The data on the web are continuously evolving. The popularity of social media, internet of things, and news feeds show that streaming data reached the web scale. Although the web is open to innovative changes, the different nature of streaming data calls for a new methodological and technological toolbox. In particular, to foster interoperability, there is a need for machine-readable stream descriptions. As for the case of Linked Data, we can achieve this by using semantic technologies. However, existing solutions do not properly address the challenge implied by streaming data. therefore, in this master thesis, I investigate the problem of modeling streaming data as linked data. To this extent, I proposed two vocabularies, i.e., VoIS and VoCaLS. The former - which stands for Vocabulary of Interlinked Streams - builds on existing Linked Data Vocabularies to enable stream descriptions on the web. The latter - which stands for Vocabulary & Catalog of Linked Streams - improves and extends VoIS, to include modules to describe streaming services and track the provenance of stream processing. Last but not least, I present MobileWave, i.e., a framework that allows creating, composing and publishing RDF streams from smart-phones using the proposed vocabularies.
I dati sul web sono in continua evoluzione. La popolarita dei social media, dell'internet of things e dei new-feed mostra come gli stream abbiano raggiunto una scala web. Nonostante il web sia un ambiente dinamico che accoglie innovazioni, la natura anomala degli stream richiede un approccio nuovo. In particolare, sussiste il bisogno di fornire descrizioni machine-readable. Come per i Linke Data, le tecnologie semantiche sono uno strumento adatto a questo scopo. tuttavia le soluzioni esistenti sono inadatte a rappresentare gli stream. Per questo motivo ho investigato in questa tesi il problema di modellare stream come Linked Data. A questo proposito, ho proposto due vocabulari: VoIS e VoCaLS. Il primo - il cui nome esteso é Vocabulary of Interlinked Streams - fornisce la terminologia per descrivere stream nel web, partendo da vocabolari esistenti per Linked Data. Il secondo -il cui nome esteso é Vocabulary & Catalog of Linked Streams - estende e migliora VoIS con moduli aggiuntivi che permetto di descrivere streaming service e di tracciare la provenance di trasformazioni che coinvolgono stream. Infine, ho sviluppato MobileWave, un framework che permette la reazione, la composizione e la pubblicazione di RDF stream sul web, partendo dai dati generati dagli smart-phone e usando i vocabolari proposti.
Towards streaming data on the Web : vocabularies, catalogs and applications
ABOSEDIRA, YEHIA MOHAMED ALI AHMED
2017/2018
Abstract
The data on the web are continuously evolving. The popularity of social media, internet of things, and news feeds show that streaming data reached the web scale. Although the web is open to innovative changes, the different nature of streaming data calls for a new methodological and technological toolbox. In particular, to foster interoperability, there is a need for machine-readable stream descriptions. As for the case of Linked Data, we can achieve this by using semantic technologies. However, existing solutions do not properly address the challenge implied by streaming data. therefore, in this master thesis, I investigate the problem of modeling streaming data as linked data. To this extent, I proposed two vocabularies, i.e., VoIS and VoCaLS. The former - which stands for Vocabulary of Interlinked Streams - builds on existing Linked Data Vocabularies to enable stream descriptions on the web. The latter - which stands for Vocabulary & Catalog of Linked Streams - improves and extends VoIS, to include modules to describe streaming services and track the provenance of stream processing. Last but not least, I present MobileWave, i.e., a framework that allows creating, composing and publishing RDF streams from smart-phones using the proposed vocabularies.File | Dimensione | Formato | |
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https://hdl.handle.net/10589/142108