Prof. Dr.

Hauswirth, Manfred

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Interoperable education infrastructures: A middleware that brings together adaptive, social and virtual learning technologies

2020 , Krauss, Christopher , Hauswirth, Manfred

What should a course provider do if all course content, which is stored in Moodle, needs to be migrated to a new learning management system? How could a provider easily use advanced technologies like learning analytics, learning recommender systems or virtual learning to create a compelling learning experience? How can a provider incorporate the content of another provider into an existing course? To address such questions, we developed the Common Learning Middleware in a joint project with several Fraunhofer institutes trying to solve these typical challenges facing educational institutions.

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RDF data storage and query processing schemes

2018 , Wylot, Marcin , Hauswirth, Manfred , Cudré-Mauroux, Philippe , Sakr, Sharif

The Resource Description Framework (RDF) represents a main ingredient and data representation format for Linked Data and the Semantic Web. It supports a generic graph-based data model and data representation format for describing things, including their relationships with other things. As the size of RDF datasets is growing fast, RDF data management systems must be able to cope with growing amounts of data. Even though physically handling RDF data using a relational table is possible, querying a giant triple table becomes very expensive because of the multiple nested joins required for answering graph queries. In addition, the heterogeneity of RDF Data poses entirely new challenges to database systems. This article provides a comprehensive study of the state of the art in handling and querying RDF data. In particular, we focus on data storage techniques, indexing strategies, and query execution mechanisms. Moreover, we provide a classification of existing systems and approaches. We also provide an overview of the various benchmarking efforts in this context and discuss some of the open problems in this domain.

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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.

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Design principles for utility-driven services and cloud-based computing modelling for the Internet of Things

2014 , Soldatos, John , Kefalakis, Nikos , Serrano, Martin , Hauswirth, Manfred

By following an analysis of the state of the art in the convergence of cloud computing and the Internet of Things (IoT), this paper presents design principles for the IoT in cloud environments. A framework for on-demand establishment of IoT services based on the automated formulation of societies of internet-connected objects is described and the interactions between architecture modules are explained in detail to validate this approach. The main building blocks of the functional framework and its operational components follow the utility-driven cloud-based computing model. The framework leverages well-known technologies (i.e. linked sensor data) and standards (notably the W3C semantic sensor networks ontology). Finally, an example for service formulation and delivery of services for a smart campus scenario is provided and discussed. This paper also introduces some experiment results using the utility-driven service formulation model for mobile applications.

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Provenance management over linked data streams

2019 , Liu, Qian , Wylot, Marcin , Le Phuoc, Danh , Hauswirth, Manfred

Provenance describes how results are produced starting from data sources, curation, recovery, intermediate processing, to the final results. Provenance has been applied to solve many problems and in particular to understand how errors are propagated in large-scale environments such as Internet of Things, Smart Cities. In fact, in such environments operations on data are often performed by multiple uncoordinated parties, each potentially introducing or propagating errors. These errors cause uncertainty of the overall data analytics process that is further amplified when many data sources are combined and errors get propagated across multiple parties. The ability to properly identify how such errors influence the results is crucial to assess the quality of the results. This problem becomes even more challenging in the case of Linked Data Streams, where data is dynamic and often incomplete. In this paper, we introduce methods to compute provenance over Linked Data Streams. More specifically, we propose provenance management techniques to compute provenance of continuous queries executed over complete Linked Data streams. Unlike traditional provenance management techniques, which are applied on static data, we focus strictly on the dynamicity and heterogeneity of Linked Data streams. Specifically, in this paper we describe: i) means to deliver a dynamic provenance trace of the results to the user, ii) a system capable to execute queries over dynamic Linked Data and compute provenance of these queries, and iii) an empirical evaluation of our approach using real-world datasets.

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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.

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