Integration of sensor data by means of an event abstraction layer

Diese Doktorarbeit stellt eine Methodik vor, die aus Zeitreihen von Sensorbeobachtungen Events (Ereignisse) nahezu in Echtzeit ableiten und darstellen kann. Die Analyse der Sensor-Daten erfolgt unter zur Hilfenahme von Prozessen und Technologien des 'Semantic event processing'. Aus den Daten abgeleitete Ereignisse werden eindeutig und maschinenlesbar als Instanzen bestehender Wissensbasen (Ontologien) dargestellt. Der Einsatz einer erweiterten Form der 'Semantic Sensor Network' Ontologie ermöglicht in diesem Zusammenhang eine Modellierung von spezifischem Fachwissen in einer mehrstufigen Ontologie-Struktur. Infolgedessen können differenzierte Perspektiven verschiedener Fachgemeinschaften auf die gleichen Daten integriert und verglichen werden.This thesis presents a methodology to infer and represent events from time series of sensor observations in near real-time. Semantic event processing is used to analyse sensor data. Inferred events are modelled using an extension of the Semantic Sensor Network ontology. Domain knowledge is represented in a multilevel ontology structure. The proposed methodology allows information communities to integrate different views on the same data

Towards efficient processing of RDF Data Streams

In the last years, there has been an increase in the amount of real-time data generated. Sensors attached to things are transforming how we interact with our environment. Extracting meaningful information from these streams of data is essential for some application areas and requires processing systems that scale to varying conditions in data sources, complex queries, and system failures. This paper describes ongoing research on the development of a scalable RDF streaming engine

On the road to the evaluation of RDF stream compression techniques

Proceedings of RDF Stream Processing Workshop in conjunction with the 12th Extended Semantic Web Conference (ESWC 2015), May 31st, 2015 in Portoroz, SloveniaThe popularization of data streaming applications, such as those related to social networks and the Internet of Things, has fostered the interest of the Semantic Web community for this kind of data. As a result of this interest, the W3C RDF Stream Processing (RSP) community group has recently been started with the goal of defining a common model “for producing, transmitting and continuously querying RDF Streams”. In this EOI we focus on the transmission model. As pointed out by recent research efforts (e.g. Ztreamy and CQELS Cloud), the efficient transmission of RDF streams is a necessary step to ensure higher throughput in RDF stream processors.This work is partially funded by Ministerio de Economía y Competitividad (Spain) under the projects “HERMES-SMARTDRIVER” (TIN2013-46801-C4-2-R) and “4V: Volumen, Velocidad, Variedad y Validez en la Gestión Innovadora de Datos” (TIN2013-46238-C4-2-R), and Austrian Science Fund (FWF): M1720-G1

An event abstraction layer for the integration of geosensor data

Time series of observations reflect the status of environmental properties. Variations in these properties can be considered as events when they potentially affect the stability of the monitored environment. Organisations dedicated to analyse environmental change use institutionalised descriptions of events to define the observable conditions under which events happen. This also applies to the methods used to classify and model changes in environmental monitoring. The heterogeneity of representations often causes interoperability problems when such communities exchange geospatial information. To enhance interoperability among diverse communities, it is required to develop models that do not restrict the representation of events, but allow integrating different perspectives on changes in the environment. The goal of the Event Abstraction Layer is to facilitate the analysis and integration of geosensor data by inferring events from time series of observations. For the analysis of geosensor data, we use event processing to detect event patterns in time series of observations. Spatio-temporal properties of the event are inferred from the geosensor location and the observation timestamps. For the data integration, we represent event-related information extracted from multiples sources under a common event model. Additionally, domain knowledge is modelled in a multilevel ontology structure. © 2014 Taylor & Francis