Heyvaert, Pieter

Mannens, Erik

Taelman, Ruben

Verborgh, Ruben

Ghent University Academic Bibliography

Querying Dynamic Datasourceswith Continuously Mapped Sensor Data?Ruben Taelman, Pieter Heyvaert,Ruben Verborgh, and Erik MannensData Science Lab (Ghent University - iMinds)firstname.lastname@ugent.beAbstract. The world contains a large amount of sensors that produce new data ata high frequency. It is currently very hard to find public services that expose thesemeasurements as dynamic Linked Data. We investigate how sensor data can bepublished continuously on the Web at a low cost. This paper describes how thepublication of various sensor data sources can be done by continuously mappingraw sensor data to rdf and inserting it into a live, low-cost server. This makes itpossible for clients to continuously evaluate dynamic queries using public sensordata. For our demonstration, we will illustrate how this pipeline works for thepublication of temperature and humidity data originating from a microcontroller,and how it can be queried.Keywords: Linked Data, Linked Data Fragments, rml, sparql, dynamic data1 IntroductionCountless sensors that are connected to the Internet produce various streams of continuousdata. Even though there are many sensors, no public interfaces are available which thesemeasurements can be consumed as dynamic Linked Data [2], which is essential whenthese measurements need to be linked to related information.Dynamic Linked Data can be published through rdf Stream Processing (rsp) queryengines, like c-sparql [1] and cqels [9]. These rsp query engines are used to publish thissensor data using an extended sparql [7] endpoint that enables continuous querying overthis data. These engines require expensive machines to host such data on theWeb, becausequeries can be complex, are continuous and originate from an unlimited number ofclients. These expensive solutions limit the development of sensor-based applications [4].In this work, we present a continuous pipeline for publishing dynamic sensor datausing a low-cost Triple Pattern Fragments (tpf) [11] server. This publication makes itpossible for evaluating queries continuously at the client using a tpf Query Streamer [10].In order to demonstrate this pipeline, we publish and query the continuous data from atemperature and humidity sensor. In the demonstration, we will show the different stepsconducted on the data, starting from the raw measurements until the final query results.? The described research activities were funded by Ghent University, iMinds, the Institute for thePromotion of Innovation by Science and Technology in Flanders (IWT), the Fund for ScientificResearch Flanders (FWO Flanders), and the European Union.2 ArchitectureOur continuous pipeline for publishing sensor data consists of a data reader, mapper andpublisher. These components connect a sensor with a dynamic data source, as can be seenin Fig. 1. The reader receives data from a sensor. The mapper converts this data to rdfusing the rdfMapping Language (rml) [6]. Finally, the publisher inserts the dynamicrdf triples into a tpf server. These three components will be explained hereafter.Sensor Reader Mapper Publisher Data sourceFig. 1: Overview of the continuous pipeline for publishing sensor data.2.1 ReaderFor this demonstration, we use a Tessel1 microcontroller to capture sensor data fromthe environment. Tessel provides a Node.js2 module that can be used to listen to sensormeasurements from the device using JavaScript events. json objects are emitted for eachmeasurement at a configurable rate. These objects contain all required data to describeeach measurement.2.2 MapperThe mapper component [8] is responsible for converting the json output that it receivesfrom the reader component to rdf. This conversion is done using the rmlmapper3. Foreach measurement, the rmlmapper reads the rml mapping file, maps the json object tordf and forwards the result to the publisher component. The mapping file depends on thetype of sensor that is being used, because not all sensors output the same data in the samestructure and format and the data is not necessarily annotated using the same ontologies.2.3 PublisherFor the final step in the continuous pipeline, the publisher component takes the measure-ment data in rdf and converts it to dynamic data. The publisher does this by addinga time annotation to it, as we described in previous work [10]. We chose to representour time annotations as expiration times and we only keep the latest version of eachmeasurement, because for this demonstration only the latest sensor measurements areneeded. Our system alternatively also supports storing all measurement versions, whichenables historical analysis. We serialized our annotations as rdf graphs [5], becausewe showed that this is the most efficient method for time annotation [10]. Finally, eachtime-annotated measurement is inserted into the tpf server and the previous measurementfor that sensor is removed.1 https://tessel.io/2 https://nodejs.org/3 https://github.com/RMLio/RML-Mapper3 Demonstration OverviewWe used our pipeline to publish data that is being created by a Tessel sensor that readstemperature and humidity data. The reader component starts by emitting measurementsin json, like the following.{ "humidity" : { "value":47.0815124512 },"device" : "TM-00-04-f000da30-0062473d-20a82586","module" : "climate","temperature": { "value":30.0167749023 } }In order to add semantical value to the measurements, the mapper component maps eachjson object to rdf.<http://www.example.com/TM-00-04-f000da30-0062473d-20a82586>a <https://w3id.org/saref#Sensor> ;<http://vocab.datex.org/terms#humidity> "47.0815124512"^^xsd:double ;<http://dbpedia.org/ontology/temperature> "30.0167749023"^^xsd:double .Next, the triples are time-annotated by the publisher component to make the triples validup until a certain time. This is represented with the TriG [3] serialization._:g12 {<http://www.example.com/TM-00-04-f000da30-0062473d-20a82586>a <https://w3id.org/saref#Sensor> ;<http://vocab.datex.org/terms#humidity> "47.0815124512"^^xsd:double ;<http://dbpedia.org/ontology/temperature> "30.0167749023"^^xsd:double .}_:g12 <http://example.org/temporal#expiration> "2016-06-27T09:48:47.808Z"^^<xsd:dateTimeStamp> .After the data is published, it can be consumed by clients. We use the client-side tpfQuery Streamer to continuously evaluate the following sparql query in order to fetchthe published measurements from our pipeline.PREFIX dat: <http://vocab.datex.org/terms#>PREFIX saref: <https://w3id.org/saref#>PREFIX dbp: <http://dbpedia.org/ontology/>SELECT ?temperature ?humidity WHERE {?sensor a saref:Sensor;dat:humidity ?humidity;dbp:temperature ?temperature.}This query selects the temperature and humidity data from all sensors. The tpf QueryStreamer is able to interpret this query as being dynamic, so it will automaticallyre-evaluate the query from the moment new measurements become available. The querycan for example show the following intermediary result.Temperature: "30.0167749023" Humidity: "47.0815124512"These continuously updating query results could, instead of printing it as text, be used asinput to various types of applications.During the demo, we will show the different steps conducted on temperature andhumidity data measurements by the different components of the pipeline and the queryresults. These steps will be shown as continuously updating log outputs for eachcomponent. The Tessel microcontroller will be part of the demonstration, this will makeit possible for users to modify the temperature or humidity and immediately see thechanges in the query output. We recorded a screencast4 containing these live logs fortemperature and humidity data. The source code of our pipeline prototype can be foundat https://github.com/mmlab/demo-tessel-continuous-datasource.The demonstration illustrates the simplicity of hosting any kind of sensor data usingthis technique. For future work, this solution can be improved and extended to supportall kinds of sensors and sensor streams. The pipeline could be extended to supportcomposing different pipelines and streams in order to create more complex dynamic data.With this demonstration we aim to show that publishing dynamic Linked Data doesnot have to be complex or costly. We hope to trigger more efforts for making dynamic datapublicly available, in order to promote the development of more dynamic applications.References1. Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: Querying rdf streams withc-sparql. SIGMOD Rec. 39(1), 20–26 (Sep 2010)2. Berners-Lee, T.: Linked Data (July 2006), http://www.w3.org/DesignIssues/LinkedData.html3. Bizer, C., Cyganiak, R.: rdf 1.1 TriG. Rec., W3C (Feb 2014), https://www.w3.org/TR/trig/4. Corcho, O., García-Castro, R.: Five challenges for the Semantic Sensor Web. Semantic Web1(1, 2), 121–125 (2010)5. Cyganiak, R., Wood, D., Lanthaler, M.: rdf 1.1: Concepts and abstract syntax. Recommenda-tion, W3C (Feb 2014), http://www.w3.org/TR/2014/REC-rdf11-concepts-20140225/6. Dimou, A., Vander Sande, M., Colpaert, P., Verborgh, R., Mannens, E., Van de Walle, R.: rml:A generic language for integrated rdf mappings of heterogeneous data. In: LDOW (2014)7. Feigenbaum, L., Todd Williams, G., Grant Clark, K., Torres, E.: sparql 1.1 protocol. Rec.,W3C (Mar 2013), http://www.w3.org/TR/2013/REC-sparql11-protocol-20130321/8. Heyvaert, P., Taelman, R., Verborgh, R., Mannens, E.: Linked Sensor Data generation usingqueryable rmlmappings. In: Proceedings of the 2016 International Semantic Web ConferencePosters & Demonstrations Track (2016), accepted for publication9. Le-Phuoc, D., Dao-Tran, M., Parreira, J.X., Hauswirth, M.: A native and adaptive approachfor unified processing of linked streams and Linked Data. In: The Semantic Web–ISWC 2011,pp. 370–388 (2011)10. Taelman, R., Verborgh, R., Colpaert, P., Mannens, E., Van deWalle, R.: Continuously updatingquery results over real-time Linked Data. In: Proceedings of the 2nd Workshop on Managingthe Evolution and Preservation of the Data Web (May 2016)11. Verborgh, R., Vander Sande, M., Hartig, O., Van Herwegen, J., De Vocht, L., De Meester,B., Haesendonck, G., Colpaert, P.: Triple Pattern Fragments: a low-cost knowledge graphinterface for the Web. Journal of Web Semantics 37–38, 184–206 (2016)4 https://vimeo.com/172409187

Querying dynamic datasources with continuously mapped sensor data

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Querying dynamic datasources with continuously mapped sensor data

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