information Technology |
Menzel M.,Research Center for Information Technology
8th International Conference on Autonomic Computing, ICAC 2011 Co-located Workshops - Proceedings of the 1st ACM/IEEE Workshop on Autonomic Computing in Economics, ACE'11 | Year: 2011
The growing number of Cloud Infrastructure-as-a-Service (IaaS) offerings today leave a wide range of choices when deploying an application in the Cloud. Self-configuring and -optimizing autonomic systems have to select an infrastructure which fits the performance preferences while simultaneously offering the optimal performance per price ratio. A task which is not trivial. Indicators provided by providers are often not coherent and not sufficient to predict the actual performance of a deployed application and, thus, raise the need for benchmarking the offered services. This implies, however, intensive effort to gather the needed metrics, growing with every additional provider taken into consideration. In this paper we present an approach based on the theory of optimal stopping that enables an automated search for an optimal infrastructure service regarding performance-per-price-ratio while reducing costs for benchmarking. © 2011 ACM.
Seifermann S.,Research Center for Information Technology
Proceedings - 2016 13th Working IEEE/IFIP Conference on Software Architecture, WICSA 2016 | Year: 2016
Quality properties including performance, security and compliance are crucial for a system's success but are hard to prove, especially for complex systems. Data flow analyses support this but often only consider source code and thereby introduce high costs of repair. Data flow analyses on the architectural design level use call-and-return semantics or event-based communication between components but do not define data flows as first class entities or consider important runtime or deployment configurations. We propose introducing data flows as first class entities on the architectural level. Analyses ensure that systems meet the quality requirements even after changes in e.g. runtime or deployment configurations. Having data flows modeled as first class entities allows analyzing compliance with privacy laws, requirements for external service providers, and throughput requirements in big data scenarios on architectural level. The results allow early, cost-efficient fixing of issues. © 2016 IEEE.
Bock J.,Research Center for Information Technology |
Hettenhausen J.,Griffith University
Information Sciences | Year: 2012
Particle swarm optimisation (PSO) is a biologically-inspired, population-based optimisation technique that has been successfully applied to various problems in science and engineering. In the context of semantic technologies, optimisation problems also occur but have rarely been considered as such. This work addresses the problem of ontology alignment, which is the identification of overlaps in heterogeneous knowledge bases backing semantic applications. To this end, the ontology alignment problem is revisited as an optimisation problem. A discrete particle swarm optimisation algorithm is designed in order to solve this optimisation problem and compute an alignment of two ontologies. A number of characteristics of traditional PSO algorithms are partially relaxed in this article, such as fixed dimensionality of particles. A complex fitness function based on similarity measures of ontological entities, as well as a tailored particle update procedure are presented. This approach brings several benefits for solving the ontology alignment problem, such as inherent parallelisation, anytime behaviour, and flexibility according to the characteristics of particular ontologies. The presented algorithm has been implemented under the name MapPSO (ontology mapping using particle swarm optimisation). Experiments demonstrate that applying PSO in the context of ontology alignment is a feasible approach. © 2010 Elsevier Inc. All rights reserved.