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MergePoint: A Graphical Web-App for merging HTTP-Endpoints and IoT- Platform Models

2020 | HICSS | Citations: 0
Authors: Burzlaff, Fabian; Hammen, Johannes; Bongarth, Benedikt; Grottker, Sven; Bartelt, Christian

Abstract: More and more devices are connected to Internet of Things Platforms in various a ... Expand

Abstract: More and more devices are connected to Internet of Things Platforms in various application domains. The resulting device integration effort is moderated by the concrete integration syntax and the technical abilities of the device integrator. Therefore, researchers from various communities have been investigating and designing component coupling architectures to achieve interoperability for more than 30 years. Emerging Smart Home scenarios challenges classical integration approaches as no single formal integration standard exists. In this paper we introduce a reference architecture called MergePoint that automates HTTPEndpoint integration with smart home platforms such as openHAB in a plug-and-play manner. Based on a prototypical system implementation, our empirical evaluation demonstrates that average integration time can be reduced by 78% and average tool usability score is increased by 65% compared to textual integration approaches. MergePoint can serve as a reference implementation for practitioners that want to automate the integration between HTTP-Endpoints and IoT Platform Models. Collapse

Semantic filters: OrientDB

Topics: usability smart home internet of things mobile system application programming interface

Methods: data modeling design science survey case study cognitive walkthrough
Efficient querying of multidimensional RDF data with aggregates: Comparing NoSQL, RDF and relational data stores
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2020 | International Journal of Information Management | Citations: 0
Authors: Ravat, Franck; Song, Jiefu; Teste, Olivier; Trojahn, Cassia

Abstract: This paper proposes an approach to tackle the problem of querying large volume o ... Expand

Abstract: This paper proposes an approach to tackle the problem of querying large volume of statistical RDF data. Our approach relies on pre-aggregation strategies to better manage the analysis of this kind of data. Specifically, we define a conceptual model to represent original RDF data with aggregates in a multidimensional structure. A set of translations rules for converting a well-known multidimensional RDF modelling vocabulary into the proposed conceptual model is then proposed. We implement the conceptual model in six different data stores: two RDF triple stores (Jena TDB and Virtuoso), one graph-oriented NoSQL database (Neo4j), one column-oriented data store (Cassandra), and two relational databases (MySQL and PostGreSQL). We compare the querying performance, with and without aggregates, in these data stores. Experimental results, on real-world datasets containing 81.92 million triplets, show that pre-aggregation allows for reducing query runtime in all data stores. Neo4j NoSQL and relational databases with aggregates outperform triple stores speeding up to 99% query runtime. Collapse

Semantic filters: OrientDB

Topics: Neo4j Cassandra relational database nosql database MySQL

Methods: experiment conceptual modelling computational algorithm multidimensional modeling optimization model

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MergePoint: A Graphical Web-App for merging HTTP-Endpoints and IoT- Platform Models

Efficient querying of multidimensional RDF data with aggregates: Comparing NoSQL, RDF and relational data stores