Real-Time Urban Monitoring in Dublin Using Semantic and Stream Technologies

Tallevi-Diotallevi, Simone; Kotoulas, Spyros; Foschini, Luca; Lécué, Freddy; Corradi, Antonio

doi:10.1007/978-3-642-41338-4_12

Cited by 13 publications

(11 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An example is represented by the domain of smart cities that aims to process and understand the information relevant for the life of a city and use it to make the city run better, faster, and cheaper [41,62].…”

Section: The Need For Stream Reasoningmentioning

confidence: 99%

Taming velocity and variety simultaneously in big data with stream reasoning

Valle

Dell’Aglio

Margara

2016

Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems

View full text Add to dashboard Cite

Many "big data" applications must tame velocity (processing data in-motion) and variety (processing many different types of data) simultaneously.The research on knowledge representation and reasoning has focused on the variety of data, devising data representation and processing techniques that promote integration and reasoning on available data to extract implicit information. On the other hand, the event and stream processing community has focused on the velocity of data, producing systems that efficiently operate on streams of data on-the-fly according to pre-deployed processing rules or queries. Several recent works explore the synergy between stream processing and reasoning to fully capture the requirements of modern data intensive applications, thus giving birth to the research domain of stream reasoning.This tutorial paper offers an overview of the theoretical and technological achievements in stream reasoning, highlighting the key benefits and limitations of existing approaches, and discussing the open challenges and the opportunities for future research. The paper mainly targets researchers and practitioners in the area of event and stream processing. The paper aims to stimulate the discussion on stream reasoning and to further promote the integration of reasoning techniques within event and stream processing systems in three ways: (i) by presenting an active research domain, where researchers on event and stream processing can apply their expertise; (ii) by discussing techniques and technologies that can help advancing the state of the art in event and stream processing; (iii) by identifying the open problems in the field of stream reasoning, and drawing attention to promising research directions.

show abstract

Section: The Need For Stream Reasoningmentioning

confidence: 99%

Taming velocity and variety simultaneously in big data with stream reasoning

Valle

Dell’Aglio

Margara

2016

Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems

View full text Add to dashboard Cite

show abstract

“…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.

View full text Add to dashboard Cite

“…Smart Cities represent a specific field where the idea of Semantic Sensor Web can find a concrete application. The final goal of the research in this area is to process and understand the information relevant for the life of a city and use it to make the city run better, faster, and cheaper [12,13].…”

Section: Motivating Scenariosmentioning

confidence: 99%

Streaming the Web: Reasoning Over Dynamic Data

Margara¹,

Urbani²,

Harmelen³

et al. 2014

SSRN Journal

View full text Add to dashboard Cite

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.

show abstract

Real-Time Urban Monitoring in Dublin Using Semantic and Stream Technologies

Cited by 13 publications

References 21 publications

Taming velocity and variety simultaneously in big data with stream reasoning

Taming velocity and variety simultaneously in big data with stream reasoning

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

Streaming the Web: Reasoning Over Dynamic Data

Contact Info

Product

Resources

About