Stream reasoning: A survey and outlook

Dell’Aglio, Daniele; Valle, Emanuele Della; Harmelen, Frank van; Bernstein, Abraham

doi:10.3233/ds-170006

Cited by 105 publications

(80 citation statements)

References 102 publications

(110 reference statements)

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“…By using the Linked Data approach [11], the semantic IoT data can then easily be linked to such domain knowledge and resources described by these and other models. Semantic reasoners, e.g., FaCT++ [12], Hermit [13], Pellet [14] and RDFox [15], have been designed to interpret this semantic interconnected data in order to derive useful knowledge [16], i.e., additional new implicit knowledge that can be useful for applications. For example, in the case of the concussion patient, a semantic reasoner can automatically derive that the patient is sensitive to light and sound, and that light and sound sensors in the patient's room should be monitored.…”

Section: Introductionmentioning

confidence: 99%

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Towards a Cascading Reasoning Framework to Support Responsive Ambient-Intelligent Healthcare Interventions

Brouwer¹,

Ongenae²,

Bonte³

et al. 2018

Preprint

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In hospitals and smart nursing homes, ambient-intelligent care rooms are equipped with many sensors. They can monitor environmental and body parameters, and detect wearable devices of patients and nurses. Hence, they continuously produce data streams. This offers the opportunity to collect, integrate and interpret this data in a context-aware manner, with a focus on reactivity and autonomy. However, doing this in real-time on huge data streams is a challenging task. In this context, cascading reasoning is an emerging research approach that exploits the trade-off between reasoning complexity and data velocity by constructing a processing hierarchy of reasoners. Therefore, a cascading reasoning framework is proposed in this paper. A generic architecture is presented allowing to create a pipeline of reasoning components hosted locally, in the edge of the network, and in the cloud. The architecture is implemented on a pervasive health use case, where medically diagnosed patients are constantly monitored, and alarming situations can be detected and reacted upon in a context-aware manner. A performance evaluation shows that the total system latency is mostly lower than 5 seconds, allowing for responsive intervention by a nurse in alarming situations. Using the evaluation results, the benefits of cascading reasoning for healthcare are analyzed.

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Section: Introductionmentioning

confidence: 99%

“…Semantic reasoning over large or complex ontologies is computationally intensive and slow. Hence, it cannot keep up with the velocity of large data streams generated in healthcare to derive real-time knowledge [16]. However, in healthcare, making decisions often is time-critical.…”

Section: Introductionmentioning

confidence: 99%

Towards a Cascading Reasoning Framework to Support Responsive Ambient-Intelligent Healthcare Interventions

Brouwer¹,

Ongenae²,

Bonte³

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…rdf Stream Processing (rsp) [1] defines a framework for continuous query answering over data streams. rsp engines can take into account one or more rdf streams to answer queries which results will be computed at several time instants to consider new available data on the streams.…”

Section: Related Workmentioning

confidence: 99%

Supporting Sustainable Publishing and Consuming of Live Linked Time Series Streams

Melendez

Sedrakyan

Colpaert

et al. 2018

Lecture Notes in Computer Science

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Abstract. The road to publishing public streaming data on the Web is paved with trade-offs that determine its viability. The cost of unrestricted query answering on top of data streams, may not be affordable for all data publishers. Therefore, public streams need to be funded in a sustainable fashion to remain online. In this paper we present an overview of possible query answering features for live time series in the form of multidimensional interfaces. For example, from a live parking availability data stream, pre-calculated time constrained statistical indicators or geographically classified data can be provided to clients on demand. Furthermore, we demonstrate the initial developments of a Linked Time Series server that supports such features through an extensible modular architecture. Benchmarking the costs associated to each of these features allows to weigh the trade-offs inherent to publishing live time series and establishes the foundations to create a decentralized and sustainable ecosystem for live data streams on the Web.

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“…The term Stream Reasoning emerged a decade ago [5], and developed as a trend involving different areas of computer science, such as semantic web, databases, robotics and knowledge representation. The multidisciplinary nature of Stream Reasoning offers an ideal setting for exchanging ideas and expertise.…”

mentioning

confidence: 99%