Prof. Dr.

Hauswirth, Manfred

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  • Publication
    Extracting GHZ states from linear cluster states
    ( 2024)
    Jong, Dirk J. de
    ;
    Hahn, F.
    ;
    Tcholtchev, Nikolay Vassilev
    ;
    Hauswirth, Manfred
    ;
    Pappa, Anna
    Quantum information processing architectures typically only allow for nearest-neighbor entanglement creation. In many cases, this prevents the direct generation of GHZ states, which are commonly used for many communication and computation tasks. Here, we show how to obtain GHZ states between nodes in a network that are connected in a straight line, naturally allowing them to initially share linear cluster states. We prove a strict upper bound of ⌊(n+3)/2⌋ on the size of the set of nodes sharing a GHZ state that can be obtained from a linear cluster state of n qubits, using local Clifford unitaries, local Pauli measurements, and classical communication. Furthermore, we completely characterize all selections of nodes below this threshold that can share a GHZ state obtained within this setting. Finally, we demonstrate these transformations on the IBMQ Montreal quantum device for linear cluster states of up to n=19 qubits.
  • Publication
    Extracting GHZ states from linear cluster states
    ( 2023-11)
    Jong, J. de
    ;
    Hahn, F.
    ;
    Tcholtchev, Nikolay Vassilev
    ;
    Hauswirth, Manfred
    ;
    Pappa, Anna
    Quantum information processing architectures typically only allow for nearest-neighbour entanglement creation. In many cases, this prevents the direct generation of states, which are commonly used for many communication and computation tasks. Here, we show how to obtain states between nodes in a network that are connected in a straight line, naturally allowing them to initially share linear cluster states. We prove a strict upper bound of ⌊(n+3)/2⌋ on the size of the set of nodes sharing a state that can be obtained from a linear cluster state of n qubits, using local Clifford unitaries, local Pauli measurements, and classical communication. Furthermore, we completely characterize all selections of nodes below this threshold that can share a state obtained within this setting. Finally, we demonstrate these transformations on the quantum device for linear cluster states of up to n=19 qubits.
  • Publication
    Design and development of a short-term photovoltaic power output forecasting method based on Random Forest, Deep Neural Network and LSTM using readily available weather features
    ( 2023)
    Rangelov, Denis
    ;
    Boerger, Michell
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    Tcholtchev, Nikolay Vassilev
    ;
    Lämmel, Philipp
    ;
    Hauswirth, Manfred
    Renewable energy sources (RES) are an essential part of building a more sustainable future, with higher diversity of clean energy, reduced emissions and less dependence on finite fossil fuels such as coal, oil and natural gas. The advancements in the renewable energy sources domain bring higher hardware efficiency and lower costs, which improves the likelihood of wider RES adoption. However, integrating renewables such as photovoltaic (PV) systems in the current grid is still a major challenge. The main reason is the volatile, intermittent nature of RES, which increases the complexity of the grid management and maintenance. Having access to accurate PV power output forecasting could reduce the number of power supply disruptions, improve the planning of the available and reserve capacities and decrease the management and operational costs. In this context, this paper explores and evaluates three Artificial Intelligence (AI) methods - random forest (RF), deep neural network (DNN) and long short-term memory network (LSTM), which are applied for the task of short-term PV output power forecasting. Following a statistical forecasting approach, the selected models are trained on weather and PV output data collected in Berlin, Germany. The assembled data set contains predominantly broadly accessible weather features, which makes the proposed approach more cost efficient and easily applicable even for geographic locations without access to specialized hardware or hard-to-obtain input features. The performance achieved by two of the selected algorithms indicates that the RF and the DNN models are able to generate accurate solar power forecasts and are also able to handle sudden changes and shifts in the PV power output.
  • Publication
    Towards a decentralized data hub and query system for federated dynamic data spaces
    ( 2023)
    Phuoc, Danh Le
    ;
    Schimmler, Sonja
    ;
    Le-Tuan, Anh
    ;
    Kuehn, Uwe A.
    ;
    Hauswirth, Manfred
    This position paper proposes a hybrid architecture for secure and efficient data sharing and processing across dynamic data spaces. On the one hand, current centralized approaches are plagued by issues such as lack of privacy and control for users, high costs, and bad performance, making these approaches unsuitable for the decentralized data spaces prevalent in Europe and various industries (decentralized on the conceptual and physical levels while centralized in the underlying implementation). On the other hand, decentralized systems face challenges with limited knowledge of/control over the global system, fair resource utilization, and data provenance. Our proposed Semantic Data Ledger (SDL) approach combines the advantages of both architectures to overcome their limitations. SDL allows users to choose the best combination of centralized and decentralized features, providing a decentralized infrastructure for the publication of structured data with machine-readable semantics. It supports expressive structured queries, secure data sharing, and payment mechanisms based on an underlying autonomous ledger, enabling the implementation of economic models and fair-use strategies.
  • Publication
    Towards building live open scientific knowledge graphs
    ( 2022)
    Le-Tuan, Anh
    ;
    Franzreb, Carlos
    ;
    Phuoc, Danh Le
    ;
    Schimmler, Sonja
    ;
    Hauswirth, Manfred
    Due to the large number and heterogeneity of data sources, it becomes increasingly difficult to follow the research output and the scientific discourse. For example, a publication listed on DBLP may be discussed on Twitter and its underlying data set may be used in a different paper published on arXiv. The scientific discourse this publication is involved in is divided among not integrated systems, and for researchers it might be very hard to follow all discourses a publication or data set may be involved in. Also, many of these data sources-DBLP, arXiv, or Twitter, to name a few-are often updated in real-time. These systems are not integrated (silos), and there is no system for users to query the content/data actively or, what would be even more beneficial, in a publish/subscribe fashion, i.e., a system would actively notify researchers of work interesting to them when such work or discussions become available. In this position paper, we introduce our concept of a live open knowledge graph which can integrate an extensible set of existing or new data sources in a streaming fashion, continuously fetching data from these heterogeneous sources, and interlinking and enriching it on-the-fly. Users can subscribe to continuously query the content/data of their interest and get notified when new content/data becomes available. We also highlight open challenges in realizing a system enabling this concept at scale.
  • Publication
    Open 5G campus networks: key drivers for 6G innovations
    ( 2022)
    Emmelmann, Marc
    ;
    Corici, Marius-Iulian
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    Eichhorn, Fabian
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    Hauswirth, Manfred
    ;
    Magedanz, Thomas
    5G was designed to enable and unify Industrial Internet communication. Emerging 5G campus networks, in particular, provide a flexible communication infrastructure option addressing the specific needs of industry verticals regarding low latency, resilience, security, and operation models. Network Function Virtualization (NFV) and Edge Computing have paved the way for vendor-independent, customized, and scalable network designs for the past decade. Today, Open Radio Access Network (Open RAN) principles extend this architectural thinking toward an innovative and open 5G end-to-end infrastructure. 5G campus networks, in particular, might benefit from this envisaged openness. One key driver for boosting the global interest in private campus networks was the allocation of a dedicated 5G spectrum in Germany in 2019. In addition to permanent spectrum allocations for static campus network deployments, nomadic ad hoc campus network deployments using novel mechanisms, such as dynamic spectrum access and trading, also emerge. Both network types are enabled by the inherent flexibility of combining or disaggregating the desired open 5G RAN and core components in appropriate network deployments. Building upon years of experience in developing and operating 5G network cores and 5G testbeds, the authors provide an overview of the emerging global campus network market, available spectrum options, use cases for nomadic campus network deployments, and the need for open campus networks and open end-to-end technology testbeds. Utilizing the Fraunhofer FOKUS Open5GCore, the 5G Playground testbed, and the 5G+ Nomadic Node as examples, the paper sketches a blueprint for campus networks for international, applied research and development. Ending with an outlook on the evolution of campus networks, namely the transition toward higher spectrums and the integration of non-terrestrial networks, but also the adoption of more agile software principles and the deeper integration of AI/ML technologies for network control and management, it will become obvious that open campus network innovations will pave the way toward 6G.
  • Publication
    A provenance meta learning framework for missing data handling methods selection
    ( 2020)
    Liu, Qian
    ;
    Hauswirth, Manfred
    Missing data is a big problem in many real-world data sets and applications, which can lead to wrong or misleading results of analyses and lower quality and confidence in the results. A large number of missing data handling methods have been proposed in the research community but there exists no universally single best method which can handle all the missing data problems. To select the right method for a specific missing data handling problem, it usually depends on multiple inter-twined factors. To alleviate this methods selection problem, in this paper, we propose a Provenance Meta Learning Framework to simplify this process. We conducted an extensive literature review over 118 missing data handling method survey papers from 2000 to 2019. With this review, we analyse 9 influential factors and 12 selection criteria for missing data handling methods and further perform a detailed analysis of 6 popular missing data handling methods (4 machine learning methods, i.e., KNN Imputation (KNNI), Weighted KNN Imputation (WKNNI), K Means Imputation (KMI), and Fuzzy KMI (FKMI), and 2 ad-hoc methods, i.e., Median/Mode Imputation (MMI) and Group/Class MMI (CMMI)). We focus on missing data handling methods selection for 3 different classification techniques, i.e., C4.5, KNN, and RIPPER. In our evaluations, we adopt 25 real world data sets from KEEL and UCI data sets repositories. Our Provenance Meta Learning Framework suggests that using KNNI to handle missing values when missing data mechanism is Missing Complete At Random (MCAR), missing data pattern is uni-attribute missing data pattern, or monotone missing data pattern, missing data rate is within [1%,5%], number of class labels is 2, sample size is no more than 10'000, since it can keep classification performance better and have higher imputation accuracy and imputation exhaustiveness than all the other 5 missing data handling methods when subsequent classification methods are KNN or RIPPER.
  • Publication
    Autonomous RDF stream processing for IoT edge devices
    ( 2020)
    Nguyen Duc, Manh
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    Le Tuan, Anh
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    Calbimonte, Jean-Paul
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    Hauswirth, Manfred
    ;
    Le-Phuoc, Danh
    The wide adoption of increasingly cheap and computationally powerful single-board computers, has triggered the emergence of new paradigms for collaborative data processing among IoT devices. Motivated by the billions of ARM chips having been shipped as IoT gateways so far, our paper proposes a novel continuous federation approach that uses RDF Stream Processing (RSP) engines as autonomous processing agents. These agents can coordinate their resources to distribute processing pipelines by delegating partial workloads to their peers via subscribing continuous queries. Our empirical study in ""cooperative sensing"" scenarios with resourceful experiments on a cluster of Raspberry Pi nodes shows that the scalability can be significantly improved by adding more autonomous agents to a network of edge devices on demand. The findings open several new interesting follow-up research challenges in enabling semantic interoperability for the edge computing paradigm.
  • Publication
    Incorporating Blockchain into RDF Store at the Lightweight Edge Devices
    ( 2019)
    Le Tuan, Anh
    ;
    Hingu, Darshan
    ;
    Hauswirth, Manfred
    ;
    Le-Phuoc, Danh
    RDF stores provide a simple abstraction for publishing and querying data, that is becoming a norm in data sharing practice. They also empower the decentralised architecture of data publishing for the Web or IoT-driven systems. Such architecture shares a lot in common with blockchain infrastructure and technologies. Therefore, there are emerging interests in marrying RDF stores and blockchain to realise desirable but speculative benefits of blockchain-powered data sharing. This paper presents the first RDF store with blockchain that enables lightweight edge devices to control of the data sharing processes (personal, IoT data). Our novel approach on the deep integration of the storage design for RDF store enables the ability to enforce controlling measures on access methods and auditing policies over data elements via smart contracts before they fetched from the sources to the consumers. Our experiments show that the prototype system delivers an effective performance for a processing load of 1 billion triples on a small network of lightweight nodes which costs less than a commodity PC.