Prof. Dr.

Hauswirth, Manfred

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Now showing 1 - 10 of 11
  • Publication
    A decentralised persistent identification layer for DCAT datasets
    ( 2023)
    Kirstein, Fabian
    ;
    Altenbernd, Anton
    ;
    Schimmler, Sonja
    ;
    Hauswirth, Manfred
    The Data Catalogue Vocabulary (DCAT) standard is a popular RDF vocabulary for publishing metadata about data catalogs and a valuable foundation for creating Knowledge Graphs. It has widespread application in the (Linked) Open Data and scientific communities. However, DCAT does not specify a robust mechanism to create and maintain persistent identifiers for the datasets. It relies on Internationalized Resource Identifiers (IRIs), that are not necessarily unique, resolvable and persistent. This impedes findability, citation abilities, and traceability of derived and aggregated data artifacts. As a remedy, we propose a decentralized identifier registry where persistent identifiers are managed by a set of collaborative distributed nodes. Every node gives full access to all identifiers, since an unambiguous state is shared across all nodes. This facilitates a common view on the identifiers without the need for a (virtually) centralized directory. To support this architecture, we propose a data model and network methodology based on a distributed ledger and the W3C recommendation for Decentralized Identifiers (DID). We implemented our approach as a working prototype on a five-peer test network based on Hyperledger Fabric.
  • 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
    ;
    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
    Uncomputation in the Qrisp High-Level Quantum Programming Framework
    ( 2023)
    Seidel, Raphael
    ;
    Tcholtchev, Nikolay Vassilev
    ;
    Bock, Sebastian
    ;
    Hauswirth, Manfred
    Uncomputation is an essential part of reversible computing and plays a vital role in quantum computing. Using this technique, memory resources can be safely deallocated without performing a non-reversible deletion process. For the case of quantum computing, several algorithms depend on this as they require disentangled states in the course of their execution. Thus, uncomputation is not only about resource management, but is also required from an algorithmic point of view. However, synthesizing uncomputation circuits is tedious and can be automated. In this paper, we describe the interface for automated generation of uncomputation circuits in our Qrisp framework. Our algorithm for synthesizing uncomputation circuits in Qrisp is based on an improved version of “Unqomp”, a solution presented by Paradis et al. Our paper also presents some improvements to the original algorithm, in order to make it suitable for the needs of a high-level programming framework. Qrisp itself is a fully compilable, high-level programming language/framework for gate-based quantum computers, which abstracts from many of the underlying hardware details. Qrisp’s goal is to support a high-level programming paradigm as known from classical software development.
  • Publication
    Automatic generation of Grover quantum oracles for arbitrary data structures
    ( 2023)
    Seidel, Raphael
    ;
    Becker, Colin Kai-Uwe
    ;
    Bock, Sebastian
    ;
    Tcholtchev, Nikolay Vassilev
    ;
    Gheorghe-Pop, Ilie-Daniel
    ;
    Hauswirth, Manfred
    The steadily growing research interest in quantum computing-together with the accompanying technological advances in the realization of quantum hardware-fuels the development of meaningful real-world applications, as well as implementations for well-known quantum algorithms. One of the most prominent examples till today is Grover’s algorithm, which can be used for efficient search in unstructured databases. Quantum oracles that are frequently masked as black boxes play an important role in Grover’s algorithm. Hence, the automatic generation of oracles is of paramount importance. Moreover, the automatic generation of the corresponding circuits for a Grover quantum oracle is deeply linked to the synthesis of reversible quantum logic, which-despite numerous advances in the field-still remains a challenge till today in terms of synthesizing efficient and scalable circuits for complex Boolean functions. In this paper, we present a flexible method for automatically encoding unstructured databases into oracles, which can then be efficiently searched with Grover’s algorithm. Furthermore, we develop a tailor-made method for quantum logic synthesis, which vastly improves circuit complexity over other current approaches. Finally, we present another logic synthesis method that considers the requirements of scaling onto real world backends. We compare our method with other approaches through evaluating the oracle generation for random databases and analyzing the resulting circuit complexities using various metrics.
  • Publication
    SemRob: Towards semantic stream reasoning for robotic operating systems
    ( 2022)
    Nguyen-Duc, Manh
    ;
    Le-Tuan, Anh
    ;
    Hauswirth, Manfred
    ;
    Bowden, David
    ;
    Phuoc, Danh Le
    Stream processing and reasoning is getting considerable attention in various application domains such as IoT, Industry IoT and Smart Cities. In parallel, reasoning and knowledge-based features have attracted research into many areas of robotics, such as robotic mapping, perception and interaction. To this end, the Semantic Stream Reasoning (SSR) framework can unify the representations of symbolic/semantic streams with deep neural networks, to integrate high-dimensional data streams, such as video streams and LiDAR point clouds, with traditional graph or relational stream data. As such, this positioning and system paper will outline our approach to build a platform to facilitate semantic stream reasoning capabilities on a robotic operating system called SemRob.
  • Publication
    Qrisp: a framework for compilable high-level programming of gate-based quantum computers
    ( 2022)
    Seidel, Raphael
    ;
    Bock, Sebastian
    ;
    Tcholtchev, Nikolay Vassilev
    ;
    Hauswirth, Manfred
    The recent advances of quantum computation hardware spark realistic hopes to achieve commercially relevant quantum advantage in less than a decade. While the physics side of quantum computing makes significant progress, the support for high-level quantum programming abstractions is still in its infancy compared to modern classical languages and frameworks. In this article we present Qrisp, a framework which aims to bridge several of the existing gaps between the abstract high-level programming paradigms of state-of-the art software engineering and the physical reality of today's quantum hardware. The goal of the framework is to provide a uniform high-level programming interface, abstraction and low-level backend interface for different hardware platforms. We specify a simple and expressive syntax which nevertheless compiles to efficient circuits. Compared to many other high-level language approaches, Qrisps most outstanding feature is that it's programs are compiled to the circuit level and can thus be executed on most of today's physical backends.
  • Publication
    CQELS 2.0: Towards a unified framework for semantic stream fusion
    ( 2022)
    Le-Tuan, Anh
    ;
    Nguyen-Duc, Manh
    ;
    Le, Chien-Quang
    ;
    Tran, Trung-Kien
    ;
    Hauswirth, Manfred
    ;
    Eiter, Thomas
    ;
    Phuoc, Danh Le
    We present CQELS 2.0, the second version of Continuous Query Evaluation over Linked Streams. CQELS 2.0 is a platform-agnostic federated execution framework towards semantic stream fusion. In this version, we introduce a novel neuralsymbolic stream reasoning component that enables specifying deep neural network (DNN) based data fusion pipelines via logic rules with learnable probabilistic degrees as weights. As a platform-agnostic framework, CQELS 2.0 can be implemented for devices with different hardware architectures (from embedded devices to cloud infrastructures). Moreover, this version also includes an adaptive federator that allows CQELS instances on different nodes in a network to coordinate their resources to distribute processing pipelines by delegating partial workloads to their peers via subscribing continuous queries.
  • Publication
    Beyond the Hype: Why Do Data-Driven Projects Fail?
    ( 2021)
    Ermakova, Tatiana
    ;
    Blume, Julia
    ;
    Fabian, Benjamin
    ;
    Fomenko, Elena
    ;
    Berlin, Marcus
    ;
    Hauswirth, Manfred
    Despite substantial investments, data science has failed to deliver significant business value in many companies. So far, the reasons for this problem have not been explored systematically. This study tries to find possible explanations for this shortcoming and analyses the specific challenges in data-driven projects. To identify the reasons that make data-driven projects fall short of expectations, multiple rounds of qualitative semi-structured interviews with domain experts with different roles in data-driven projects were carried out. This was followed by a questionnaire surveying 112 experts with experience in data projects from eleven industries. Our results show that the main reasons for failure in data-driven projects are (1) the lack of understanding of the business context and user needs, (2) low data quality, and (3) data access problems. It is interesting, that 54% of respondents see a conceptual gap between business strategies and the implementation of analytics solutions. Based on our results, we give recommendations for how to overcome this conceptual distance and carrying out data-driven projects more successfully in the future.
  • Publication
    Quantum DevOps: Towards reliable and applicable NISQ Quantum Computing
    ( 2020)
    Gheorghe-Pop, Ilie-Daniel
    ;
    Tcholtchev, Nikolay
    ;
    Ritter, Tom
    ;
    Hauswirth, Manfred
    Quantum Computing is emerging as one of the great hopes for boosting current computational resources and enabling the application of ICT for optimizing processes and solving complex and challenging domain specific problems. However, the Quantum Computing technology has not matured to a level where it can provide a clear advantage over high performance computing yet. Towards achieving this "quantum advantage", a larger number of Qubits is required, leading inevitably to a more complex topology of the computing Qubits. This raises additional difficulties with decoherence times and implies higher Qubit error rates. Nevertheless, the current Noisy Intermediate-Scale Quantum (NISQ) computers can prove useful despite the intrinsic uncertainties on the quantum hardware layer. In order to utilize such error-prone computing resources, various concepts are required to address Qubit errors and to deliver successful computations. In this paper describe and motivate the need for the novel concept of Quantum DevOps. which entails regular checking of the reliability of NISQ Quantum Computing (QC) instances. By means of testing the computational reliability of basic quantum gates and computations (C-NOT, Hadamard, etc.)it consequently estimates the likelihood for a large scale critical computation (e.g. calculating hourly traffic flow models for a city) to provide results of sufficient quality. Following this approach to select the best matching (cloud) QC instance and having it integrated directly with the processes of development, testing and finally the operations of quantum based algorithms and systems enables the Quantum DevOps concept.
  • Publication
    Linked data in the European Data Portal
    ( 2019)
    Kirstein, Fabian
    ;
    Dittwald, Benjamin
    ;
    Dutkowski, Simon
    ;
    Glikman, Yury
    ;
    Schimmler, Sonja
    ;
    Hauswirth, Manfred
    The European Data Portal (EDP) is a central access point for metadata of Open Data published by public authorities in Europe and acquires data from more than 70 national data providers. The platform is a starting point in adopting the Linked Data specification DCAT-AP, aiming to increase interoperability and accessibility of Open Data. In this paper, we present the design of the central data management components of the platform, responsible for metadata storage, data harvesting and quality assessment. The core component is based on CKAN, which is extended by the support for native Linked Data replication to a triplestore to ensure legacy compatibility and the support for DCAT-AP. Regular data harvesting and the creation of detailed quality reports are performed by custom components adressing the requirements of DCAT-AP. The EDP is well on track to become the core platform for European Open Data and fostered the acceptance of DCAT-AP. Our platform is available here: https://www.europeandataportal.eu