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265032

Research outputs

As an application-oriented research organisation, Fraunhofer aims to conduct highly innovative and solution-oriented research - for the benefit of society and to strengthen the German and European economy.

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Projects

Fraunhofer is tackling the current challenges facing industry head on. By pooling their expertise and involving industrial partners at an early stage, the Fraunhofer Institutes involved in the projects aim to turn original scientific ideas into marketable products as quickly as possible.

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Researchers

Scientific achievement and practical relevance are not opposites - at Fraunhofer they are mutually dependent. Thanks to the close organisational links between Fraunhofer Institutes and universities, science at Fraunhofer is conducted at an internationally first-class level.

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Institutes

The Fraunhofer-Gesellschaft is the leading organisation for applied research in Europe. Institutes and research facilities work under its umbrella at various locations throughout Germany.

Recent Additions

  • Publication
    Defining and visualizing process execution variants from partially ordered event data
    ( 2024) ;
    Zerbato, Francesca
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    Aalst, Wil van der
    The execution of operational processes generates event data stored in enterprise information systems. Process mining techniques analyze such event data to obtain insights vital for decision-makers to improve the reviewed process. In this context, event data visualizations are essential. We focus on visualizing variants describing process executions that are control flow equivalent. Such variants are an integral concept for process mining and are used, for instance, for data exploration and filtering. We propose high-level and low-level variants covering different levels of abstraction and present corresponding visualizations. Compared to existing variant visualizations, we support partially ordered event data and allow for heterogeneous temporal information per event, i.e., we support both time intervals and time points. We evaluate our contributions using automated experiments showing practical applicability to real-life event data. Finally, we present a user study indicating significantly improved usefulness and ease of use of the proposed high-level variant visualization compared to existing variant visualizations for typical analysis tasks.
  • Publication
    Self-Adjusting Partially Ordered Lists
    ( 2023)
    Addanki, Vamsi
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    Pacut, Maciej
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    Pourdamghani, Arash
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    Rétvári, Gabor
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    Vanerio, Juan
    We introduce self-adjusting partially ordered lists, a generalization of self-adjusting lists where additionally there may be constraints for the relative order of some nodes in the list. The lists self-adjust to improve performance while serving input sequences exhibiting favorable properties, such as locality of reference, but the constraints must be respected.We design a deterministic adjusting algorithm that operates without any assumptions about the input distribution and without maintaining frequency statistics or timestamps. Despite the more general model, we show that our deterministic algorithm performs closely to optimum (it is 4-competitive). In addition, we design a family of randomized algorithms with improved competitive ratios, handling also a more general rearrangement cost model, scaled by an arbitrary constant d ≥1. Moreover, we observe that different constraints influence the competitiveness of online algorithms, and we shed light on this aspect with a lower bound.We investigate the applicability of our self-adjusting lists in the context of network packet classification. Our evaluations show that our classifier performs similarly to a static list for low-locality traffic, but significantly outperforms Efficuts (by factor 7x), CutSplit (3.6x) and the static list (14x) for high locality and small rulesets.
  • Publication
    SeedTree: A Dynamically Optimal and Local Self-Adjusting Tree
    ( 2023)
    Pourdamghani, Arash
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    Avin, Chen
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    Sama, Robert
    ;
    We consider the fundamental problem of designing a self-adjusting tree, which efficiently and locally adapts itself towards the demand it serves (namely accesses to the items stored by the tree nodes), striking a balance between the benefits of such adjustments (enabling faster access) and their costs (reconfigurations). This problem finds applications, among others, in the context of emerging demand-aware and reconfigurable datacenter networks and features connections to self-adjusting data structures. Our main contribution is SeedTree, a dynamically optimal self-adjusting tree which supports local (i.e., greedy) routing, which is particularly attractive under highly dynamic demands. SeedTree relies on an innovative approach which defines a set of unique paths based on randomized item addresses, and uses a small constant number of items per node. We complement our analytical results by showing the benefits of SeedTree empirically, evaluating it on various synthetic and real-world communication traces.
  • Publication
    The Twin Transformation Butterfly: Capabilities for an Integrated Digital and Sustainability Transformation
    ( 2024)
    Christmann, Anne-Sophie
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    Crome, Carlotta
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    Graf-Drasch, Valerie
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    Oberländer, Anna Maria
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    Schmidt, Leonie
    Complex digitalization and sustainability challenges shape today’s management agendas. To date, the dedication of Information Systems research to both challenges has not been equal in terms of effort and reward. Building capabilities to leverage the synergetic potential of digital and sustainability transformation may enhance organizational performance and imply new value creation for the common good. To uncover such synergetic potential, this work conceptualizes the “twin transformation” construct as a value-adding reinforcing interplay between digital transformation and sustainability transformation efforts that improve an organization by leveraging digital technologies to enable sustainability and to guide digital progress by leveraging sustainability. The twin transformation conceptualization is complemented with a capability framework for twin transformation drawing from dynamic capability theory. This work contributes to descriptive knowledge of the interplay between digital transformation and sustainability transformation, setting a foundation for further theorizing on twin transformation and enabling organizations to twin transform.

Most viewed

  • Publication
    Enterprise Interoperability
    (Wiley, 2018)
    Zelm, Martin
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    Jaekel, Frank-Walter
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    Doumeingts, Guy
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    Wollschlaeger, Martin
    Markets, stakeholders and information technologies will constantly evolve making it challenging for a single organization to keep up with the competition. Modern production enterprises are responding to this challenge with industries 4.0 and interoperable solutions in collaborative networks to become more reactive and innovative for their organization and their production systems. The International Conference on Enterprise Interoperability (I-ESA 2018) is presenting interoperable solutions for enterprises from a research and business impact point of view. The workshops address Smart Services and new technologies like the Next Generation Internet (Internet of Things, Cloud-based platforms, and Artificial Intelligence) applied in Future Manufacturing systems using Digital Transformation. The proceedings contain short papers from eleven workshops and from a Doctoral Symposium. One particular method used for each workshop has been to exchange knowledge about actual research and applications and to interactively discuss issues and new ideas between the presenters and the audience of experts from research and industry. Because the growth of internet of things (IOT) technology, stakeholders who come from different research areas (information modeling area, dynamic process modeling area, and so on) will very easily and simultaneously evaluate and control the manufacturing process from all over the world. Therefore, there is an urgent need for a comprehensive enterprise modeling methodology with the integration of information modeling and dynamic process modeling method. However, it is known that there is limited research on the realization of a modeling methodology that can simultaneously handle information modeling and dynamic process modeling. The method for object-oriented business process optimization (MO2GO) system performs modeling in terms of information and process. However, the process modeling part in this system is static. Meanwhile, the Petri net mathematical modeling language has strong dynamic simulation capability. Thus, our main contribution is to analyze the characteristics of Petri net, which would be helpful to the dynamic process modeling realization in the MO2GO system, and integrate Petri net engine into the MO2GO system to allow a static process model to become dynamic. In here, the system can not only display a simulated manufacturing process but also calculate actual information (time and cost) for a final manufactured product. Therefore, it is possible for the system to handle both information modeling and dynamic process modeling. Finally, the MO2GO system will be more competitive in the future industry.
  • Publication
  • Publication
    Mastering Uncertainty in Model-Based Prediction of Vibroacoustic Vehicle Properties
    ( 2022)
    Xu, Wei
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    Feldmann, Robert
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    Kleinfeller, Nikolai
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    Adams, Christian
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    Vibroacoustic properties specify among others the quality of vehicles and contribute to customer satisfaction. In the early design phase, a finite element (FE) analysis is usually carried out to predict and design vibroacoustic properties. Nevertheless, the inevitable lack of information adversely affects the model-based prediction like vibroacoustic vehicle properties. For instance, a deterministic one-point model parameter like Young's Modulus, which ignores the variance due to manufacturing tolerance, leads to among others an incomplete determination of real vibroacoustic behavior of the vehicle in FE simulation. At this point, uncertainty is only partially taken into account in vehicle vibroacoustic. This paper contributes to mastering uncertainty in model-based prediction of vibroacoustic vehicle properties. A category comprising data-, model- and structural uncertainty and their mathematical description is preliminarily introduced to provide an insight into uncertainty in the early design phase of vehicle vibroacoustic. In regard to this, this paper introduces the concept and framework to treat uncertainty in vehicle vibroacoustics. It is composed of the following three steps: (1) Data uncertainty is quantified and can be reduced by a probabilistic approach like Bayesian Inference based statistical model calibration using the information of existing data. (2) Model uncertainty can then be treated by capturing and calibrating the discrepancy function. (3) Structural uncertainty can be treated by exploring the topology of structure through the occurrence probability of equipment variants. Possible challenges of the proposed concept like computational effort are highlighted and the future implementation of the approach is briefly described.
  • Publication
    RIFM fragrance ingredient safety assessment, Methylcyclooctyl carbonate, CAS Registry Number 61699-38-5
    ( 2017)
    Api, A.M.
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    Belsito, D.
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    Botelho, D.
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    Browne, D.
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    Bruze, M.
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    Burton, G.A.
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    Buschmann, J.
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    Dagli, M.L.
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    Date, M.
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    Dekant, W.
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    Deodhar, C.
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    Francis, M.
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    Fryer, A.D.
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    Joshi, K.
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    La Cava, S.
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    Lapczynski, A.
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    Liebler, D.C.
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    O'Brien, D.
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    Parakhia, R.
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    Patel, A.
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    Penning, T.M.
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    Ritacco, G.
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    Romine, J.
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    Salvito, D.
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    Schultz, T.W.
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    Sipes, I.G.
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    Thakkar, Y.
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    Theophilus, E.H.
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    Tiethof, A.K.
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    Tokura, Y.
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    Tsang, S.
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    Wahler, J.