Plan-Based Semantic Enrichment of Event Streams

Teymourian, Kia; Paschke, Adrian

doi:10.1007/978-3-319-07443-6_3

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…Schultz-Møller et al [183] introduce the Next CEP system, which includes a cost model to re-write and optimize the CEP query. Teymourian et al [184] present a semantic enrichment model to select the optimal processing plan based on the cost of the event and background knowledge acquisition. There are several other CEP cost models proposed to select the optimal execution plan based on CPU and communication cost [168,[185][186][187].…”

Section: Cep Cost Modelsmentioning

confidence: 99%

A situation refinement model for complex event processing

Alakari

Gebali

2020

Knowledge-Based Systems

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Section: Cep Cost Modelsmentioning

confidence: 99%

A situation refinement model for complex event processing

Alakari

Gebali

2020

Knowledge-Based Systems

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“…Ren and Pan [39], STARQL [35], and Cayuga [39]; and (3) rule-based formalisms e.g., ETALIS [2], Reactive ASP [26], Teymourian et al [44], LARS [8], metric Datalog [12], and Streamlog [46].…”

Section: Stream Processing and Stream Reasoningmentioning

confidence: 99%

Towards a Semantically Enriched Local Dynamic Map

et al. 2018

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With the increasing availability of Cooperative Intelligent Transport Systems, the Local Dynamic Map (LDM) is becoming a key technology for integrating static, temporary, and dynamic information in a geographical context. However, existing ideas do not leverage the full potential of the LDM approach, as an LDM contains streaming data and varying implicit information which are not captured by current models. We aim to provide a semantically enriched LDM that applies Semantic Web technologies, in particular ontologies, in combination with spatial stream databases. This allows us to define an enhanced world model, to derive model properties, to infer new information, and to offer expressive query capabilities over streams. We introduce our envisioned architecture which includes an LDM ontology, an integration and annotation framework, and a stream query answering component. We also sketch three application scenarios that illustrate the usability and benefits of our approach, thus we provide an in-depth validation of the scenarios in an experimental prototype.

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“…The studies related to plan-based approaches [18][19][20] share a common goal of obtaining a lower latency for detecting data while guaranteeing a reduced cost for transmitting data. Akdere et al [18] find multi-step-based data processing to leverage temporal relationships among events and generate a candidate plan with a cost-latency model.…”

Section: Related Workmentioning

confidence: 99%

“…Gao et al [19] propose a semantic service composition model and complex composition plans with event patterns to guarantee the lowest network traffic demand. Teymourian and Paschke [20] present event detection with semantic enrichment that integrates external knowledge and meets user-specified latency expectations. However, in distributed federated sources, these researches produce a plan, which quickly becomes less optimised due to various changing rates in dataset updates.…”

Section: Related Workmentioning

confidence: 99%

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An adaptive plan-based approach to integrating semantic streams with remote RDF data

Chun

Jung

Seo

et al. 2016

Journal of Information Science

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To satisfy a user’s complex requirements, Resource Description Framework (RDF) Stream Processing (RSP) systems envision the fusion of remote RDF data with semantic streams, using common data models to query semantic streams continuously. While streaming data are changing at a high rate and are pushed into RSP systems, the remote RDF data are retrieved from different remote sources. With the growth of SPARQL endpoints that provide access to remote RDF data, RSP systems can easily integrate the remote data with streams. Such integration provides new opportunities for mixing static (or quasi-static) data with streams on a large scale. However, the current RSP systems do not offer any optimisation for the integration. In this article, we present an adaptive plan-based approach to efficiently integrate sematic streams with the static data from a remote source. We create a query execution plan based on temporal constraints among constituent services for the timely acquisition of remote data. To predict the change of remote sources in real time, we propose an adaptive process of detecting a source update, forecasting the update in the future, deciding a new plan to obtain remote data and reacting to a new plan. We extend a SPARQL query with operators for describing the multiple strategies of the proposed adaptive process. Experimental results show that our approach is more efficient than the conventional RSP systems in distributed settings.

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Plan-Based Semantic Enrichment of Event Streams

Cited by 7 publications

References 7 publications

A situation refinement model for complex event processing

A situation refinement model for complex event processing

Towards a Semantically Enriched Local Dynamic Map

An adaptive plan-based approach to integrating semantic streams with remote RDF data

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