Social Listening of City Scale Events Using the Streaming Linked Data Framework

Balduini, Marco; Valle, Emanuele Della; Dell’Aglio, Daniele; Tsytsarau, Mikalai; Palpanas, Themis; Confalonieri, Cristian

doi:10.1007/978-3-642-41338-4_1

Cited by 48 publications

(28 citation statements)

References 11 publications

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“…Семантические веб-технологии уже доказали [11,12], что они могут стать инструментом для ре-шения проблем синтаксической и семантической интеграции данных в гетерогенных средах и в различ-ных форматах. Resource Description Framework (RDF) и Ontology Web Language (OWL) обеспечивают общую модель и словарь для такого типа данных, а язык SPARQL и механизмы логического вывода по-зволяют динамически формировать специфическую и полностью новую информацию на основе интегри-рованного массива данных.…”

Section: архитектура системы мониторинга энергопотребленияunclassified

Energy consumption monitoring of Smart grid based on semantic stream data analysis

Kolchin¹,

Fensel²,

Mouromtsev³

et al. 2015

Naučno-teh. vestn. inf. tehnol. meh. opt.

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Section: архитектура системы мониторинга энергопотребленияunclassified

Energy consumption monitoring of Smart grid based on semantic stream data analysis

Kolchin¹,

Fensel²,

Mouromtsev³

et al. 2015

Naučno-teh. vestn. inf. tehnol. meh. opt.

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“…Some experiments on the use of social media analysis have been reported in [24], where the authors focus on the processing of posts to Twitter to extract new information (e.g., how the mood changes during the day, which are the trending topics) during some largescale events (London Olympic Games 2012 and Milano Design Week 2013).…”

Section: Motivating Scenariosmentioning

confidence: 99%

Streaming the Web: Reasoning over dynamic data

Margara

Urbani

Harmelen

et al. 2014

Journal of Web Semantics

111

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a b s t r a c tIn the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates.Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation.The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning.

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“…Other approaches have proposed to create RDF datasets fed from unstructured streams [8,16], to lift streaming data as Linked Data [2,10], or to provide virtual RDF Streams [6]. To improve scalability, systems like Ztreamy [7] are designed for efficient transmission of compressed data streams, although they do not address the heterogeneity of data sources, declarative transformation and consumption modes.…”

Section: Introductionmentioning

confidence: 99%

TripleWave: Spreading RDF Streams on the Web

Mauri

Calbimonte

Dell’Aglio

et al. 2016

Lecture Notes in Computer Science

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Abstract. Processing data streams is increasingly gaining momentum, given the need to process these flows of information in real-time and at Web scale. In this context, RDF Stream Processing (RSP) and Stream Reasoning (SR) have emerged as solutions to combine semantic technologies with stream and event processing techniques. Research in these areas has proposed an ecosystem of solutions to query, reason and perform real-time processing over heterogeneous and distributed data streams on the Web. However, so far one basic building block has been missing: a mechanism to disseminate and exchange RDF streams on the Web. In this work we close this gap, proposing TripleWave, a reusable and generic tool that enables the publication of RDF streams on the Web. The features of TripleWave were selected based on requirements of real use-cases, and support a diverse set of scenarios, independent of any specific RSP implementation. TripleWave can be fed with existing Web streams (e.g. Twitter and Wikipedia streams) or time-annotated RDF datasets (e.g. the Linked Sensor Data dataset). It can be invoked through both pull-and push-based mechanisms, thus enabling RSP engines to automatically register and receive data from TripleWave.

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Social Listening of City Scale Events Using the Streaming Linked Data Framework

Cited by 48 publications

References 11 publications

Energy consumption monitoring of Smart grid based on semantic stream data analysis

Energy consumption monitoring of Smart grid based on semantic stream data analysis

Streaming the Web: Reasoning over dynamic data

TripleWave: Spreading RDF Streams on the Web

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