Prof. Dr.

Hauswirth, Manfred

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  • Publication
    Pushing the scalability of RDF engines on IoT edge devices
    ( 2020)
    Le Tuan, Anh
    ;
    Hayes, Conor
    ;
    Hauswirth, Manfred
    ;
    Le-Phuoc, Danh
    Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. In particular, RDF4Led requires 10%-30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J.
  • Publication
    Autonomous RDF stream processing for IoT edge devices
    ( 2020)
    Nguyen Duc, Manh
    ;
    Le Tuan, Anh
    ;
    Calbimonte, Jean-Paul
    ;
    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.
  • Publication
  • Publication
    Resource optimisation in IoT cloud systems by using matchmaking and self-management principles
    ( 2013)
    Serrano, Martin
    ;
    Le-Phuoc, Danh
    ;
    Zaremba, Maciej
    ;
    Galis, Alex
    ;
    Bhiri, Sami
    ;
    Hauswirth, Manfred
    IoT Cloud systems provide scalable capacity and dynamic behaviour control of virtual infrastructures for running applications, services and processes. Key aspects in this type of complex systems are the resource optimisation and the performance of dynamic management based on distributed user data metrics and/or IoT application data demands and/or resource utilisation metrics. In this paper we particularly focus on Cloud management perspective - integrating IoT Cloud service data management - based on annotated data of monitored Cloud performance and user profiles (matchmaking) and enabling management systems to use shared infrastructures and resources to enable efficient deployment of IoT services and applications. We illustrate a Cloud service management approach based on matchmaking operations and self-management principles which enable improved distribution and management of IoT services across different Cloud vendors and use the results from the analysis as mechanism to control applications and services deployment in Cloud systems. For our IoT Cloud data management solution we utilize performance metrics expressed with linked data in order to integrate monitored performance data and end user profile information (via linked data relations).
  • Publication
    The SSN ontology of the W3C semantic sensor network incubator group
    ( 2012)
    Compton, Michael
    ;
    Barnaghi, Payam
    ;
    Bermudez, Luis
    ;
    García-Castro, Raúl
    ;
    Corcho, Oscar
    ;
    Cox, Simon
    ;
    Graybeal, John
    ;
    Hauswirth, Manfred
    ;
    Henson, Cory
    ;
    Herzog, Arthur
    ;
    Huang, Vincent
    ;
    Janowicz, Krzysztof
    ;
    Kelsey, W.D.
    ;
    Le-Phuoc, Danh
    ;
    Lefort, Laurent
    ;
    Leggieri, Myriam
    ;
    Neuhaus, Holger
    ;
    Nikolov, Andriy
    ;
    Page, Kevin
    ;
    Passant, Alexandre
    ;
    Sheth, Amit
    ;
    Taylor, Kerry
    The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations - the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.
  • Publication
    Linked stream data processing
    ( 2012)
    Le-Phuoc, Danh
    ;
    Parreira, Josiane Xavier
    ;
    Hauswirth, Manfred
    Linked Stream Data has emerged as an effort to represent dynamic, time-dependent data streams following the principles of Linked Data. Given the increasing number of available stream data sources like sensors and social network services, Linked Stream Data allows an easy and seamless integration, not only among heterogenous stream data, but also between streams and Linked Data collections, enabling a new range of real-time applications. This tutorial gives an overview about Linked Stream Data processing. It describes the basic requirements for the processing, highlighting the challenges that are faced, such as managing the temporal aspects and memory overflow. It presents the different architectures for Linked Stream Data processing engines, their advantages and disadvantages. The tutorial also reviews the state of the art Linked Stream Data processing systems, and provide a comparison among them regarding the design choices and overall performance. A short discussion of the current challenges in open problems is given at the end.
  • Publication
    A middleware framework for scalable management of linked streams
    ( 2012)
    Le-Phuoc, Danh
    ;
    Nguyen Mau Quoc, Hoan
    ;
    Parreira, Josiane Xavier
    ;
    Hauswirth, Manfred
    The Web has long exceeded its original purpose of a distributed hypertext system and has become a global, data sharing and processing platform. This development is confirmed by remarkable milestones such as the Semantic Web, Web services, social networks and mashups. In parallel with these developments on the Web, the Internet of Things (IoT), i.e., sensors and actuators, has matured and has become a major scientific and economic driver. Its potential impact cannot be overestimated-for example, in logistics, cities, electricity grids and in our daily life, in the form of sensor-laden mobile phones-and rivals that of the Web itself. While the Web provides ease of use of distributed resources and a sophisticated development and deployment infrastructure, the IoT excels in bringing real-time information from the physical world into the picture. Thus a combination of these players seems to be the natural next step in the development of even more sophisticated systems of systems. While only starting, there is already a significant amount of sensor-generated, or more generally dynamic information, available on the Web. However, this information is not easy to access and process, depends on specialised gateways and requires significant knowledge on the concrete deployments, for example, resource constraints and access protocols. To remedy these problems and draw on the advantages of both sides, we try to make dynamic, online sensor data of any form as easily accessible as resources and data on the Web, by applying well-established Web principles, access and processing methods, thus shielding users and developers from the underlying complexities. In this paper we describe our Linked Stream Middleware (LSM, http://lsm.deri.ie/), which makes it easy to integrate time-dependent data with other Linked Data sources, by enriching both sensor sources and sensor data streams with semantic descriptions, and enabling complex SPARQL-like queries across both dataset types through a novel query processing engine, along with means to mashup the data and process results. Most prominently, LSM provides (1) extensible means for real-time data collection and publishing using a cloud-based infrastructure, (2) a Web interface for data annotation and visualisation, and (3) a SPARQL endpoint for querying unified Linked Stream Data and Linked Data. We describe the system architecture behind LSM, provide details of how Linked Stream Data is generated, and demonstrate the benefits and efficiency of the platform by showcasing some experimental evaluations and the system's interface.